Group8_GeneExpressionElectrophysiologyAlzheimers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Differential Gene Expression in Brain Regions and Cell Types\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Team Member Names & Contributions\n",
    "\n",
    "- **Arthur Kim (A14545727)**: Background, Write code to wrangle dataset #2, Write code to visualize findings, Write markdown around code processes, Edit grammar, text and code.\n",
    "\n",
    "- **Stacey Pham (A14161000)**: Write code to wrangle dataset #1, Write code to visualize findings, Write markdown around code processes, Write discussion section, Edit grammar, text and code.\n",
    "\n",
    "- **Martin Shen (A13821781)**: Write code to analyze/integrate dataset #1, Write code to analyze/integrate dataset #2, Write code to visualize findings, Write markdown around code processes, Edit grammar, text and code.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abstract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alzheimer's Disease affects over 3 million people in the United States and is most common for people who are older than the age of 60. Four of key genes that are associated with AD is APP, PSEN1, PSEN2, and APOE. In this project, we plan to see the differential level of gene expression in cell types and brain regions across different age groups. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How are the expression levels of the genes associated with Alzheimer disease related to brain regions, cell types, and age?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alzheimer's disease (AD) is a neurodegenerative disease that progressively\n",
    "worsens over a period of time. By exploring the effects of\n",
    "differential gene expression in both cell types and brain regions, it would help provide a greater\n",
    "understanding of how certain gene expressions play a role in the progression of\n",
    "Alzheimer's and how it might be linked to cognitive decline in Alzheimer’s patients. It\n",
    "would help us gain insights and conclude which brain regions and cell types show differential expression of genes in Alzheimer’s Disease. \n",
    "\n",
    "Studies have found that there are several genes associated with the development of Alzheimer’s disease. The early-onset of AD primarily involves mutations in three genes —  amyloid-β precursor protein (APP), presenilin 1 (PSEN1), and presenilin 2 (PSEN2) — while the apolipoprotein E (APOE) gene is considered a risk factor for late-onset AD (Parasuraman et al. 2002). The APOE gene, which is responsible for coding a protein that helps regulate and transport cholesterol through the bloodstream, can be inherited as any of three major types in the form of e2, e3, or e4 alleles (Parasuraman et al. 2002). Among these types, the e4 allele of the APOE gene is the greatest factor associated with increasing the chance of receiving late-onset AD, but while it is a risk gene, it is not a definitive biomarker of the disease. Also, there have been studies done with magnetic resonance imaging that shows that Alzheimer's patients have a significantly smaller hippocampus relative to that of a healthy person (Convit et al., 1997; Kesslak, Nacclioglu, & Cotman, 1991; Killiany, Moss, Albert, & Tamas, 1993). Furthermore, studies conducted with twins who have the APOE genotype reveals that those who have the e4 allele (n=6) have structurally smaller hippocampus than those who do not have the e4 allele (n=14). This conclusion suggests that there is some level of differential expression of the APOE-e4 gene that affects the structure, function, and volume of the hippocampus. Thus, it gives us some insight that some brain regions and cell types may indeed have differential gene expression as a result of Alzheimer's Disease. \n",
    "\n",
    "Previous studies have also discovered that the APOE-e4 gene affects certain cell types in the brain. It has been suggested that the varying expression of the APOE-e4 gene in neural and glial cells can lead to severe AD (Lin et. al, 2018). As a consequence of the varied gene expression of APOE-e4, microglial cells activate inflammatory genes and have less efficient clearing of Aβ42 in AD brains. Additional research has found that the four major types of cells that are typically affected and could be related to Alzheimer’s disease are through the pyramidal neuron, astrocyte, oligodendrocyte and microglia cell types (Miller et. al, 2013). Other studies have shown that glial cells are involved in glutamatergic transmission and that GABAergic cell content rises as age increases, so this may also indicate a relationship involving expression of glutamatergic and GABAergic cell types with AD-associated genes (Martínez-Lozada, et. al, 2015; Olga, et. al, 2019). We would like to investigate the differential gene expression levels of the APP, PSEN1, PSEN2, and APOE genes across the major cell types and brain regions in Alzheimer’s Disease. \n",
    "\n",
    "The first dataset that we used is the Allen Brain Atlas microarray data to see how APP, PSEN1, PSEN2, and APOE gene expressions varied in different brain regions. There were six different donors, varying from age, gender, and ethnicity, that contributed to the microarray data collection. The dataset recorded 27 unique top level brain regions analyzed and within those 27, there were 108 unique specific brain structures that the expression of genes were measured for. In total, there were 1014 observations of expression for each gene. The dataset for each gene provided information about their expression values, the metadata for each column in the expression data, and the metadata for the probes referenced. The expression data contained the expression values with Z-score normalization for each gene probe. The column metadata supplied the donor’s name, age, and ID, the expression MRI coordinates, the brain structure’s name, ID, and color of expression. The probe metadata contained information about the gene probe’s name and ID, the gene’s name, symbol, and ID, and lastly the chromosome number and position.\n",
    "\n",
    "The second dataset that we will be using is the RNA Sequencing of Cell Types in order to show  differential expression of APP, PSEN1, PSEN2 and APOE genes in different cell types. The nuclei that were used for sampling were from postmortem and neurosurgical (MTG only) donors. The dataset has 76,533 total nuclei to see the diversity of cell types in the primary motor cortex (M1). Also, in this dataset, there were 127 transcriptomic cell types that they were able to identify. In this dataset, we chose to look at the metadata and trimmed means. Within the metadata, it includes information about the cell types and their order, label, and color column. The trimmed means includes the trimmed means of gene expression per cell type. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1)Convit A, De Leon MJ, Tarshish C, De Santi S, Tsui W, Rusinek H, George A. Specific hippocampal volume reductions in individuals at risk for Alzheimer's disease\n",
    "Neurobiol Aging. 1997 Mar-Apr; 18(2):131-8.\n",
    "\n",
    "Link: https://pubmed.ncbi.nlm.nih.gov/9258889/\n",
    "\n",
    "(2)Garaschuk, Olga, and Alexei Verkhratsky. “GABAergic astrocytes in Alzheimer's disease.” Aging \n",
    "vol. 11,6 (2019): 1602-1604. doi:10.18632/aging.101870\n",
    "\n",
    "Link:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6461167/\n",
    "\n",
    "\n",
    "(3)Lin, Yuan-Ta, et al. APOE4 Causes Widespread Molecular and Cellular Alterations \n",
    "Associated with Alzheimer’s Disease Phenotypes in Human IPSC-Derived Brain Cell Types. Neuron, vol. 98, no. 6, 27 June 2018, pp. 1141–1154., doi:10.1016/j.neuron.2018.06.011.\n",
    "\n",
    "Link: https://www.sciencedirect.com/science/article/pii/S0896627318303805\n",
    "\n",
    "(4)Martínez-Lozada, Zila, and Arturo Ortega. “Glutamatergic Transmission: A Matter of Three.” \n",
    "Neural plasticity vol. 2015 (2015): 787396. doi:10.1155/2015/787396\n",
    "\n",
    "Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4539489/\n",
    "\n",
    "(5)Miller, J.A., Woltjer, R.L., Goodenbour, J.M. et al. Genes and pathways underlying \n",
    "regional and cell type changes in Alzheimer's disease. Genome Med 5, 48 (2013). https://doi.org/10.1186/gm452\n",
    "\n",
    "Link: https://link.springer.com/article/10.1186/gm452\n",
    "\n",
    "(6)Parasuraman, Raja et al. The apolipoprotein E gene, attention, and brain function.\n",
    "Neuropsychology vol. 16,2 (2002): 254-74. doi:10.1037//0894-4105.16.2.254\n",
    "\n",
    "Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1350934/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We expect the hippocampus area to have lower gene expression due to the reduction of volume that occurs in AD patients. In addition, we would expect to see higher levels of expression of glial-related cells. \n",
    "We predict that there could be some correlation between the expression level of genes in the brain regions by age that are most affected in Alzheimer disease because it is well documented that age is an important factor in the risk and development of Alzheimer disease. (p=0.05.) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "import these packages for code to work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from csv import reader\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling: Allen Brain Atlas Microarray Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gene Expression in Brain Regions Dataset\n",
    "This notebook will wrangle data for the first data set. Using the Allen Microarray database (https://human.brain-map.org/microarray/search/ ), we looked at four genes: APP, PSEN1, PSEN2, and APOE.\n",
    "\n",
    "### Table of Contents\n",
    "1. [APP Gene](#one)\n",
    "2. [PSEN1 Gene](#two)\n",
    "3. [PSEN2 Gene](#three)\n",
    "4. [APOE Gene](#four)\n",
    "5. [Plotting Graphs](#five)\n",
    "    1. [Heatmap](#five.2)\n",
    "    2. [Boxplot](#five.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"one\"></a>\n",
    "\n",
    "## APP Gene\n",
    "\n",
    "The data for each gene came with three CSV files.\n",
    "As the first step, imported the necessary packages so that we could read each CSV file as a pandas DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for APP's Columns.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>donor_id</th>\n",
       "      <th>donor_name</th>\n",
       "      <th>donor_age</th>\n",
       "      <th>donor_color</th>\n",
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       "      <th>top_level_structure_abbreviation</th>\n",
       "      <th>top_level_structure_color</th>\n",
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       "      <th>0</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
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       "      <td>154</td>\n",
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       "      <td>96</td>\n",
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       "      <td>4053</td>\n",
       "      <td>anterior orbital gyrus</td>\n",
       "      <td>AOrG</td>\n",
       "      <td>E8BF59</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>859</td>\n",
       "      <td>39232</td>\n",
       "      <td>129</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>4078</td>\n",
       "      <td>frontal operculum</td>\n",
       "      <td>fro</td>\n",
       "      <td>E8C159</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
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       "      <th>2</th>\n",
       "      <td>9861</td>\n",
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       "      <td>EC891D</td>\n",
       "      <td>97</td>\n",
       "      <td>28028</td>\n",
       "      <td>85</td>\n",
       "      <td>96</td>\n",
       "      <td>19</td>\n",
       "      <td>4888</td>\n",
       "      <td>frontal pole</td>\n",
       "      <td>FP</td>\n",
       "      <td>E8C359</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>123</td>\n",
       "      <td>27165</td>\n",
       "      <td>94</td>\n",
       "      <td>102</td>\n",
       "      <td>26</td>\n",
       "      <td>4047</td>\n",
       "      <td>gyrus rectus</td>\n",
       "      <td>GRe</td>\n",
       "      <td>E8C459</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>539</td>\n",
       "      <td>36756</td>\n",
       "      <td>145</td>\n",
       "      <td>75</td>\n",
       "      <td>66</td>\n",
       "      <td>4041</td>\n",
       "      <td>inferior frontal gyrus, opercular part</td>\n",
       "      <td>opIFG</td>\n",
       "      <td>E8C659</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160535259</td>\n",
       "      <td>156687325</td>\n",
       "      <td>80</td>\n",
       "      <td>144</td>\n",
       "      <td>131</td>\n",
       "      <td>9676</td>\n",
       "      <td>spinal trigeminal nucleus</td>\n",
       "      <td>Sp5</td>\n",
       "      <td>377FE4</td>\n",
       "      <td>9512</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>MY</td>\n",
       "      <td>1F67CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160535275</td>\n",
       "      <td>159233515</td>\n",
       "      <td>82</td>\n",
       "      <td>144</td>\n",
       "      <td>134</td>\n",
       "      <td>9697</td>\n",
       "      <td>vestibular nuclei</td>\n",
       "      <td>8Ve</td>\n",
       "      <td>3D85EA</td>\n",
       "      <td>9512</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>MY</td>\n",
       "      <td>1F67CC</td>\n",
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       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>159226181</td>\n",
       "      <td>156950588</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
       "      <td>117</td>\n",
       "      <td>9222</td>\n",
       "      <td>corpus callosum</td>\n",
       "      <td>cc</td>\n",
       "      <td>F2F1F0</td>\n",
       "      <td>9218</td>\n",
       "      <td>white matter</td>\n",
       "      <td>WM</td>\n",
       "      <td>F2F1F0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>9240</td>\n",
       "      <td>cingulum bundle</td>\n",
       "      <td>cgb</td>\n",
       "      <td>F2F1F0</td>\n",
       "      <td>9218</td>\n",
       "      <td>white matter</td>\n",
       "      <td>WM</td>\n",
       "      <td>F2F1F0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160091917</td>\n",
       "      <td>159215934</td>\n",
       "      <td>111</td>\n",
       "      <td>125</td>\n",
       "      <td>107</td>\n",
       "      <td>9708</td>\n",
       "      <td>choroid plexus of the lateral ventricle</td>\n",
       "      <td>CPLV</td>\n",
       "      <td>872601</td>\n",
       "      <td>9352</td>\n",
       "      <td>sulci &amp; spaces</td>\n",
       "      <td>SS</td>\n",
       "      <td>6D6E70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 17 columns</p>\n",
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      ],
      "text/plain": [
       "      donor_id  donor_name donor_age donor_color  sample_well  sample_polygon  \\\n",
       "0         9861  H0351.2001  24 years      EC891D          154           27255   \n",
       "1         9861  H0351.2001  24 years      EC891D          859           39232   \n",
       "2         9861  H0351.2001  24 years      EC891D           97           28028   \n",
       "3         9861  H0351.2001  24 years      EC891D          123           27165   \n",
       "4         9861  H0351.2001  24 years      EC891D          539           36756   \n",
       "...        ...         ...       ...         ...          ...             ...   \n",
       "1009     15697  H0351.1016  55 years      EB3B00    160535259       156687325   \n",
       "1010     15697  H0351.1016  55 years      EB3B00    160535275       159233515   \n",
       "1011     15697  H0351.1016  55 years      EB3B00    159226181       156950588   \n",
       "1012     15697  H0351.1016  55 years      EB3B00           -1              -1   \n",
       "1013     15697  H0351.1016  55 years      EB3B00    160091917       159215934   \n",
       "\n",
       "      sample_mri_0  sample_mri_1  sample_mri_2  structure_id  \\\n",
       "0               74            96            29          4053   \n",
       "1              129            78            81          4078   \n",
       "2               85            96            19          4888   \n",
       "3               94           102            26          4047   \n",
       "4              145            75            66          4041   \n",
       "...            ...           ...           ...           ...   \n",
       "1009            80           144           131          9676   \n",
       "1010            82           144           134          9697   \n",
       "1011            97            84           117          9222   \n",
       "1012            -1            -1            -1          9240   \n",
       "1013           111           125           107          9708   \n",
       "\n",
       "                               structure_name structure_abbreviation  \\\n",
       "0                      anterior orbital gyrus                   AOrG   \n",
       "1                           frontal operculum                    fro   \n",
       "2                                frontal pole                     FP   \n",
       "3                                gyrus rectus                    GRe   \n",
       "4      inferior frontal gyrus, opercular part                  opIFG   \n",
       "...                                       ...                    ...   \n",
       "1009                spinal trigeminal nucleus                    Sp5   \n",
       "1010                        vestibular nuclei                    8Ve   \n",
       "1011                          corpus callosum                     cc   \n",
       "1012                          cingulum bundle                    cgb   \n",
       "1013  choroid plexus of the lateral ventricle                   CPLV   \n",
       "\n",
       "     structure_color  top_level_structure_id top_level_structure_name  \\\n",
       "0             E8BF59                    4009             frontal lobe   \n",
       "1             E8C159                    4009             frontal lobe   \n",
       "2             E8C359                    4009             frontal lobe   \n",
       "3             E8C459                    4009             frontal lobe   \n",
       "4             E8C659                    4009             frontal lobe   \n",
       "...              ...                     ...                      ...   \n",
       "1009          377FE4                    9512           myelencephalon   \n",
       "1010          3D85EA                    9512           myelencephalon   \n",
       "1011          F2F1F0                    9218             white matter   \n",
       "1012          F2F1F0                    9218             white matter   \n",
       "1013          872601                    9352           sulci & spaces   \n",
       "\n",
       "     top_level_structure_abbreviation top_level_structure_color  \n",
       "0                                  FL                    E8CD59  \n",
       "1                                  FL                    E8CD59  \n",
       "2                                  FL                    E8CD59  \n",
       "3                                  FL                    E8CD59  \n",
       "4                                  FL                    E8CD59  \n",
       "...                               ...                       ...  \n",
       "1009                               MY                    1F67CC  \n",
       "1010                               MY                    1F67CC  \n",
       "1011                               WM                    F2F1F0  \n",
       "1012                               WM                    F2F1F0  \n",
       "1013                               SS                    6D6E70  \n",
       "\n",
       "[1014 rows x 17 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read APP's columns csv file as dataframe\n",
    "APP_columns_df = pd.read_csv('APP_Columns.csv')\n",
    "APP_columns_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for APP's Probes.csv. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>gene-id</th>\n",
       "      <th>gene-symbol</th>\n",
       "      <th>gene-name</th>\n",
       "      <th>entrez-id</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>start-position</th>\n",
       "      <th>end-position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1029275</td>\n",
       "      <td>A_32_P167000</td>\n",
       "      <td>348</td>\n",
       "      <td>APP</td>\n",
       "      <td>amyloid beta (A4) precursor protein</td>\n",
       "      <td>351</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1029276</td>\n",
       "      <td>A_24_P314159</td>\n",
       "      <td>348</td>\n",
       "      <td>APP</td>\n",
       "      <td>amyloid beta (A4) precursor protein</td>\n",
       "      <td>351</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1029277</td>\n",
       "      <td>CUST_84_PI416408490</td>\n",
       "      <td>348</td>\n",
       "      <td>APP</td>\n",
       "      <td>amyloid beta (A4) precursor protein</td>\n",
       "      <td>351</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                 name  gene-id gene-symbol  \\\n",
       "0  1029275         A_32_P167000      348         APP   \n",
       "1  1029276         A_24_P314159      348         APP   \n",
       "2  1029277  CUST_84_PI416408490      348         APP   \n",
       "\n",
       "                             gene-name  entrez-id  chromosome  start-position  \\\n",
       "0  amyloid beta (A4) precursor protein        351          21             NaN   \n",
       "1  amyloid beta (A4) precursor protein        351          21             NaN   \n",
       "2  amyloid beta (A4) precursor protein        351          21             NaN   \n",
       "\n",
       "   end-position  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read APP's probe csv file as dataframe\n",
    "APP_probes_df = pd.read_csv('APP_Probes.csv')\n",
    "APP_probes_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for APP's Expression.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_32_P167000</th>\n",
       "      <th>A_24_P314159</th>\n",
       "      <th>CUST_84_PI416408490</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.3986</td>\n",
       "      <td>1.3230</td>\n",
       "      <td>0.7138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.6176</td>\n",
       "      <td>-0.0828</td>\n",
       "      <td>-0.2437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.2992</td>\n",
       "      <td>0.7687</td>\n",
       "      <td>0.4941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6595</td>\n",
       "      <td>0.5479</td>\n",
       "      <td>0.3077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.1978</td>\n",
       "      <td>-0.5632</td>\n",
       "      <td>-0.4256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>-0.3345</td>\n",
       "      <td>1.1739</td>\n",
       "      <td>1.2782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>0.6085</td>\n",
       "      <td>1.2909</td>\n",
       "      <td>1.6062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>0.2663</td>\n",
       "      <td>0.9404</td>\n",
       "      <td>0.7080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>-2.5205</td>\n",
       "      <td>0.7128</td>\n",
       "      <td>0.3189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A_32_P167000  A_24_P314159  CUST_84_PI416408490\n",
       "0          -1.3986        1.3230               0.7138\n",
       "1           1.6176       -0.0828              -0.2437\n",
       "2          -2.2992        0.7687               0.4941\n",
       "3           0.6595        0.5479               0.3077\n",
       "4           1.1978       -0.5632              -0.4256\n",
       "...            ...           ...                  ...\n",
       "1009       -0.3345        1.1739               1.2782\n",
       "1010        0.6085        1.2909               1.6062\n",
       "1011        0.2663        0.9404               0.7080\n",
       "1012           NaN           NaN                  NaN\n",
       "1013       -2.5205        0.7128               0.3189\n",
       "\n",
       "[1014 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read APP's expression csv file as dataframe\n",
    "APP_exp_df = pd.read_csv('APP_Expression.csv', header=None)\n",
    "# Set the index to the 0 column which contains the probe ID\n",
    "APP_exp_df = APP_exp_df.set_index(0)\n",
    "# Transpose the dataframe\n",
    "APP_exp_df = APP_exp_df.transpose()\n",
    "# Reset the index\n",
    "APP_exp_df = APP_exp_df.reset_index(drop = True)\n",
    "APP_exp_df.columns = list(APP_probes_df['name'])\n",
    "APP_exp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a new df about the gene expression by age and brain region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_32_P167000</th>\n",
       "      <th>A_24_P314159</th>\n",
       "      <th>CUST_84_PI416408490</th>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th>donor_name</th>\n",
       "      <th>donor_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.3986</td>\n",
       "      <td>1.3230</td>\n",
       "      <td>0.7138</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.6176</td>\n",
       "      <td>-0.0828</td>\n",
       "      <td>-0.2437</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.2992</td>\n",
       "      <td>0.7687</td>\n",
       "      <td>0.4941</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6595</td>\n",
       "      <td>0.5479</td>\n",
       "      <td>0.3077</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.1978</td>\n",
       "      <td>-0.5632</td>\n",
       "      <td>-0.4256</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>-0.3345</td>\n",
       "      <td>1.1739</td>\n",
       "      <td>1.2782</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>0.6085</td>\n",
       "      <td>1.2909</td>\n",
       "      <td>1.6062</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>0.2663</td>\n",
       "      <td>0.9404</td>\n",
       "      <td>0.7080</td>\n",
       "      <td>white matter</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>white matter</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>-2.5205</td>\n",
       "      <td>0.7128</td>\n",
       "      <td>0.3189</td>\n",
       "      <td>sulci &amp; spaces</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A_32_P167000  A_24_P314159  CUST_84_PI416408490  \\\n",
       "0          -1.3986        1.3230               0.7138   \n",
       "1           1.6176       -0.0828              -0.2437   \n",
       "2          -2.2992        0.7687               0.4941   \n",
       "3           0.6595        0.5479               0.3077   \n",
       "4           1.1978       -0.5632              -0.4256   \n",
       "...            ...           ...                  ...   \n",
       "1009       -0.3345        1.1739               1.2782   \n",
       "1010        0.6085        1.2909               1.6062   \n",
       "1011        0.2663        0.9404               0.7080   \n",
       "1012           NaN           NaN                  NaN   \n",
       "1013       -2.5205        0.7128               0.3189   \n",
       "\n",
       "     top_level_structure_name  donor_name donor_age  \n",
       "0                frontal lobe  H0351.2001  24 years  \n",
       "1                frontal lobe  H0351.2001  24 years  \n",
       "2                frontal lobe  H0351.2001  24 years  \n",
       "3                frontal lobe  H0351.2001  24 years  \n",
       "4                frontal lobe  H0351.2001  24 years  \n",
       "...                       ...         ...       ...  \n",
       "1009           myelencephalon  H0351.1016  55 years  \n",
       "1010           myelencephalon  H0351.1016  55 years  \n",
       "1011             white matter  H0351.1016  55 years  \n",
       "1012             white matter  H0351.1016  55 years  \n",
       "1013           sulci & spaces  H0351.1016  55 years  \n",
       "\n",
       "[1014 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "APP_age_region_df = APP_exp_df.join(APP_columns_df['top_level_structure_name']).join(APP_columns_df['donor_name']).join(APP_columns_df['donor_age'])\n",
    "APP_age_region_df                                    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"two\"></a>\n",
    "\n",
    "## PSEN1 Gene\n",
    "\n",
    "We repeat the steps of turning the CSV files of PSEN1 into DataFrames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for PSEN1's Columns.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>donor_id</th>\n",
       "      <th>donor_name</th>\n",
       "      <th>donor_age</th>\n",
       "      <th>donor_color</th>\n",
       "      <th>sample_well</th>\n",
       "      <th>sample_polygon</th>\n",
       "      <th>sample_mri_0</th>\n",
       "      <th>sample_mri_1</th>\n",
       "      <th>sample_mri_2</th>\n",
       "      <th>structure_id</th>\n",
       "      <th>structure_name</th>\n",
       "      <th>structure_abbreviation</th>\n",
       "      <th>structure_color</th>\n",
       "      <th>top_level_structure_id</th>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th>top_level_structure_abbreviation</th>\n",
       "      <th>top_level_structure_color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>154</td>\n",
       "      <td>27255</td>\n",
       "      <td>74</td>\n",
       "      <td>96</td>\n",
       "      <td>29</td>\n",
       "      <td>4053</td>\n",
       "      <td>anterior orbital gyrus</td>\n",
       "      <td>AOrG</td>\n",
       "      <td>E8BF59</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>859</td>\n",
       "      <td>39232</td>\n",
       "      <td>129</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>4078</td>\n",
       "      <td>frontal operculum</td>\n",
       "      <td>fro</td>\n",
       "      <td>E8C159</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>97</td>\n",
       "      <td>28028</td>\n",
       "      <td>85</td>\n",
       "      <td>96</td>\n",
       "      <td>19</td>\n",
       "      <td>4888</td>\n",
       "      <td>frontal pole</td>\n",
       "      <td>FP</td>\n",
       "      <td>E8C359</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>123</td>\n",
       "      <td>27165</td>\n",
       "      <td>94</td>\n",
       "      <td>102</td>\n",
       "      <td>26</td>\n",
       "      <td>4047</td>\n",
       "      <td>gyrus rectus</td>\n",
       "      <td>GRe</td>\n",
       "      <td>E8C459</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>539</td>\n",
       "      <td>36756</td>\n",
       "      <td>145</td>\n",
       "      <td>75</td>\n",
       "      <td>66</td>\n",
       "      <td>4041</td>\n",
       "      <td>inferior frontal gyrus, opercular part</td>\n",
       "      <td>opIFG</td>\n",
       "      <td>E8C659</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160535259</td>\n",
       "      <td>156687325</td>\n",
       "      <td>80</td>\n",
       "      <td>144</td>\n",
       "      <td>131</td>\n",
       "      <td>9676</td>\n",
       "      <td>spinal trigeminal nucleus</td>\n",
       "      <td>Sp5</td>\n",
       "      <td>377FE4</td>\n",
       "      <td>9512</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>MY</td>\n",
       "      <td>1F67CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160535275</td>\n",
       "      <td>159233515</td>\n",
       "      <td>82</td>\n",
       "      <td>144</td>\n",
       "      <td>134</td>\n",
       "      <td>9697</td>\n",
       "      <td>vestibular nuclei</td>\n",
       "      <td>8Ve</td>\n",
       "      <td>3D85EA</td>\n",
       "      <td>9512</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>MY</td>\n",
       "      <td>1F67CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>159226181</td>\n",
       "      <td>156950588</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
       "      <td>117</td>\n",
       "      <td>9222</td>\n",
       "      <td>corpus callosum</td>\n",
       "      <td>cc</td>\n",
       "      <td>F2F1F0</td>\n",
       "      <td>9218</td>\n",
       "      <td>white matter</td>\n",
       "      <td>WM</td>\n",
       "      <td>F2F1F0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>9240</td>\n",
       "      <td>cingulum bundle</td>\n",
       "      <td>cgb</td>\n",
       "      <td>F2F1F0</td>\n",
       "      <td>9218</td>\n",
       "      <td>white matter</td>\n",
       "      <td>WM</td>\n",
       "      <td>F2F1F0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160091917</td>\n",
       "      <td>159215934</td>\n",
       "      <td>111</td>\n",
       "      <td>125</td>\n",
       "      <td>107</td>\n",
       "      <td>9708</td>\n",
       "      <td>choroid plexus of the lateral ventricle</td>\n",
       "      <td>CPLV</td>\n",
       "      <td>872601</td>\n",
       "      <td>9352</td>\n",
       "      <td>sulci &amp; spaces</td>\n",
       "      <td>SS</td>\n",
       "      <td>6D6E70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      donor_id  donor_name donor_age donor_color  sample_well  sample_polygon  \\\n",
       "0         9861  H0351.2001  24 years      EC891D          154           27255   \n",
       "1         9861  H0351.2001  24 years      EC891D          859           39232   \n",
       "2         9861  H0351.2001  24 years      EC891D           97           28028   \n",
       "3         9861  H0351.2001  24 years      EC891D          123           27165   \n",
       "4         9861  H0351.2001  24 years      EC891D          539           36756   \n",
       "...        ...         ...       ...         ...          ...             ...   \n",
       "1009     15697  H0351.1016  55 years      EB3B00    160535259       156687325   \n",
       "1010     15697  H0351.1016  55 years      EB3B00    160535275       159233515   \n",
       "1011     15697  H0351.1016  55 years      EB3B00    159226181       156950588   \n",
       "1012     15697  H0351.1016  55 years      EB3B00           -1              -1   \n",
       "1013     15697  H0351.1016  55 years      EB3B00    160091917       159215934   \n",
       "\n",
       "      sample_mri_0  sample_mri_1  sample_mri_2  structure_id  \\\n",
       "0               74            96            29          4053   \n",
       "1              129            78            81          4078   \n",
       "2               85            96            19          4888   \n",
       "3               94           102            26          4047   \n",
       "4              145            75            66          4041   \n",
       "...            ...           ...           ...           ...   \n",
       "1009            80           144           131          9676   \n",
       "1010            82           144           134          9697   \n",
       "1011            97            84           117          9222   \n",
       "1012            -1            -1            -1          9240   \n",
       "1013           111           125           107          9708   \n",
       "\n",
       "                               structure_name structure_abbreviation  \\\n",
       "0                      anterior orbital gyrus                   AOrG   \n",
       "1                           frontal operculum                    fro   \n",
       "2                                frontal pole                     FP   \n",
       "3                                gyrus rectus                    GRe   \n",
       "4      inferior frontal gyrus, opercular part                  opIFG   \n",
       "...                                       ...                    ...   \n",
       "1009                spinal trigeminal nucleus                    Sp5   \n",
       "1010                        vestibular nuclei                    8Ve   \n",
       "1011                          corpus callosum                     cc   \n",
       "1012                          cingulum bundle                    cgb   \n",
       "1013  choroid plexus of the lateral ventricle                   CPLV   \n",
       "\n",
       "     structure_color  top_level_structure_id top_level_structure_name  \\\n",
       "0             E8BF59                    4009             frontal lobe   \n",
       "1             E8C159                    4009             frontal lobe   \n",
       "2             E8C359                    4009             frontal lobe   \n",
       "3             E8C459                    4009             frontal lobe   \n",
       "4             E8C659                    4009             frontal lobe   \n",
       "...              ...                     ...                      ...   \n",
       "1009          377FE4                    9512           myelencephalon   \n",
       "1010          3D85EA                    9512           myelencephalon   \n",
       "1011          F2F1F0                    9218             white matter   \n",
       "1012          F2F1F0                    9218             white matter   \n",
       "1013          872601                    9352           sulci & spaces   \n",
       "\n",
       "     top_level_structure_abbreviation top_level_structure_color  \n",
       "0                                  FL                    E8CD59  \n",
       "1                                  FL                    E8CD59  \n",
       "2                                  FL                    E8CD59  \n",
       "3                                  FL                    E8CD59  \n",
       "4                                  FL                    E8CD59  \n",
       "...                               ...                       ...  \n",
       "1009                               MY                    1F67CC  \n",
       "1010                               MY                    1F67CC  \n",
       "1011                               WM                    F2F1F0  \n",
       "1012                               WM                    F2F1F0  \n",
       "1013                               SS                    6D6E70  \n",
       "\n",
       "[1014 rows x 17 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read PSEN1's columns csv file as dataframe\n",
    "PSEN1_columns_df = pd.read_csv('PSEN1_Columns.csv')\n",
    "PSEN1_columns_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for PSEN1's Probes.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>gene-id</th>\n",
       "      <th>gene-symbol</th>\n",
       "      <th>gene-name</th>\n",
       "      <th>entrez-id</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>start-position</th>\n",
       "      <th>end-position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1052589</td>\n",
       "      <td>A_23_P205686</td>\n",
       "      <td>5631</td>\n",
       "      <td>PSEN1</td>\n",
       "      <td>presenilin 1</td>\n",
       "      <td>5663</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1052590</td>\n",
       "      <td>A_23_P340728</td>\n",
       "      <td>5631</td>\n",
       "      <td>PSEN1</td>\n",
       "      <td>presenilin 1</td>\n",
       "      <td>5663</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id          name  gene-id gene-symbol     gene-name  entrez-id  \\\n",
       "0  1052589  A_23_P205686     5631       PSEN1  presenilin 1       5663   \n",
       "1  1052590  A_23_P340728     5631       PSEN1  presenilin 1       5663   \n",
       "\n",
       "   chromosome  start-position  end-position  \n",
       "0          14             NaN           NaN  \n",
       "1          14             NaN           NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read PSEN1's probe csv file as dataframe\n",
    "PSEN1_probes_df = pd.read_csv('PSEN1_Probes.csv')\n",
    "PSEN1_probes_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for PSEN1's Expression.csv. Again, we aligned the expression values to their correlated top level brain structures by setting the brain structures as the column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>A_23_P205686</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.2481</td>\n",
       "      <td>0.0476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.4923</td>\n",
       "      <td>-1.2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.7403</td>\n",
       "      <td>-0.7898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.4826</td>\n",
       "      <td>-0.8385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.4261</td>\n",
       "      <td>-1.0389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>0.2341</td>\n",
       "      <td>0.6752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>1.1637</td>\n",
       "      <td>1.5391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>2.8550</td>\n",
       "      <td>2.5623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>0.3855</td>\n",
       "      <td>0.2265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A_23_P205686  A_23_P340728\n",
       "0          -0.2481        0.0476\n",
       "1          -0.4923       -1.2025\n",
       "2          -0.7403       -0.7898\n",
       "3          -0.4826       -0.8385\n",
       "4          -0.4261       -1.0389\n",
       "...            ...           ...\n",
       "1009        0.2341        0.6752\n",
       "1010        1.1637        1.5391\n",
       "1011        2.8550        2.5623\n",
       "1012           NaN           NaN\n",
       "1013        0.3855        0.2265\n",
       "\n",
       "[1014 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read PSEN1's expression csv file as dataframe\n",
    "PSEN1_exp_df = pd.read_csv('PSEN1_Expression.csv', header=None)\n",
    "\n",
    "# Set the index to the 0 column which contains the probe ID\n",
    "PSEN1_exp_df = PSEN1_exp_df.set_index(0)\n",
    "# Transpose the dataframe\n",
    "PSEN1_exp_df = PSEN1_exp_df.transpose()\n",
    "# Reset the index\n",
    "PSEN1_exp_df = PSEN1_exp_df.reset_index(drop = True)\n",
    "PSEN1_exp_df.columns = list(PSEN1_probes_df['name'])\n",
    "PSEN1_exp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_23_P205686</th>\n",
       "      <th>A_23_P340728</th>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th>donor_name</th>\n",
       "      <th>donor_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.2481</td>\n",
       "      <td>0.0476</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.4923</td>\n",
       "      <td>-1.2025</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.7403</td>\n",
       "      <td>-0.7898</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.4826</td>\n",
       "      <td>-0.8385</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.4261</td>\n",
       "      <td>-1.0389</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>0.2341</td>\n",
       "      <td>0.6752</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>1.1637</td>\n",
       "      <td>1.5391</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>2.8550</td>\n",
       "      <td>2.5623</td>\n",
       "      <td>white matter</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>white matter</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>0.3855</td>\n",
       "      <td>0.2265</td>\n",
       "      <td>sulci &amp; spaces</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A_23_P205686  A_23_P340728 top_level_structure_name  donor_name  \\\n",
       "0          -0.2481        0.0476             frontal lobe  H0351.2001   \n",
       "1          -0.4923       -1.2025             frontal lobe  H0351.2001   \n",
       "2          -0.7403       -0.7898             frontal lobe  H0351.2001   \n",
       "3          -0.4826       -0.8385             frontal lobe  H0351.2001   \n",
       "4          -0.4261       -1.0389             frontal lobe  H0351.2001   \n",
       "...            ...           ...                      ...         ...   \n",
       "1009        0.2341        0.6752           myelencephalon  H0351.1016   \n",
       "1010        1.1637        1.5391           myelencephalon  H0351.1016   \n",
       "1011        2.8550        2.5623             white matter  H0351.1016   \n",
       "1012           NaN           NaN             white matter  H0351.1016   \n",
       "1013        0.3855        0.2265           sulci & spaces  H0351.1016   \n",
       "\n",
       "     donor_age  \n",
       "0     24 years  \n",
       "1     24 years  \n",
       "2     24 years  \n",
       "3     24 years  \n",
       "4     24 years  \n",
       "...        ...  \n",
       "1009  55 years  \n",
       "1010  55 years  \n",
       "1011  55 years  \n",
       "1012  55 years  \n",
       "1013  55 years  \n",
       "\n",
       "[1014 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PSEN1_age_region_df = PSEN1_exp_df.join(PSEN1_columns_df['top_level_structure_name']).join(PSEN1_columns_df['donor_name']).join(PSEN1_columns_df['donor_age'])\n",
    "PSEN1_age_region_df  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"three\"></a>\n",
    "\n",
    "## PSEN2 Gene\n",
    "\n",
    "We repeat the steps of turning the CSV files of PSEN2 into DataFrames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for PSEN2's Columns.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>donor_id</th>\n",
       "      <th>donor_name</th>\n",
       "      <th>donor_age</th>\n",
       "      <th>donor_color</th>\n",
       "      <th>sample_well</th>\n",
       "      <th>sample_polygon</th>\n",
       "      <th>sample_mri_0</th>\n",
       "      <th>sample_mri_1</th>\n",
       "      <th>sample_mri_2</th>\n",
       "      <th>structure_id</th>\n",
       "      <th>structure_name</th>\n",
       "      <th>structure_abbreviation</th>\n",
       "      <th>structure_color</th>\n",
       "      <th>top_level_structure_id</th>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th>top_level_structure_abbreviation</th>\n",
       "      <th>top_level_structure_color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>154</td>\n",
       "      <td>27255</td>\n",
       "      <td>74</td>\n",
       "      <td>96</td>\n",
       "      <td>29</td>\n",
       "      <td>4053</td>\n",
       "      <td>anterior orbital gyrus</td>\n",
       "      <td>AOrG</td>\n",
       "      <td>E8BF59</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>859</td>\n",
       "      <td>39232</td>\n",
       "      <td>129</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>4078</td>\n",
       "      <td>frontal operculum</td>\n",
       "      <td>fro</td>\n",
       "      <td>E8C159</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>97</td>\n",
       "      <td>28028</td>\n",
       "      <td>85</td>\n",
       "      <td>96</td>\n",
       "      <td>19</td>\n",
       "      <td>4888</td>\n",
       "      <td>frontal pole</td>\n",
       "      <td>FP</td>\n",
       "      <td>E8C359</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>123</td>\n",
       "      <td>27165</td>\n",
       "      <td>94</td>\n",
       "      <td>102</td>\n",
       "      <td>26</td>\n",
       "      <td>4047</td>\n",
       "      <td>gyrus rectus</td>\n",
       "      <td>GRe</td>\n",
       "      <td>E8C459</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>539</td>\n",
       "      <td>36756</td>\n",
       "      <td>145</td>\n",
       "      <td>75</td>\n",
       "      <td>66</td>\n",
       "      <td>4041</td>\n",
       "      <td>inferior frontal gyrus, opercular part</td>\n",
       "      <td>opIFG</td>\n",
       "      <td>E8C659</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160535259</td>\n",
       "      <td>156687325</td>\n",
       "      <td>80</td>\n",
       "      <td>144</td>\n",
       "      <td>131</td>\n",
       "      <td>9676</td>\n",
       "      <td>spinal trigeminal nucleus</td>\n",
       "      <td>Sp5</td>\n",
       "      <td>377FE4</td>\n",
       "      <td>9512</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>MY</td>\n",
       "      <td>1F67CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160535275</td>\n",
       "      <td>159233515</td>\n",
       "      <td>82</td>\n",
       "      <td>144</td>\n",
       "      <td>134</td>\n",
       "      <td>9697</td>\n",
       "      <td>vestibular nuclei</td>\n",
       "      <td>8Ve</td>\n",
       "      <td>3D85EA</td>\n",
       "      <td>9512</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>MY</td>\n",
       "      <td>1F67CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>159226181</td>\n",
       "      <td>156950588</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
       "      <td>117</td>\n",
       "      <td>9222</td>\n",
       "      <td>corpus callosum</td>\n",
       "      <td>cc</td>\n",
       "      <td>F2F1F0</td>\n",
       "      <td>9218</td>\n",
       "      <td>white matter</td>\n",
       "      <td>WM</td>\n",
       "      <td>F2F1F0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>9240</td>\n",
       "      <td>cingulum bundle</td>\n",
       "      <td>cgb</td>\n",
       "      <td>F2F1F0</td>\n",
       "      <td>9218</td>\n",
       "      <td>white matter</td>\n",
       "      <td>WM</td>\n",
       "      <td>F2F1F0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160091917</td>\n",
       "      <td>159215934</td>\n",
       "      <td>111</td>\n",
       "      <td>125</td>\n",
       "      <td>107</td>\n",
       "      <td>9708</td>\n",
       "      <td>choroid plexus of the lateral ventricle</td>\n",
       "      <td>CPLV</td>\n",
       "      <td>872601</td>\n",
       "      <td>9352</td>\n",
       "      <td>sulci &amp; spaces</td>\n",
       "      <td>SS</td>\n",
       "      <td>6D6E70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      donor_id  donor_name donor_age donor_color  sample_well  sample_polygon  \\\n",
       "0         9861  H0351.2001  24 years      EC891D          154           27255   \n",
       "1         9861  H0351.2001  24 years      EC891D          859           39232   \n",
       "2         9861  H0351.2001  24 years      EC891D           97           28028   \n",
       "3         9861  H0351.2001  24 years      EC891D          123           27165   \n",
       "4         9861  H0351.2001  24 years      EC891D          539           36756   \n",
       "...        ...         ...       ...         ...          ...             ...   \n",
       "1009     15697  H0351.1016  55 years      EB3B00    160535259       156687325   \n",
       "1010     15697  H0351.1016  55 years      EB3B00    160535275       159233515   \n",
       "1011     15697  H0351.1016  55 years      EB3B00    159226181       156950588   \n",
       "1012     15697  H0351.1016  55 years      EB3B00           -1              -1   \n",
       "1013     15697  H0351.1016  55 years      EB3B00    160091917       159215934   \n",
       "\n",
       "      sample_mri_0  sample_mri_1  sample_mri_2  structure_id  \\\n",
       "0               74            96            29          4053   \n",
       "1              129            78            81          4078   \n",
       "2               85            96            19          4888   \n",
       "3               94           102            26          4047   \n",
       "4              145            75            66          4041   \n",
       "...            ...           ...           ...           ...   \n",
       "1009            80           144           131          9676   \n",
       "1010            82           144           134          9697   \n",
       "1011            97            84           117          9222   \n",
       "1012            -1            -1            -1          9240   \n",
       "1013           111           125           107          9708   \n",
       "\n",
       "                               structure_name structure_abbreviation  \\\n",
       "0                      anterior orbital gyrus                   AOrG   \n",
       "1                           frontal operculum                    fro   \n",
       "2                                frontal pole                     FP   \n",
       "3                                gyrus rectus                    GRe   \n",
       "4      inferior frontal gyrus, opercular part                  opIFG   \n",
       "...                                       ...                    ...   \n",
       "1009                spinal trigeminal nucleus                    Sp5   \n",
       "1010                        vestibular nuclei                    8Ve   \n",
       "1011                          corpus callosum                     cc   \n",
       "1012                          cingulum bundle                    cgb   \n",
       "1013  choroid plexus of the lateral ventricle                   CPLV   \n",
       "\n",
       "     structure_color  top_level_structure_id top_level_structure_name  \\\n",
       "0             E8BF59                    4009             frontal lobe   \n",
       "1             E8C159                    4009             frontal lobe   \n",
       "2             E8C359                    4009             frontal lobe   \n",
       "3             E8C459                    4009             frontal lobe   \n",
       "4             E8C659                    4009             frontal lobe   \n",
       "...              ...                     ...                      ...   \n",
       "1009          377FE4                    9512           myelencephalon   \n",
       "1010          3D85EA                    9512           myelencephalon   \n",
       "1011          F2F1F0                    9218             white matter   \n",
       "1012          F2F1F0                    9218             white matter   \n",
       "1013          872601                    9352           sulci & spaces   \n",
       "\n",
       "     top_level_structure_abbreviation top_level_structure_color  \n",
       "0                                  FL                    E8CD59  \n",
       "1                                  FL                    E8CD59  \n",
       "2                                  FL                    E8CD59  \n",
       "3                                  FL                    E8CD59  \n",
       "4                                  FL                    E8CD59  \n",
       "...                               ...                       ...  \n",
       "1009                               MY                    1F67CC  \n",
       "1010                               MY                    1F67CC  \n",
       "1011                               WM                    F2F1F0  \n",
       "1012                               WM                    F2F1F0  \n",
       "1013                               SS                    6D6E70  \n",
       "\n",
       "[1014 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read PSEN2's columns csv file as dataframe\n",
    "PSEN2_columns_df = pd.read_csv('PSEN2_Columns.csv')\n",
    "PSEN2_columns_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for PSEN2's Probes.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>gene-id</th>\n",
       "      <th>gene-symbol</th>\n",
       "      <th>gene-name</th>\n",
       "      <th>entrez-id</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>start-position</th>\n",
       "      <th>end-position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1025452</td>\n",
       "      <td>A_23_P103398</td>\n",
       "      <td>5632</td>\n",
       "      <td>PSEN2</td>\n",
       "      <td>presenilin 2 (Alzheimer disease 4)</td>\n",
       "      <td>5664</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1025456</td>\n",
       "      <td>A_23_P96985</td>\n",
       "      <td>5632</td>\n",
       "      <td>PSEN2</td>\n",
       "      <td>presenilin 2 (Alzheimer disease 4)</td>\n",
       "      <td>5664</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1025453</td>\n",
       "      <td>A_24_P62521</td>\n",
       "      <td>5632</td>\n",
       "      <td>PSEN2</td>\n",
       "      <td>presenilin 2 (Alzheimer disease 4)</td>\n",
       "      <td>5664</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1025455</td>\n",
       "      <td>CUST_16_PI416558187</td>\n",
       "      <td>5632</td>\n",
       "      <td>PSEN2</td>\n",
       "      <td>presenilin 2 (Alzheimer disease 4)</td>\n",
       "      <td>5664</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1025454</td>\n",
       "      <td>CUST_526_PI416408490</td>\n",
       "      <td>5632</td>\n",
       "      <td>PSEN2</td>\n",
       "      <td>presenilin 2 (Alzheimer disease 4)</td>\n",
       "      <td>5664</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                  name  gene-id gene-symbol  \\\n",
       "0  1025452          A_23_P103398     5632       PSEN2   \n",
       "1  1025456           A_23_P96985     5632       PSEN2   \n",
       "2  1025453           A_24_P62521     5632       PSEN2   \n",
       "3  1025455   CUST_16_PI416558187     5632       PSEN2   \n",
       "4  1025454  CUST_526_PI416408490     5632       PSEN2   \n",
       "\n",
       "                            gene-name  entrez-id  chromosome  start-position  \\\n",
       "0  presenilin 2 (Alzheimer disease 4)       5664           1             NaN   \n",
       "1  presenilin 2 (Alzheimer disease 4)       5664           1             NaN   \n",
       "2  presenilin 2 (Alzheimer disease 4)       5664           1             NaN   \n",
       "3  presenilin 2 (Alzheimer disease 4)       5664           1             NaN   \n",
       "4  presenilin 2 (Alzheimer disease 4)       5664           1             NaN   \n",
       "\n",
       "   end-position  \n",
       "0           NaN  \n",
       "1           NaN  \n",
       "2           NaN  \n",
       "3           NaN  \n",
       "4           NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read PSEN1's probe csv file as dataframe\n",
    "PSEN2_probes_df = pd.read_csv('PSEN2_Probes.csv')\n",
    "PSEN2_probes_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for PSEN2's Expression.csv. Again, we aligned the expression values to their correlated top level brain structures by setting the brain structures as the column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_23_P103398</th>\n",
       "      <th>A_23_P96985</th>\n",
       "      <th>A_24_P62521</th>\n",
       "      <th>CUST_16_PI416558187</th>\n",
       "      <th>CUST_526_PI416408490</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.3638</td>\n",
       "      <td>-3.0252</td>\n",
       "      <td>-1.0759</td>\n",
       "      <td>-1.0937</td>\n",
       "      <td>-0.4062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.7909</td>\n",
       "      <td>-0.8316</td>\n",
       "      <td>-0.1873</td>\n",
       "      <td>-0.8169</td>\n",
       "      <td>-0.0757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.9592</td>\n",
       "      <td>-1.7208</td>\n",
       "      <td>-1.7506</td>\n",
       "      <td>-0.3318</td>\n",
       "      <td>-0.4968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.8183</td>\n",
       "      <td>-0.5072</td>\n",
       "      <td>-0.4353</td>\n",
       "      <td>-0.9255</td>\n",
       "      <td>-0.2891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.6816</td>\n",
       "      <td>0.2668</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>-0.8937</td>\n",
       "      <td>0.0714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>-0.3910</td>\n",
       "      <td>-0.9164</td>\n",
       "      <td>-0.5003</td>\n",
       "      <td>0.8147</td>\n",
       "      <td>-1.1331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>0.8279</td>\n",
       "      <td>0.3873</td>\n",
       "      <td>0.4523</td>\n",
       "      <td>0.8329</td>\n",
       "      <td>0.1227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>-3.7593</td>\n",
       "      <td>-4.2725</td>\n",
       "      <td>-4.1642</td>\n",
       "      <td>0.4053</td>\n",
       "      <td>-4.1472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>1.7010</td>\n",
       "      <td>0.0389</td>\n",
       "      <td>1.0126</td>\n",
       "      <td>1.3903</td>\n",
       "      <td>0.5149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A_23_P103398  A_23_P96985  A_24_P62521  CUST_16_PI416558187  \\\n",
       "0          -1.3638      -3.0252      -1.0759              -1.0937   \n",
       "1          -0.7909      -0.8316      -0.1873              -0.8169   \n",
       "2          -0.9592      -1.7208      -1.7506              -0.3318   \n",
       "3          -0.8183      -0.5072      -0.4353              -0.9255   \n",
       "4          -0.6816       0.2668       0.0074              -0.8937   \n",
       "...            ...          ...          ...                  ...   \n",
       "1009       -0.3910      -0.9164      -0.5003               0.8147   \n",
       "1010        0.8279       0.3873       0.4523               0.8329   \n",
       "1011       -3.7593      -4.2725      -4.1642               0.4053   \n",
       "1012           NaN          NaN          NaN                  NaN   \n",
       "1013        1.7010       0.0389       1.0126               1.3903   \n",
       "\n",
       "      CUST_526_PI416408490  \n",
       "0                  -0.4062  \n",
       "1                  -0.0757  \n",
       "2                  -0.4968  \n",
       "3                  -0.2891  \n",
       "4                   0.0714  \n",
       "...                    ...  \n",
       "1009               -1.1331  \n",
       "1010                0.1227  \n",
       "1011               -4.1472  \n",
       "1012                   NaN  \n",
       "1013                0.5149  \n",
       "\n",
       "[1014 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read PSEN2's expression csv file as dataframe\n",
    "PSEN2_exp_df = pd.read_csv('PSEN2_Expression.csv', header=None)\n",
    "\n",
    "# Set the index to the 0 column which contains the probe ID\n",
    "PSEN2_exp_df = PSEN2_exp_df.set_index(0)\n",
    "# Transpose the dataframe\n",
    "PSEN2_exp_df = PSEN2_exp_df.transpose()\n",
    "# Reset the index\n",
    "PSEN2_exp_df = PSEN2_exp_df.reset_index(drop = True)\n",
    "PSEN2_exp_df.columns = list(PSEN2_probes_df['name'])\n",
    "PSEN2_exp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_23_P103398</th>\n",
       "      <th>A_23_P96985</th>\n",
       "      <th>A_24_P62521</th>\n",
       "      <th>CUST_16_PI416558187</th>\n",
       "      <th>CUST_526_PI416408490</th>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th>donor_name</th>\n",
       "      <th>donor_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.3638</td>\n",
       "      <td>-3.0252</td>\n",
       "      <td>-1.0759</td>\n",
       "      <td>-1.0937</td>\n",
       "      <td>-0.4062</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.7909</td>\n",
       "      <td>-0.8316</td>\n",
       "      <td>-0.1873</td>\n",
       "      <td>-0.8169</td>\n",
       "      <td>-0.0757</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.9592</td>\n",
       "      <td>-1.7208</td>\n",
       "      <td>-1.7506</td>\n",
       "      <td>-0.3318</td>\n",
       "      <td>-0.4968</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.8183</td>\n",
       "      <td>-0.5072</td>\n",
       "      <td>-0.4353</td>\n",
       "      <td>-0.9255</td>\n",
       "      <td>-0.2891</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.6816</td>\n",
       "      <td>0.2668</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>-0.8937</td>\n",
       "      <td>0.0714</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>-0.3910</td>\n",
       "      <td>-0.9164</td>\n",
       "      <td>-0.5003</td>\n",
       "      <td>0.8147</td>\n",
       "      <td>-1.1331</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>0.8279</td>\n",
       "      <td>0.3873</td>\n",
       "      <td>0.4523</td>\n",
       "      <td>0.8329</td>\n",
       "      <td>0.1227</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>-3.7593</td>\n",
       "      <td>-4.2725</td>\n",
       "      <td>-4.1642</td>\n",
       "      <td>0.4053</td>\n",
       "      <td>-4.1472</td>\n",
       "      <td>white matter</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>white matter</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>1.7010</td>\n",
       "      <td>0.0389</td>\n",
       "      <td>1.0126</td>\n",
       "      <td>1.3903</td>\n",
       "      <td>0.5149</td>\n",
       "      <td>sulci &amp; spaces</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A_23_P103398  A_23_P96985  A_24_P62521  CUST_16_PI416558187  \\\n",
       "0          -1.3638      -3.0252      -1.0759              -1.0937   \n",
       "1          -0.7909      -0.8316      -0.1873              -0.8169   \n",
       "2          -0.9592      -1.7208      -1.7506              -0.3318   \n",
       "3          -0.8183      -0.5072      -0.4353              -0.9255   \n",
       "4          -0.6816       0.2668       0.0074              -0.8937   \n",
       "...            ...          ...          ...                  ...   \n",
       "1009       -0.3910      -0.9164      -0.5003               0.8147   \n",
       "1010        0.8279       0.3873       0.4523               0.8329   \n",
       "1011       -3.7593      -4.2725      -4.1642               0.4053   \n",
       "1012           NaN          NaN          NaN                  NaN   \n",
       "1013        1.7010       0.0389       1.0126               1.3903   \n",
       "\n",
       "      CUST_526_PI416408490 top_level_structure_name  donor_name donor_age  \n",
       "0                  -0.4062             frontal lobe  H0351.2001  24 years  \n",
       "1                  -0.0757             frontal lobe  H0351.2001  24 years  \n",
       "2                  -0.4968             frontal lobe  H0351.2001  24 years  \n",
       "3                  -0.2891             frontal lobe  H0351.2001  24 years  \n",
       "4                   0.0714             frontal lobe  H0351.2001  24 years  \n",
       "...                    ...                      ...         ...       ...  \n",
       "1009               -1.1331           myelencephalon  H0351.1016  55 years  \n",
       "1010                0.1227           myelencephalon  H0351.1016  55 years  \n",
       "1011               -4.1472             white matter  H0351.1016  55 years  \n",
       "1012                   NaN             white matter  H0351.1016  55 years  \n",
       "1013                0.5149           sulci & spaces  H0351.1016  55 years  \n",
       "\n",
       "[1014 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PSEN2_age_region_df = PSEN2_exp_df.join(PSEN2_columns_df['top_level_structure_name']).join(PSEN2_columns_df['donor_name']).join(PSEN2_columns_df['donor_age'])\n",
    "PSEN2_age_region_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"four\"></a>\n",
    "\n",
    "## APOE Gene\n",
    "\n",
    "We repeat the steps of turning the CSV files of APOE into DataFrames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for APOE's Columns.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>donor_id</th>\n",
       "      <th>donor_name</th>\n",
       "      <th>donor_age</th>\n",
       "      <th>donor_color</th>\n",
       "      <th>sample_well</th>\n",
       "      <th>sample_polygon</th>\n",
       "      <th>sample_mri_0</th>\n",
       "      <th>sample_mri_1</th>\n",
       "      <th>sample_mri_2</th>\n",
       "      <th>structure_id</th>\n",
       "      <th>structure_name</th>\n",
       "      <th>structure_abbreviation</th>\n",
       "      <th>structure_color</th>\n",
       "      <th>top_level_structure_id</th>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th>top_level_structure_abbreviation</th>\n",
       "      <th>top_level_structure_color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>154</td>\n",
       "      <td>27255</td>\n",
       "      <td>74</td>\n",
       "      <td>96</td>\n",
       "      <td>29</td>\n",
       "      <td>4053</td>\n",
       "      <td>anterior orbital gyrus</td>\n",
       "      <td>AOrG</td>\n",
       "      <td>E8BF59</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>859</td>\n",
       "      <td>39232</td>\n",
       "      <td>129</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>4078</td>\n",
       "      <td>frontal operculum</td>\n",
       "      <td>fro</td>\n",
       "      <td>E8C159</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>97</td>\n",
       "      <td>28028</td>\n",
       "      <td>85</td>\n",
       "      <td>96</td>\n",
       "      <td>19</td>\n",
       "      <td>4888</td>\n",
       "      <td>frontal pole</td>\n",
       "      <td>FP</td>\n",
       "      <td>E8C359</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>123</td>\n",
       "      <td>27165</td>\n",
       "      <td>94</td>\n",
       "      <td>102</td>\n",
       "      <td>26</td>\n",
       "      <td>4047</td>\n",
       "      <td>gyrus rectus</td>\n",
       "      <td>GRe</td>\n",
       "      <td>E8C459</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9861</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "      <td>EC891D</td>\n",
       "      <td>539</td>\n",
       "      <td>36756</td>\n",
       "      <td>145</td>\n",
       "      <td>75</td>\n",
       "      <td>66</td>\n",
       "      <td>4041</td>\n",
       "      <td>inferior frontal gyrus, opercular part</td>\n",
       "      <td>opIFG</td>\n",
       "      <td>E8C659</td>\n",
       "      <td>4009</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>FL</td>\n",
       "      <td>E8CD59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160535259</td>\n",
       "      <td>156687325</td>\n",
       "      <td>80</td>\n",
       "      <td>144</td>\n",
       "      <td>131</td>\n",
       "      <td>9676</td>\n",
       "      <td>spinal trigeminal nucleus</td>\n",
       "      <td>Sp5</td>\n",
       "      <td>377FE4</td>\n",
       "      <td>9512</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>MY</td>\n",
       "      <td>1F67CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160535275</td>\n",
       "      <td>159233515</td>\n",
       "      <td>82</td>\n",
       "      <td>144</td>\n",
       "      <td>134</td>\n",
       "      <td>9697</td>\n",
       "      <td>vestibular nuclei</td>\n",
       "      <td>8Ve</td>\n",
       "      <td>3D85EA</td>\n",
       "      <td>9512</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>MY</td>\n",
       "      <td>1F67CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>159226181</td>\n",
       "      <td>156950588</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
       "      <td>117</td>\n",
       "      <td>9222</td>\n",
       "      <td>corpus callosum</td>\n",
       "      <td>cc</td>\n",
       "      <td>F2F1F0</td>\n",
       "      <td>9218</td>\n",
       "      <td>white matter</td>\n",
       "      <td>WM</td>\n",
       "      <td>F2F1F0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>9240</td>\n",
       "      <td>cingulum bundle</td>\n",
       "      <td>cgb</td>\n",
       "      <td>F2F1F0</td>\n",
       "      <td>9218</td>\n",
       "      <td>white matter</td>\n",
       "      <td>WM</td>\n",
       "      <td>F2F1F0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>15697</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "      <td>EB3B00</td>\n",
       "      <td>160091917</td>\n",
       "      <td>159215934</td>\n",
       "      <td>111</td>\n",
       "      <td>125</td>\n",
       "      <td>107</td>\n",
       "      <td>9708</td>\n",
       "      <td>choroid plexus of the lateral ventricle</td>\n",
       "      <td>CPLV</td>\n",
       "      <td>872601</td>\n",
       "      <td>9352</td>\n",
       "      <td>sulci &amp; spaces</td>\n",
       "      <td>SS</td>\n",
       "      <td>6D6E70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      donor_id  donor_name donor_age donor_color  sample_well  sample_polygon  \\\n",
       "0         9861  H0351.2001  24 years      EC891D          154           27255   \n",
       "1         9861  H0351.2001  24 years      EC891D          859           39232   \n",
       "2         9861  H0351.2001  24 years      EC891D           97           28028   \n",
       "3         9861  H0351.2001  24 years      EC891D          123           27165   \n",
       "4         9861  H0351.2001  24 years      EC891D          539           36756   \n",
       "...        ...         ...       ...         ...          ...             ...   \n",
       "1009     15697  H0351.1016  55 years      EB3B00    160535259       156687325   \n",
       "1010     15697  H0351.1016  55 years      EB3B00    160535275       159233515   \n",
       "1011     15697  H0351.1016  55 years      EB3B00    159226181       156950588   \n",
       "1012     15697  H0351.1016  55 years      EB3B00           -1              -1   \n",
       "1013     15697  H0351.1016  55 years      EB3B00    160091917       159215934   \n",
       "\n",
       "      sample_mri_0  sample_mri_1  sample_mri_2  structure_id  \\\n",
       "0               74            96            29          4053   \n",
       "1              129            78            81          4078   \n",
       "2               85            96            19          4888   \n",
       "3               94           102            26          4047   \n",
       "4              145            75            66          4041   \n",
       "...            ...           ...           ...           ...   \n",
       "1009            80           144           131          9676   \n",
       "1010            82           144           134          9697   \n",
       "1011            97            84           117          9222   \n",
       "1012            -1            -1            -1          9240   \n",
       "1013           111           125           107          9708   \n",
       "\n",
       "                               structure_name structure_abbreviation  \\\n",
       "0                      anterior orbital gyrus                   AOrG   \n",
       "1                           frontal operculum                    fro   \n",
       "2                                frontal pole                     FP   \n",
       "3                                gyrus rectus                    GRe   \n",
       "4      inferior frontal gyrus, opercular part                  opIFG   \n",
       "...                                       ...                    ...   \n",
       "1009                spinal trigeminal nucleus                    Sp5   \n",
       "1010                        vestibular nuclei                    8Ve   \n",
       "1011                          corpus callosum                     cc   \n",
       "1012                          cingulum bundle                    cgb   \n",
       "1013  choroid plexus of the lateral ventricle                   CPLV   \n",
       "\n",
       "     structure_color  top_level_structure_id top_level_structure_name  \\\n",
       "0             E8BF59                    4009             frontal lobe   \n",
       "1             E8C159                    4009             frontal lobe   \n",
       "2             E8C359                    4009             frontal lobe   \n",
       "3             E8C459                    4009             frontal lobe   \n",
       "4             E8C659                    4009             frontal lobe   \n",
       "...              ...                     ...                      ...   \n",
       "1009          377FE4                    9512           myelencephalon   \n",
       "1010          3D85EA                    9512           myelencephalon   \n",
       "1011          F2F1F0                    9218             white matter   \n",
       "1012          F2F1F0                    9218             white matter   \n",
       "1013          872601                    9352           sulci & spaces   \n",
       "\n",
       "     top_level_structure_abbreviation top_level_structure_color  \n",
       "0                                  FL                    E8CD59  \n",
       "1                                  FL                    E8CD59  \n",
       "2                                  FL                    E8CD59  \n",
       "3                                  FL                    E8CD59  \n",
       "4                                  FL                    E8CD59  \n",
       "...                               ...                       ...  \n",
       "1009                               MY                    1F67CC  \n",
       "1010                               MY                    1F67CC  \n",
       "1011                               WM                    F2F1F0  \n",
       "1012                               WM                    F2F1F0  \n",
       "1013                               SS                    6D6E70  \n",
       "\n",
       "[1014 rows x 17 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read APOE's columns csv file as dataframe\n",
    "APOE_columns_df = pd.read_csv('APOE_Columns.csv')\n",
    "APOE_columns_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for APOEs Probes.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>gene-id</th>\n",
       "      <th>gene-symbol</th>\n",
       "      <th>gene-name</th>\n",
       "      <th>entrez-id</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>start-position</th>\n",
       "      <th>end-position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1059132</td>\n",
       "      <td>A_23_P164650</td>\n",
       "      <td>345</td>\n",
       "      <td>APOE</td>\n",
       "      <td>apolipoprotein E</td>\n",
       "      <td>348</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1067762</td>\n",
       "      <td>A_24_P788772</td>\n",
       "      <td>345</td>\n",
       "      <td>APOE</td>\n",
       "      <td>apolipoprotein E</td>\n",
       "      <td>348</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1059133</td>\n",
       "      <td>A_32_P93036</td>\n",
       "      <td>345</td>\n",
       "      <td>APOE</td>\n",
       "      <td>apolipoprotein E</td>\n",
       "      <td>348</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id          name  gene-id gene-symbol         gene-name  entrez-id  \\\n",
       "0  1059132  A_23_P164650      345        APOE  apolipoprotein E        348   \n",
       "1  1067762  A_24_P788772      345        APOE  apolipoprotein E        348   \n",
       "2  1059133   A_32_P93036      345        APOE  apolipoprotein E        348   \n",
       "\n",
       "   chromosome  start-position  end-position  \n",
       "0          19             NaN           NaN  \n",
       "1          19             NaN           NaN  \n",
       "2          19             NaN           NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read APOE's probe csv file as dataframe\n",
    "APOE_probes_df = pd.read_csv('APOE_Probes.csv')\n",
    "APOE_probes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_23_P164650</th>\n",
       "      <th>A_24_P788772</th>\n",
       "      <th>A_32_P93036</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.7123</td>\n",
       "      <td>-0.4996</td>\n",
       "      <td>-1.9294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.5436</td>\n",
       "      <td>-0.1458</td>\n",
       "      <td>-0.4091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.9559</td>\n",
       "      <td>-0.7125</td>\n",
       "      <td>-1.1660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.8651</td>\n",
       "      <td>-0.5736</td>\n",
       "      <td>-1.3164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.9899</td>\n",
       "      <td>-0.4632</td>\n",
       "      <td>-0.4214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>0.5112</td>\n",
       "      <td>0.4277</td>\n",
       "      <td>-0.6280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>-0.4948</td>\n",
       "      <td>-0.3298</td>\n",
       "      <td>-0.8422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>-0.4700</td>\n",
       "      <td>-0.6282</td>\n",
       "      <td>-0.7245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>-2.0046</td>\n",
       "      <td>-2.1501</td>\n",
       "      <td>-1.5725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A_23_P164650  A_24_P788772  A_32_P93036\n",
       "0          -0.7123       -0.4996      -1.9294\n",
       "1          -0.5436       -0.1458      -0.4091\n",
       "2          -0.9559       -0.7125      -1.1660\n",
       "3          -0.8651       -0.5736      -1.3164\n",
       "4          -0.9899       -0.4632      -0.4214\n",
       "...            ...           ...          ...\n",
       "1009        0.5112        0.4277      -0.6280\n",
       "1010       -0.4948       -0.3298      -0.8422\n",
       "1011       -0.4700       -0.6282      -0.7245\n",
       "1012           NaN           NaN          NaN\n",
       "1013       -2.0046       -2.1501      -1.5725\n",
       "\n",
       "[1014 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read APOE's expression csv file as dataframe\n",
    "APOE_exp_df = pd.read_csv('APOE_Expression.csv', header=None)\n",
    "\n",
    "# Set the index to the 0 column which contains the probe ID\n",
    "APOE_exp_df = APOE_exp_df.set_index(0)\n",
    "# Transpose the dataframe\n",
    "APOE_exp_df = APOE_exp_df.transpose()\n",
    "# Reset the index\n",
    "APOE_exp_df = APOE_exp_df.reset_index(drop = True)\n",
    "APOE_exp_df.columns = list(APOE_probes_df['name'])\n",
    "APOE_exp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made a DataFrame for APOE's Expression.csv. Again, we aligned the expression values to their correlated top level brain structures by setting the brain structures as the column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_23_P164650</th>\n",
       "      <th>A_24_P788772</th>\n",
       "      <th>A_32_P93036</th>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th>donor_name</th>\n",
       "      <th>donor_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.7123</td>\n",
       "      <td>-0.4996</td>\n",
       "      <td>-1.9294</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.5436</td>\n",
       "      <td>-0.1458</td>\n",
       "      <td>-0.4091</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.9559</td>\n",
       "      <td>-0.7125</td>\n",
       "      <td>-1.1660</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.8651</td>\n",
       "      <td>-0.5736</td>\n",
       "      <td>-1.3164</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.9899</td>\n",
       "      <td>-0.4632</td>\n",
       "      <td>-0.4214</td>\n",
       "      <td>frontal lobe</td>\n",
       "      <td>H0351.2001</td>\n",
       "      <td>24 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>0.5112</td>\n",
       "      <td>0.4277</td>\n",
       "      <td>-0.6280</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>-0.4948</td>\n",
       "      <td>-0.3298</td>\n",
       "      <td>-0.8422</td>\n",
       "      <td>myelencephalon</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>-0.4700</td>\n",
       "      <td>-0.6282</td>\n",
       "      <td>-0.7245</td>\n",
       "      <td>white matter</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>white matter</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>-2.0046</td>\n",
       "      <td>-2.1501</td>\n",
       "      <td>-1.5725</td>\n",
       "      <td>sulci &amp; spaces</td>\n",
       "      <td>H0351.1016</td>\n",
       "      <td>55 years</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A_23_P164650  A_24_P788772  A_32_P93036 top_level_structure_name  \\\n",
       "0          -0.7123       -0.4996      -1.9294             frontal lobe   \n",
       "1          -0.5436       -0.1458      -0.4091             frontal lobe   \n",
       "2          -0.9559       -0.7125      -1.1660             frontal lobe   \n",
       "3          -0.8651       -0.5736      -1.3164             frontal lobe   \n",
       "4          -0.9899       -0.4632      -0.4214             frontal lobe   \n",
       "...            ...           ...          ...                      ...   \n",
       "1009        0.5112        0.4277      -0.6280           myelencephalon   \n",
       "1010       -0.4948       -0.3298      -0.8422           myelencephalon   \n",
       "1011       -0.4700       -0.6282      -0.7245             white matter   \n",
       "1012           NaN           NaN          NaN             white matter   \n",
       "1013       -2.0046       -2.1501      -1.5725           sulci & spaces   \n",
       "\n",
       "      donor_name donor_age  \n",
       "0     H0351.2001  24 years  \n",
       "1     H0351.2001  24 years  \n",
       "2     H0351.2001  24 years  \n",
       "3     H0351.2001  24 years  \n",
       "4     H0351.2001  24 years  \n",
       "...          ...       ...  \n",
       "1009  H0351.1016  55 years  \n",
       "1010  H0351.1016  55 years  \n",
       "1011  H0351.1016  55 years  \n",
       "1012  H0351.1016  55 years  \n",
       "1013  H0351.1016  55 years  \n",
       "\n",
       "[1014 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "APOE_age_region_df = APOE_exp_df.join(APOE_columns_df['top_level_structure_name']).join(APOE_columns_df['donor_name']).join(APOE_columns_df['donor_age'])\n",
    "APOE_age_region_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis:\n",
    "\n",
    "<a id=\"five\"></a>\n",
    "\n",
    "## Plotting Graphs\n",
    "\n",
    "We have finished moving all the gene data from the database into Python. Next, we visualized the data by making graphs. To do this, we first imported the necessary files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"five.2\"></a>\n",
    "\n",
    "### Heatmap\n",
    "\n",
    "We graphed heatmaps of each gene by their expression in a brain region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APP (A_32_P167000)</th>\n",
       "      <th>APP (A_24_P314159)</th>\n",
       "      <th>APP (CUST_84_PI416408490)</th>\n",
       "      <th>PSEN1 (A_23_P205686)</th>\n",
       "      <th>PSEN1 (A_23_P340728)</th>\n",
       "      <th>PSEN2 (A_23_P103398)</th>\n",
       "      <th>PSEN2 (A_23_P96985)</th>\n",
       "      <th>PSEN2 (A_24_P62521)</th>\n",
       "      <th>PSEN2 (CUST_16_PI416558187)</th>\n",
       "      <th>PSEN2 (CUST_526_PI416408490)</th>\n",
       "      <th>APOE (A_23_P164650)</th>\n",
       "      <th>APOE (A_24_P788772)</th>\n",
       "      <th>APOE (A_32_P93036)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-1.3986</td>\n",
       "      <td>1.3230</td>\n",
       "      <td>0.7138</td>\n",
       "      <td>-0.2481</td>\n",
       "      <td>0.0476</td>\n",
       "      <td>-1.3638</td>\n",
       "      <td>-3.0252</td>\n",
       "      <td>-1.0759</td>\n",
       "      <td>-1.0937</td>\n",
       "      <td>-0.4062</td>\n",
       "      <td>-0.7123</td>\n",
       "      <td>-0.4996</td>\n",
       "      <td>-1.9294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>1.6176</td>\n",
       "      <td>-0.0828</td>\n",
       "      <td>-0.2437</td>\n",
       "      <td>-0.4923</td>\n",
       "      <td>-1.2025</td>\n",
       "      <td>-0.7909</td>\n",
       "      <td>-0.8316</td>\n",
       "      <td>-0.1873</td>\n",
       "      <td>-0.8169</td>\n",
       "      <td>-0.0757</td>\n",
       "      <td>-0.5436</td>\n",
       "      <td>-0.1458</td>\n",
       "      <td>-0.4091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-2.2992</td>\n",
       "      <td>0.7687</td>\n",
       "      <td>0.4941</td>\n",
       "      <td>-0.7403</td>\n",
       "      <td>-0.7898</td>\n",
       "      <td>-0.9592</td>\n",
       "      <td>-1.7208</td>\n",
       "      <td>-1.7506</td>\n",
       "      <td>-0.3318</td>\n",
       "      <td>-0.4968</td>\n",
       "      <td>-0.9559</td>\n",
       "      <td>-0.7125</td>\n",
       "      <td>-1.1660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>0.6595</td>\n",
       "      <td>0.5479</td>\n",
       "      <td>0.3077</td>\n",
       "      <td>-0.4826</td>\n",
       "      <td>-0.8385</td>\n",
       "      <td>-0.8183</td>\n",
       "      <td>-0.5072</td>\n",
       "      <td>-0.4353</td>\n",
       "      <td>-0.9255</td>\n",
       "      <td>-0.2891</td>\n",
       "      <td>-0.8651</td>\n",
       "      <td>-0.5736</td>\n",
       "      <td>-1.3164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>1.1978</td>\n",
       "      <td>-0.5632</td>\n",
       "      <td>-0.4256</td>\n",
       "      <td>-0.4261</td>\n",
       "      <td>-1.0389</td>\n",
       "      <td>-0.6816</td>\n",
       "      <td>0.2668</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>-0.8937</td>\n",
       "      <td>0.0714</td>\n",
       "      <td>-0.9899</td>\n",
       "      <td>-0.4632</td>\n",
       "      <td>-0.4214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>1.0890</td>\n",
       "      <td>1.1379</td>\n",
       "      <td>1.1774</td>\n",
       "      <td>0.8996</td>\n",
       "      <td>1.5231</td>\n",
       "      <td>0.4563</td>\n",
       "      <td>-0.6729</td>\n",
       "      <td>-0.3077</td>\n",
       "      <td>0.9550</td>\n",
       "      <td>-1.1787</td>\n",
       "      <td>0.0459</td>\n",
       "      <td>-0.1131</td>\n",
       "      <td>-0.8141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>-0.3345</td>\n",
       "      <td>1.1739</td>\n",
       "      <td>1.2782</td>\n",
       "      <td>0.2341</td>\n",
       "      <td>0.6752</td>\n",
       "      <td>-0.3910</td>\n",
       "      <td>-0.9164</td>\n",
       "      <td>-0.5003</td>\n",
       "      <td>0.8147</td>\n",
       "      <td>-1.1331</td>\n",
       "      <td>0.5112</td>\n",
       "      <td>0.4277</td>\n",
       "      <td>-0.6280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>0.6085</td>\n",
       "      <td>1.2909</td>\n",
       "      <td>1.6062</td>\n",
       "      <td>1.1637</td>\n",
       "      <td>1.5391</td>\n",
       "      <td>0.8279</td>\n",
       "      <td>0.3873</td>\n",
       "      <td>0.4523</td>\n",
       "      <td>0.8329</td>\n",
       "      <td>0.1227</td>\n",
       "      <td>-0.4948</td>\n",
       "      <td>-0.3298</td>\n",
       "      <td>-0.8422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white matter</th>\n",
       "      <td>0.2663</td>\n",
       "      <td>0.9404</td>\n",
       "      <td>0.7080</td>\n",
       "      <td>2.8550</td>\n",
       "      <td>2.5623</td>\n",
       "      <td>-3.7593</td>\n",
       "      <td>-4.2725</td>\n",
       "      <td>-4.1642</td>\n",
       "      <td>0.4053</td>\n",
       "      <td>-4.1472</td>\n",
       "      <td>-0.4700</td>\n",
       "      <td>-0.6282</td>\n",
       "      <td>-0.7245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulci &amp; spaces</th>\n",
       "      <td>-2.5205</td>\n",
       "      <td>0.7128</td>\n",
       "      <td>0.3189</td>\n",
       "      <td>0.3855</td>\n",
       "      <td>0.2265</td>\n",
       "      <td>1.7010</td>\n",
       "      <td>0.0389</td>\n",
       "      <td>1.0126</td>\n",
       "      <td>1.3903</td>\n",
       "      <td>0.5149</td>\n",
       "      <td>-2.0046</td>\n",
       "      <td>-2.1501</td>\n",
       "      <td>-1.5725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>841 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          APP (A_32_P167000)  APP (A_24_P314159)  \\\n",
       "top_level_structure_name                                           \n",
       "frontal lobe                         -1.3986              1.3230   \n",
       "frontal lobe                          1.6176             -0.0828   \n",
       "frontal lobe                         -2.2992              0.7687   \n",
       "frontal lobe                          0.6595              0.5479   \n",
       "frontal lobe                          1.1978             -0.5632   \n",
       "...                                      ...                 ...   \n",
       "myelencephalon                        1.0890              1.1379   \n",
       "myelencephalon                       -0.3345              1.1739   \n",
       "myelencephalon                        0.6085              1.2909   \n",
       "white matter                          0.2663              0.9404   \n",
       "sulci & spaces                       -2.5205              0.7128   \n",
       "\n",
       "                          APP (CUST_84_PI416408490)  PSEN1 (A_23_P205686)  \\\n",
       "top_level_structure_name                                                    \n",
       "frontal lobe                                 0.7138               -0.2481   \n",
       "frontal lobe                                -0.2437               -0.4923   \n",
       "frontal lobe                                 0.4941               -0.7403   \n",
       "frontal lobe                                 0.3077               -0.4826   \n",
       "frontal lobe                                -0.4256               -0.4261   \n",
       "...                                             ...                   ...   \n",
       "myelencephalon                               1.1774                0.8996   \n",
       "myelencephalon                               1.2782                0.2341   \n",
       "myelencephalon                               1.6062                1.1637   \n",
       "white matter                                 0.7080                2.8550   \n",
       "sulci & spaces                               0.3189                0.3855   \n",
       "\n",
       "                          PSEN1 (A_23_P340728)  PSEN2 (A_23_P103398)  \\\n",
       "top_level_structure_name                                               \n",
       "frontal lobe                            0.0476               -1.3638   \n",
       "frontal lobe                           -1.2025               -0.7909   \n",
       "frontal lobe                           -0.7898               -0.9592   \n",
       "frontal lobe                           -0.8385               -0.8183   \n",
       "frontal lobe                           -1.0389               -0.6816   \n",
       "...                                        ...                   ...   \n",
       "myelencephalon                          1.5231                0.4563   \n",
       "myelencephalon                          0.6752               -0.3910   \n",
       "myelencephalon                          1.5391                0.8279   \n",
       "white matter                            2.5623               -3.7593   \n",
       "sulci & spaces                          0.2265                1.7010   \n",
       "\n",
       "                          PSEN2 (A_23_P96985)  PSEN2 (A_24_P62521)  \\\n",
       "top_level_structure_name                                             \n",
       "frontal lobe                          -3.0252              -1.0759   \n",
       "frontal lobe                          -0.8316              -0.1873   \n",
       "frontal lobe                          -1.7208              -1.7506   \n",
       "frontal lobe                          -0.5072              -0.4353   \n",
       "frontal lobe                           0.2668               0.0074   \n",
       "...                                       ...                  ...   \n",
       "myelencephalon                        -0.6729              -0.3077   \n",
       "myelencephalon                        -0.9164              -0.5003   \n",
       "myelencephalon                         0.3873               0.4523   \n",
       "white matter                          -4.2725              -4.1642   \n",
       "sulci & spaces                         0.0389               1.0126   \n",
       "\n",
       "                          PSEN2 (CUST_16_PI416558187)  \\\n",
       "top_level_structure_name                                \n",
       "frontal lobe                                  -1.0937   \n",
       "frontal lobe                                  -0.8169   \n",
       "frontal lobe                                  -0.3318   \n",
       "frontal lobe                                  -0.9255   \n",
       "frontal lobe                                  -0.8937   \n",
       "...                                               ...   \n",
       "myelencephalon                                 0.9550   \n",
       "myelencephalon                                 0.8147   \n",
       "myelencephalon                                 0.8329   \n",
       "white matter                                   0.4053   \n",
       "sulci & spaces                                 1.3903   \n",
       "\n",
       "                          PSEN2 (CUST_526_PI416408490)  APOE (A_23_P164650)  \\\n",
       "top_level_structure_name                                                      \n",
       "frontal lobe                                   -0.4062              -0.7123   \n",
       "frontal lobe                                   -0.0757              -0.5436   \n",
       "frontal lobe                                   -0.4968              -0.9559   \n",
       "frontal lobe                                   -0.2891              -0.8651   \n",
       "frontal lobe                                    0.0714              -0.9899   \n",
       "...                                                ...                  ...   \n",
       "myelencephalon                                 -1.1787               0.0459   \n",
       "myelencephalon                                 -1.1331               0.5112   \n",
       "myelencephalon                                  0.1227              -0.4948   \n",
       "white matter                                   -4.1472              -0.4700   \n",
       "sulci & spaces                                  0.5149              -2.0046   \n",
       "\n",
       "                          APOE (A_24_P788772)  APOE (A_32_P93036)  \n",
       "top_level_structure_name                                           \n",
       "frontal lobe                          -0.4996             -1.9294  \n",
       "frontal lobe                          -0.1458             -0.4091  \n",
       "frontal lobe                          -0.7125             -1.1660  \n",
       "frontal lobe                          -0.5736             -1.3164  \n",
       "frontal lobe                          -0.4632             -0.4214  \n",
       "...                                       ...                 ...  \n",
       "myelencephalon                        -0.1131             -0.8141  \n",
       "myelencephalon                         0.4277             -0.6280  \n",
       "myelencephalon                        -0.3298             -0.8422  \n",
       "white matter                          -0.6282             -0.7245  \n",
       "sulci & spaces                        -2.1501             -1.5725  \n",
       "\n",
       "[841 rows x 13 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a dataframe with all probes from all four genes\n",
    "All_genes = APP_exp_df.join(PSEN1_exp_df).join(PSEN2_exp_df).join(APOE_exp_df)\n",
    "# Add the column for top level structure name\n",
    "All_genes = All_genes.join(APOE_columns_df['top_level_structure_name'])\n",
    "# Drop the rows with NaN values\n",
    "All_genes = All_genes.dropna()\n",
    "# Set index to top level structure name\n",
    "All_genes = All_genes.set_index('top_level_structure_name')\n",
    "# Rename probes with gene attached\n",
    "All_genes.columns = ['APP (A_32_P167000)', 'APP (A_24_P314159)', 'APP (CUST_84_PI416408490)', 'PSEN1 (A_23_P205686)',\n",
    "       'PSEN1 (A_23_P340728)', 'PSEN2 (A_23_P103398)', 'PSEN2 (A_23_P96985)', 'PSEN2 (A_24_P62521)',\n",
    "       'PSEN2 (CUST_16_PI416558187)', 'PSEN2 (CUST_526_PI416408490)', 'APOE (A_23_P164650)',\n",
    "       'APOE (A_24_P788772)', 'APOE (A_32_P93036)']\n",
    "All_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "ALLGeneprobe_data = pd.pivot_table(All_genes, values = ('APP (A_32_P167000)', 'APP (A_24_P314159)', \n",
    "        'APP (CUST_84_PI416408490)', 'PSEN1 (A_23_P205686)', 'PSEN1 (A_23_P340728)', 'PSEN2 (A_23_P103398)', \n",
    "        'PSEN2 (A_23_P96985)', 'PSEN2 (A_24_P62521)', 'PSEN2 (CUST_16_PI416558187)', 'PSEN2 (CUST_526_PI416408490)', \n",
    "        'APOE (A_23_P164650)', 'APOE (A_24_P788772)', 'APOE (A_32_P93036)'), index = ['top_level_structure_name'])\n",
    "\n",
    "plt.suptitle('Heatmap of All Four Genes (APOE, APP, PSEN1, PSEN2) in Top Level Brain Structures')\n",
    "plt.title('|-------APOE-------|  |--------APP--------|  |--PSEN1--|  |--------------PSEN2--------------|')\n",
    "sns.heatmap(ALLGeneprobe_data, cmap=\"icefire\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"five.3\"></a>\n",
    "\n",
    "### Boxplot\n",
    "\n",
    "We made a boxplot for the gene expressions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot for APP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_32_P167000</th>\n",
       "      <th>A_24_P314159</th>\n",
       "      <th>CUST_84_PI416408490</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-1.3986</td>\n",
       "      <td>1.3230</td>\n",
       "      <td>0.7138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>1.6176</td>\n",
       "      <td>-0.0828</td>\n",
       "      <td>-0.2437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-2.2992</td>\n",
       "      <td>0.7687</td>\n",
       "      <td>0.4941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>0.6595</td>\n",
       "      <td>0.5479</td>\n",
       "      <td>0.3077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>1.1978</td>\n",
       "      <td>-0.5632</td>\n",
       "      <td>-0.4256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>1.0890</td>\n",
       "      <td>1.1379</td>\n",
       "      <td>1.1774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>-0.3345</td>\n",
       "      <td>1.1739</td>\n",
       "      <td>1.2782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>0.6085</td>\n",
       "      <td>1.2909</td>\n",
       "      <td>1.6062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white matter</th>\n",
       "      <td>0.2663</td>\n",
       "      <td>0.9404</td>\n",
       "      <td>0.7080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulci &amp; spaces</th>\n",
       "      <td>-2.5205</td>\n",
       "      <td>0.7128</td>\n",
       "      <td>0.3189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>841 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          A_32_P167000  A_24_P314159  CUST_84_PI416408490\n",
       "top_level_structure_name                                                 \n",
       "frontal lobe                   -1.3986        1.3230               0.7138\n",
       "frontal lobe                    1.6176       -0.0828              -0.2437\n",
       "frontal lobe                   -2.2992        0.7687               0.4941\n",
       "frontal lobe                    0.6595        0.5479               0.3077\n",
       "frontal lobe                    1.1978       -0.5632              -0.4256\n",
       "...                                ...           ...                  ...\n",
       "myelencephalon                  1.0890        1.1379               1.1774\n",
       "myelencephalon                 -0.3345        1.1739               1.2782\n",
       "myelencephalon                  0.6085        1.2909               1.6062\n",
       "white matter                    0.2663        0.9404               0.7080\n",
       "sulci & spaces                 -2.5205        0.7128               0.3189\n",
       "\n",
       "[841 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join the column for top_level_structure_names to the APP expression df\n",
    "APP_by_id = APP_exp_df.join(APP_columns_df['top_level_structure_name'])\n",
    "# Set the index to the structure names\n",
    "APP_by_id = APP_by_id.set_index('top_level_structure_name')\n",
    "# Drop NaN values\n",
    "APP_by_id = APP_by_id.dropna()\n",
    "\n",
    "APP_by_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(2,2,figsize=(20,12))\n",
    "\n",
    "APP_by_id.boxplot(column=['A_32_P167000'], by='top_level_structure_name', ax=ax[0,0], grid=False, rot=90, figsize=(20,8))\n",
    "APP_by_id.boxplot(column=['A_24_P314159'], by='top_level_structure_name', ax=ax[0,1], grid=False, rot=90, figsize=(20,8))\n",
    "APP_by_id.boxplot(column=['CUST_84_PI416408490'], by='top_level_structure_name', ax=ax[1,0], grid=False, rot=90, figsize=(20,8))\n",
    "\n",
    "ax[0,0].set_ylabel('zscore expression')\n",
    "ax[0,1].set_ylabel('zscore expression')\n",
    "ax[1,0].set_ylabel('zscore expression')\n",
    "ax[1,1].set_visible(False)\n",
    "\n",
    "plt.suptitle('Boxplots of APP Probes by Z-score expression in Brain Regions')\n",
    "plt.subplots_adjust(hspace = 1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot for PSEN1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_23_P205686</th>\n",
       "      <th>A_23_P340728</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.2481</td>\n",
       "      <td>0.0476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.4923</td>\n",
       "      <td>-1.2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.7403</td>\n",
       "      <td>-0.7898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.4826</td>\n",
       "      <td>-0.8385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.4261</td>\n",
       "      <td>-1.0389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>0.8996</td>\n",
       "      <td>1.5231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>0.2341</td>\n",
       "      <td>0.6752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>1.1637</td>\n",
       "      <td>1.5391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white matter</th>\n",
       "      <td>2.8550</td>\n",
       "      <td>2.5623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulci &amp; spaces</th>\n",
       "      <td>0.3855</td>\n",
       "      <td>0.2265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>841 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          A_23_P205686  A_23_P340728\n",
       "top_level_structure_name                            \n",
       "frontal lobe                   -0.2481        0.0476\n",
       "frontal lobe                   -0.4923       -1.2025\n",
       "frontal lobe                   -0.7403       -0.7898\n",
       "frontal lobe                   -0.4826       -0.8385\n",
       "frontal lobe                   -0.4261       -1.0389\n",
       "...                                ...           ...\n",
       "myelencephalon                  0.8996        1.5231\n",
       "myelencephalon                  0.2341        0.6752\n",
       "myelencephalon                  1.1637        1.5391\n",
       "white matter                    2.8550        2.5623\n",
       "sulci & spaces                  0.3855        0.2265\n",
       "\n",
       "[841 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join the column for top_level_structure_names to the APP expression df\n",
    "PSEN1_by_id = PSEN1_exp_df.join(PSEN1_columns_df['top_level_structure_name'])\n",
    "# Set the index to the structure names\n",
    "PSEN1_by_id = PSEN1_by_id.set_index('top_level_structure_name')\n",
    "# Drop NaN values\n",
    "PSEN1_by_id = PSEN1_by_id.dropna()\n",
    "\n",
    "PSEN1_by_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2,figsize=(20,5))\n",
    "\n",
    "PSEN1_by_id.boxplot(column=['A_23_P205686'], by='top_level_structure_name', ax=ax[0], grid=False, rot=90)\n",
    "PSEN1_by_id.boxplot(column=['A_23_P340728'], by='top_level_structure_name', ax=ax[1], grid=False, rot=90)\n",
    "\n",
    "ax[0].set_ylabel('Z-score expression')\n",
    "ax[1].set_ylabel('Z-score expression')\n",
    "\n",
    "plt.suptitle('Boxplots of PSEN1 Probes by Z-score expression in Brain Regions')\n",
    "plt.subplots_adjust(hspace = 1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot for PSEN2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_23_P103398</th>\n",
       "      <th>A_23_P96985</th>\n",
       "      <th>A_24_P62521</th>\n",
       "      <th>CUST_16_PI416558187</th>\n",
       "      <th>CUST_526_PI416408490</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-1.3638</td>\n",
       "      <td>-3.0252</td>\n",
       "      <td>-1.0759</td>\n",
       "      <td>-1.0937</td>\n",
       "      <td>-0.4062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.7909</td>\n",
       "      <td>-0.8316</td>\n",
       "      <td>-0.1873</td>\n",
       "      <td>-0.8169</td>\n",
       "      <td>-0.0757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.9592</td>\n",
       "      <td>-1.7208</td>\n",
       "      <td>-1.7506</td>\n",
       "      <td>-0.3318</td>\n",
       "      <td>-0.4968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.8183</td>\n",
       "      <td>-0.5072</td>\n",
       "      <td>-0.4353</td>\n",
       "      <td>-0.9255</td>\n",
       "      <td>-0.2891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.6816</td>\n",
       "      <td>0.2668</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>-0.8937</td>\n",
       "      <td>0.0714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>0.4563</td>\n",
       "      <td>-0.6729</td>\n",
       "      <td>-0.3077</td>\n",
       "      <td>0.9550</td>\n",
       "      <td>-1.1787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>-0.3910</td>\n",
       "      <td>-0.9164</td>\n",
       "      <td>-0.5003</td>\n",
       "      <td>0.8147</td>\n",
       "      <td>-1.1331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>0.8279</td>\n",
       "      <td>0.3873</td>\n",
       "      <td>0.4523</td>\n",
       "      <td>0.8329</td>\n",
       "      <td>0.1227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white matter</th>\n",
       "      <td>-3.7593</td>\n",
       "      <td>-4.2725</td>\n",
       "      <td>-4.1642</td>\n",
       "      <td>0.4053</td>\n",
       "      <td>-4.1472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulci &amp; spaces</th>\n",
       "      <td>1.7010</td>\n",
       "      <td>0.0389</td>\n",
       "      <td>1.0126</td>\n",
       "      <td>1.3903</td>\n",
       "      <td>0.5149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>841 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          A_23_P103398  A_23_P96985  A_24_P62521  \\\n",
       "top_level_structure_name                                           \n",
       "frontal lobe                   -1.3638      -3.0252      -1.0759   \n",
       "frontal lobe                   -0.7909      -0.8316      -0.1873   \n",
       "frontal lobe                   -0.9592      -1.7208      -1.7506   \n",
       "frontal lobe                   -0.8183      -0.5072      -0.4353   \n",
       "frontal lobe                   -0.6816       0.2668       0.0074   \n",
       "...                                ...          ...          ...   \n",
       "myelencephalon                  0.4563      -0.6729      -0.3077   \n",
       "myelencephalon                 -0.3910      -0.9164      -0.5003   \n",
       "myelencephalon                  0.8279       0.3873       0.4523   \n",
       "white matter                   -3.7593      -4.2725      -4.1642   \n",
       "sulci & spaces                  1.7010       0.0389       1.0126   \n",
       "\n",
       "                          CUST_16_PI416558187  CUST_526_PI416408490  \n",
       "top_level_structure_name                                             \n",
       "frontal lobe                          -1.0937               -0.4062  \n",
       "frontal lobe                          -0.8169               -0.0757  \n",
       "frontal lobe                          -0.3318               -0.4968  \n",
       "frontal lobe                          -0.9255               -0.2891  \n",
       "frontal lobe                          -0.8937                0.0714  \n",
       "...                                       ...                   ...  \n",
       "myelencephalon                         0.9550               -1.1787  \n",
       "myelencephalon                         0.8147               -1.1331  \n",
       "myelencephalon                         0.8329                0.1227  \n",
       "white matter                           0.4053               -4.1472  \n",
       "sulci & spaces                         1.3903                0.5149  \n",
       "\n",
       "[841 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join the column for top_level_structure_names to the APP expression df\n",
    "PSEN2_by_id = PSEN2_exp_df.join(PSEN2_columns_df['top_level_structure_name'])\n",
    "# Set the index to the structure names\n",
    "PSEN2_by_id = PSEN2_by_id.set_index('top_level_structure_name')\n",
    "# Drop NaN values\n",
    "PSEN2_by_id = PSEN2_by_id.dropna()\n",
    "\n",
    "PSEN2_by_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(3,2,figsize=(20,20))\n",
    "\n",
    "PSEN2_by_id.boxplot(column=['A_23_P103398'], by='top_level_structure_name', ax=ax[0,0], grid=False, rot=90)\n",
    "PSEN2_by_id.boxplot(column=['A_23_P96985'], by='top_level_structure_name', ax=ax[0,1], grid=False, rot=90)\n",
    "PSEN2_by_id.boxplot(column=['A_24_P62521'], by='top_level_structure_name', ax=ax[1,0], grid=False, rot=90)\n",
    "PSEN2_by_id.boxplot(column=['CUST_16_PI416558187'], by='top_level_structure_name', ax=ax[1,1], grid=False, rot=90)\n",
    "PSEN2_by_id.boxplot(column=['CUST_526_PI416408490'], by='top_level_structure_name', ax=ax[2,0], grid=False, rot=90)\n",
    "\n",
    "\n",
    "ax[0,0].set_ylabel('Z-score expression')\n",
    "ax[0,1].set_ylabel('Z-score expression')\n",
    "ax[1,0].set_ylabel('Z-score expression')\n",
    "ax[1,1].set_ylabel('Z-score expression')\n",
    "ax[2,0].set_ylabel('Z-score expression')\n",
    "ax[2,1].set_visible(False)\n",
    "\n",
    "plt.suptitle('Boxplots of PSEN2 Probes by Z-score expression in Brain Regions')\n",
    "plt.subplots_adjust(hspace = 1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot for APOE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_23_P164650</th>\n",
       "      <th>A_24_P788772</th>\n",
       "      <th>A_32_P93036</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top_level_structure_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.7123</td>\n",
       "      <td>-0.4996</td>\n",
       "      <td>-1.9294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.5436</td>\n",
       "      <td>-0.1458</td>\n",
       "      <td>-0.4091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.9559</td>\n",
       "      <td>-0.7125</td>\n",
       "      <td>-1.1660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.8651</td>\n",
       "      <td>-0.5736</td>\n",
       "      <td>-1.3164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frontal lobe</th>\n",
       "      <td>-0.9899</td>\n",
       "      <td>-0.4632</td>\n",
       "      <td>-0.4214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>0.0459</td>\n",
       "      <td>-0.1131</td>\n",
       "      <td>-0.8141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>0.5112</td>\n",
       "      <td>0.4277</td>\n",
       "      <td>-0.6280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>myelencephalon</th>\n",
       "      <td>-0.4948</td>\n",
       "      <td>-0.3298</td>\n",
       "      <td>-0.8422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white matter</th>\n",
       "      <td>-0.4700</td>\n",
       "      <td>-0.6282</td>\n",
       "      <td>-0.7245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulci &amp; spaces</th>\n",
       "      <td>-2.0046</td>\n",
       "      <td>-2.1501</td>\n",
       "      <td>-1.5725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>841 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          A_23_P164650  A_24_P788772  A_32_P93036\n",
       "top_level_structure_name                                         \n",
       "frontal lobe                   -0.7123       -0.4996      -1.9294\n",
       "frontal lobe                   -0.5436       -0.1458      -0.4091\n",
       "frontal lobe                   -0.9559       -0.7125      -1.1660\n",
       "frontal lobe                   -0.8651       -0.5736      -1.3164\n",
       "frontal lobe                   -0.9899       -0.4632      -0.4214\n",
       "...                                ...           ...          ...\n",
       "myelencephalon                  0.0459       -0.1131      -0.8141\n",
       "myelencephalon                  0.5112        0.4277      -0.6280\n",
       "myelencephalon                 -0.4948       -0.3298      -0.8422\n",
       "white matter                   -0.4700       -0.6282      -0.7245\n",
       "sulci & spaces                 -2.0046       -2.1501      -1.5725\n",
       "\n",
       "[841 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join the column for top_level_structure_names to the APP expression df\n",
    "APOE_by_id = APOE_exp_df.join(APOE_columns_df['top_level_structure_name'])\n",
    "# Set the index to the structure names\n",
    "APOE_by_id = APOE_by_id.set_index('top_level_structure_name')\n",
    "# Drop NaN values\n",
    "APOE_by_id = APOE_by_id.dropna()\n",
    "\n",
    "APOE_by_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(2,2,figsize=(20,12))\n",
    "\n",
    "APOE_by_id.boxplot(column=['A_23_P164650'], by='top_level_structure_name', ax=ax[0,0], grid=False, rot=90)\n",
    "APOE_by_id.boxplot(column=['A_24_P788772'], by='top_level_structure_name', ax=ax[0,1], grid=False, rot=90)\n",
    "APOE_by_id.boxplot(column=['A_32_P93036'], by='top_level_structure_name', ax=ax[1,0], grid=False, rot=90)\n",
    "\n",
    "ax[0,0].set_ylabel('Z-score expression')\n",
    "ax[0,1].set_ylabel('Z-score expression')\n",
    "ax[1,0].set_ylabel('Z-score expression')\n",
    "ax[1,1].set_visible(False)\n",
    "\n",
    "plt.suptitle('Boxplots of APOE Probes by Z-score expression in Brain Regions')\n",
    "plt.subplots_adjust(hspace = 1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling: RNA Seq of Cell Types: Human M1 - 10X GENOMICS (2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gene Expression in Different Cell Types DataSet within Primary Motor Cortex (M1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will wrangle data for the second dataset from the RNA seq of Cell Types: Human M1 - 10X GENOMICS (2020) https://portal.brain-map.org/atlases-and-data/rnaseq. We will be looking at four genes: APP, APOE, PSEN1, and PSEN2 and their gene expression levels across different cell types in M1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Upload, open, and read metadata.csv (from the RNA Seq Human M1) file in order for code to work.\n",
    "    - This file contains Metadata on cells. Each entry has an _order, _label, and _color column.\n",
    "2. Upload, open, and read trimmed_means.csv (from the RNA Seq Human M1) file in order for code to work.\n",
    "    - This file contain Gene expression aggregated per cell type, calculated as trimmed means. Each row is a cell type cluster and each column is a gene containing the calculated trimmed means.\n",
    "\n",
    "We want to convert our csv files into dataframes that we can work with.\n",
    "\n",
    "**note you need to download the metadata.csv and trimmed_means.csv files from the RNA Seq of Cell Types from the Allen Brain Observatory and upload to your jupyter notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.\n",
    "opened_file = open('metadata.csv')\n",
    "read_file = reader(opened_file, delimiter = '\\t')\n",
    "meta_data = list(read_file)\n",
    "\n",
    "#2.\n",
    "opened_file = open('trimmed_means.csv')\n",
    "read_file = reader(opened_file, delimiter = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Turning MetaData and Trimmed means into DataFrames "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Turn metadata into a dataframe\n",
    "2. Narrow down meta_data to only two columns: <font color=red>'cluster_label and class_label'</font> and save it as a variable **new_metadata_df**. This will tell us which class label (Non-neuronal, GABAergic, Glutamatergic) each cell type belongs to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster_label</th>\n",
       "      <th>class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inh L1-2 SST CCNJL</td>\n",
       "      <td>GABAergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Exc L5-6 FEZF2 IFNG-AS1</td>\n",
       "      <td>Glutamatergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Exc L3-5 RORB LINC01202</td>\n",
       "      <td>Glutamatergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Exc L2 LINC00507 GLRA3</td>\n",
       "      <td>Glutamatergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oligo L2-6 OPALIN FTH1P3</td>\n",
       "      <td>Non-Neuronal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76528</th>\n",
       "      <td>Oligo L2-6 OPALIN FTH1P3</td>\n",
       "      <td>Non-Neuronal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76529</th>\n",
       "      <td>Exc L3-5 RORB LNX2</td>\n",
       "      <td>Glutamatergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76530</th>\n",
       "      <td>Exc L2-3 RORB PTPN3</td>\n",
       "      <td>Glutamatergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76531</th>\n",
       "      <td>Oligo L2-6 OPALIN FTH1P3</td>\n",
       "      <td>Non-Neuronal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76532</th>\n",
       "      <td>Inh L2-5 PVALB RPH3AL</td>\n",
       "      <td>GABAergic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76533 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  cluster_label    class_label\n",
       "0            Inh L1-2 SST CCNJL      GABAergic\n",
       "1       Exc L5-6 FEZF2 IFNG-AS1  Glutamatergic\n",
       "2       Exc L3-5 RORB LINC01202  Glutamatergic\n",
       "3        Exc L2 LINC00507 GLRA3  Glutamatergic\n",
       "4      Oligo L2-6 OPALIN FTH1P3   Non-Neuronal\n",
       "...                         ...            ...\n",
       "76528  Oligo L2-6 OPALIN FTH1P3   Non-Neuronal\n",
       "76529        Exc L3-5 RORB LNX2  Glutamatergic\n",
       "76530       Exc L2-3 RORB PTPN3  Glutamatergic\n",
       "76531  Oligo L2-6 OPALIN FTH1P3   Non-Neuronal\n",
       "76532     Inh L2-5 PVALB RPH3AL      GABAergic\n",
       "\n",
       "[76533 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. \n",
    "metadata_df = pd.read_csv('metadata.csv')\n",
    "#2.\n",
    "new_metadata_df = metadata_df[['cluster_label','class_label']]\n",
    "new_metadata_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turn trimmed_means into a dataframe and save it as a variable, **trimmed_means_df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "trimmed_means_df = pd.read_csv('trimmed_means.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Narrow down dataframe to only inlcude <font color=red>'cluster_label' , 'APP', 'APOE', 'PSEN1', 'PSEN2'</font> and save it as a varibale **gene_means**.\n",
    "Those columns are the ones that we are interested in exploring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster_label</th>\n",
       "      <th>APP</th>\n",
       "      <th>PSEN1</th>\n",
       "      <th>PSEN2</th>\n",
       "      <th>APOE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inh L1 PAX6 CHRFAM7A</td>\n",
       "      <td>9.809140</td>\n",
       "      <td>1.241007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Inh L1-3 VIP CBLN1</td>\n",
       "      <td>9.160936</td>\n",
       "      <td>0.993764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Exc L3 LAMP5 CARM1P1</td>\n",
       "      <td>9.200633</td>\n",
       "      <td>2.929186</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inh L1-3 SST FAM20A</td>\n",
       "      <td>9.154916</td>\n",
       "      <td>0.653703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inh L1-6 LAMP5 AARD</td>\n",
       "      <td>9.386059</td>\n",
       "      <td>2.246162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Exc L5-6 FEZF2 OR1L8</td>\n",
       "      <td>9.344962</td>\n",
       "      <td>5.457576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Exc L2 LINC00507 GLRA3</td>\n",
       "      <td>8.919819</td>\n",
       "      <td>2.507005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Inh L3-5 SST OR5AH1P</td>\n",
       "      <td>9.287355</td>\n",
       "      <td>1.859061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Exc L2-3 RORB RTKN2</td>\n",
       "      <td>9.127199</td>\n",
       "      <td>2.896370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Exc L5 RORB MED8</td>\n",
       "      <td>8.968805</td>\n",
       "      <td>2.615302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              cluster_label       APP     PSEN1  PSEN2  APOE\n",
       "0      Inh L1 PAX6 CHRFAM7A  9.809140  1.241007    0.0   0.0\n",
       "1        Inh L1-3 VIP CBLN1  9.160936  0.993764    0.0   0.0\n",
       "2      Exc L3 LAMP5 CARM1P1  9.200633  2.929186    0.0   0.0\n",
       "3       Inh L1-3 SST FAM20A  9.154916  0.653703    0.0   0.0\n",
       "4       Inh L1-6 LAMP5 AARD  9.386059  2.246162    0.0   0.0\n",
       "..                      ...       ...       ...    ...   ...\n",
       "122    Exc L5-6 FEZF2 OR1L8  9.344962  5.457576    0.0   0.0\n",
       "123  Exc L2 LINC00507 GLRA3  8.919819  2.507005    0.0   0.0\n",
       "124    Inh L3-5 SST OR5AH1P  9.287355  1.859061    0.0   0.0\n",
       "125     Exc L2-3 RORB RTKN2  9.127199  2.896370    0.0   0.0\n",
       "126        Exc L5 RORB MED8  8.968805  2.615302    0.0   0.0\n",
       "\n",
       "[127 rows x 5 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_means= trimmed_means_df[['cluster_label','APP','PSEN1','PSEN2','APOE']]\n",
    "gene_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Join together **new_metadata_df** and **gene_means** so that the column, <font color=red>'class_label'</font> , is included in the dataframe and save it to a variable called **new_df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster_label</th>\n",
       "      <th>APP</th>\n",
       "      <th>PSEN1</th>\n",
       "      <th>PSEN2</th>\n",
       "      <th>APOE</th>\n",
       "      <th>class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inh L1 PAX6 CHRFAM7A</td>\n",
       "      <td>9.809140</td>\n",
       "      <td>1.241007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GABAergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Inh L1-3 VIP CBLN1</td>\n",
       "      <td>9.160936</td>\n",
       "      <td>0.993764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Glutamatergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Exc L3 LAMP5 CARM1P1</td>\n",
       "      <td>9.200633</td>\n",
       "      <td>2.929186</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Glutamatergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inh L1-3 SST FAM20A</td>\n",
       "      <td>9.154916</td>\n",
       "      <td>0.653703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Glutamatergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inh L1-6 LAMP5 AARD</td>\n",
       "      <td>9.386059</td>\n",
       "      <td>2.246162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Non-Neuronal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Exc L5-6 FEZF2 OR1L8</td>\n",
       "      <td>9.344962</td>\n",
       "      <td>5.457576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Glutamatergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Exc L2 LINC00507 GLRA3</td>\n",
       "      <td>8.919819</td>\n",
       "      <td>2.507005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GABAergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Inh L3-5 SST OR5AH1P</td>\n",
       "      <td>9.287355</td>\n",
       "      <td>1.859061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Glutamatergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Exc L2-3 RORB RTKN2</td>\n",
       "      <td>9.127199</td>\n",
       "      <td>2.896370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Glutamatergic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Exc L5 RORB MED8</td>\n",
       "      <td>8.968805</td>\n",
       "      <td>2.615302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Non-Neuronal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              cluster_label       APP     PSEN1  PSEN2  APOE    class_label\n",
       "0      Inh L1 PAX6 CHRFAM7A  9.809140  1.241007    0.0   0.0      GABAergic\n",
       "1        Inh L1-3 VIP CBLN1  9.160936  0.993764    0.0   0.0  Glutamatergic\n",
       "2      Exc L3 LAMP5 CARM1P1  9.200633  2.929186    0.0   0.0  Glutamatergic\n",
       "3       Inh L1-3 SST FAM20A  9.154916  0.653703    0.0   0.0  Glutamatergic\n",
       "4       Inh L1-6 LAMP5 AARD  9.386059  2.246162    0.0   0.0   Non-Neuronal\n",
       "..                      ...       ...       ...    ...   ...            ...\n",
       "122    Exc L5-6 FEZF2 OR1L8  9.344962  5.457576    0.0   0.0  Glutamatergic\n",
       "123  Exc L2 LINC00507 GLRA3  8.919819  2.507005    0.0   0.0      GABAergic\n",
       "124    Inh L3-5 SST OR5AH1P  9.287355  1.859061    0.0   0.0  Glutamatergic\n",
       "125     Exc L2-3 RORB RTKN2  9.127199  2.896370    0.0   0.0  Glutamatergic\n",
       "126        Exc L5 RORB MED8  8.968805  2.615302    0.0   0.0   Non-Neuronal\n",
       "\n",
       "[127 rows x 6 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = gene_means.join(new_metadata_df['class_label'])\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting Boxplot of Gene expression of different cell type relative to APP, PSEN1, PSEN2, APOE in the Primary Motor Cortex. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Genes')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Define figure\n",
    "fig,ax = plt.subplots(figsize=(20,5))\n",
    "\n",
    "APP_gene = trimmed_means_df['APP']\n",
    "PSEN1_gene = trimmed_means_df['PSEN1']\n",
    "PSEN2_gene = trimmed_means_df['PSEN2']\n",
    "APOE_gene = trimmed_means_df['APOE']\n",
    "\n",
    "genedata = (APP_gene,PSEN1_gene,PSEN2_gene,APOE_gene)\n",
    "plt.boxplot(genedata)\n",
    "\n",
    "ax.set_xticklabels(['APP', 'PSEN1','PSEN2', 'APOE'])\n",
    "plt.ylabel('Trimmed means of gene expression per cell type')\n",
    "plt.title('Trimmed means of Gene Expression of different Genes')\n",
    "plt.xlabel('Genes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WIthin the primary motor cortex, it shows that the APP gene and PSEN1 gene show relatively high levels of gene expression. This could indicate that these genes could play a major role in the onset of AD. PSEN2 had no expression and APOE does have some outliers that indicate gene expression. However, this does not mean these PSEN2 and APOE does not have a role in the onset of AD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heatmap of Gene Expression in Cell cluster by class label and class label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Turn data into a pivot table \n",
    "heatmap1_data = pd.pivot_table(new_df, values = ('APP','PSEN1','PSEN2','APOE'),\n",
    "                     index=['cluster_label'], columns = 'class_label')\n",
    "#plot heatmap \n",
    "plt.figure(figsize=(10, 12))\n",
    "plt.title('Heat Map of Gene expression in Cell Types')\n",
    "sns.heatmap(heatmap1_data, cmap=\"RdYlBu_r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within the primary motor cortex, it shows that Glutamergic and GABAergic of PSEN1 and APP show high levels of gene expression throughout different cell types. While non-nueronal (glial cells) do not have lots of different cell types that showed differential levels of gene expression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of Contents\n",
    "1. [APP Gene](#one)\n",
    "2. [PSEN1 Gene](#two)\n",
    "3. [PSEN2 Gene](#three)\n",
    "4. [APOE Gene](#four)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"one\"></a>\n",
    "\n",
    "## APP Gene"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Scatter plots for APP Expression Level vs. Age in 5 Brain Regions**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['24 years' '39 years' '57 years' '31 years' '49 years' '55 years']\n",
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rs_APP_FL=0.1178797264327377 ps_APP_FL=0.8239994168767328\n",
      "rs_APP_HF=-0.2864908125127584 ps_APP_HF=0.5820209323852\n",
      "rs_APP_PL=0.12972090266628766 ps_APP_PL=0.8065100860618768\n",
      "rs_APP_TL=0.2744536979381532 ps_APP_TL=0.5986560424800597\n",
      "rs_APP_OL=-0.00997434746049698 ps_APP_OL=0.9850389749712339\n"
     ]
    }
   ],
   "source": [
    "#Obtain the age list from the dataframe\n",
    "Age_list = APP_age_region_df['donor_age'].unique() \n",
    "\n",
    "print(APP_age_region_df['donor_age'].unique())\n",
    "\n",
    "print(len(APP_age_region_df['donor_age'].unique()))\n",
    "\n",
    "#Convert the age list into a float list without changing the order of original list for correlation analysis\n",
    "Age_float_list = [24.,39.,57.,31.,49.,55.] \n",
    "\n",
    "#Define the list of brain regions that are most affected in AD\n",
    "Brain_Area_list = ['frontal lobe','hippocampal formation','parietal lobe','temporal lobe','occipital lobe']\n",
    "\n",
    "APP_Mean_Expression_list_frontal_lobe = []\n",
    "APP_Mean_Expression_list_hippocampal_formation = []\n",
    "APP_Mean_Expression_list_parietal_lobe = []\n",
    "APP_Mean_Expression_list_temporal_lobe = []\n",
    "APP_Mean_Expression_list_occipital_lobe = []\n",
    "\n",
    "for age in Age_list:\n",
    "    APP_age_frontal_lobe_df =APP_age_region_df[APP_age_region_df['top_level_structure_name']=='frontal lobe'] \n",
    "    APP_donor_age_frontal_lobe_df = APP_age_frontal_lobe_df[APP_age_frontal_lobe_df['donor_age']==age]\n",
    "    #calculate the mean of each probe column first\n",
    "    m1 = APP_donor_age_frontal_lobe_df['A_32_P167000'].mean()\n",
    "    m2 = APP_donor_age_frontal_lobe_df['A_24_P314159'].mean()\n",
    "    m3 = APP_donor_age_frontal_lobe_df['CUST_84_PI416408490'].mean()\n",
    "    #take the average of all three column means to obtain the overall average expression level for each donor \n",
    "    APP_Mean_Expression_list_frontal_lobe.append(1/3*(m1+m2+m3))\n",
    "    \n",
    "for age in Age_list:\n",
    "    APP_age_hippocampal_formation_df =APP_age_region_df[APP_age_region_df['top_level_structure_name']=='hippocampal formation'] \n",
    "    APP_donor_age_hippocampal_formation_df = APP_age_hippocampal_formation_df[APP_age_hippocampal_formation_df['donor_age']==age]\n",
    "    m1 = APP_donor_age_hippocampal_formation_df['A_32_P167000'].mean()\n",
    "    m2 = APP_donor_age_hippocampal_formation_df['A_24_P314159'].mean()\n",
    "    m3 = APP_donor_age_hippocampal_formation_df['CUST_84_PI416408490'].mean()\n",
    "    APP_Mean_Expression_list_hippocampal_formation.append(1/3*(m1+m2+m3))\n",
    "                                                          \n",
    "for age in Age_list:\n",
    "    APP_age_parietal_lobe_df =APP_age_region_df[APP_age_region_df['top_level_structure_name']=='parietal lobe'] \n",
    "    APP_donor_age_parietal_lobe_df = APP_age_parietal_lobe_df[APP_age_parietal_lobe_df['donor_age']==age]\n",
    "    m1 = APP_donor_age_parietal_lobe_df['A_32_P167000'].mean()\n",
    "    m2 = APP_donor_age_parietal_lobe_df['A_24_P314159'].mean()\n",
    "    m3 = APP_donor_age_parietal_lobe_df['CUST_84_PI416408490'].mean()\n",
    "    APP_Mean_Expression_list_parietal_lobe.append(1/3*(m1+m2+m3))    \n",
    "                                                          \n",
    "for age in Age_list:\n",
    "    APP_age_temporal_lobe_df =APP_age_region_df[APP_age_region_df['top_level_structure_name']=='temporal lobe'] \n",
    "    APP_donor_age_temporal_lobe_df = APP_age_temporal_lobe_df[APP_age_temporal_lobe_df['donor_age']==age]\n",
    "    m1 = APP_donor_age_temporal_lobe_df['A_32_P167000'].mean()\n",
    "    m2 = APP_donor_age_temporal_lobe_df['A_24_P314159'].mean()\n",
    "    m3 = APP_donor_age_temporal_lobe_df['CUST_84_PI416408490'].mean()\n",
    "    APP_Mean_Expression_list_temporal_lobe.append(1/3*(m1+m2+m3)) \n",
    "\n",
    "for age in Age_list:\n",
    "    APP_age_occipital_lobe_df =APP_age_region_df[APP_age_region_df['top_level_structure_name']=='occipital lobe'] \n",
    "    APP_donor_age_occipital_lobe_df = APP_age_occipital_lobe_df[APP_age_occipital_lobe_df['donor_age']==age]\n",
    "    m1 = APP_donor_age_occipital_lobe_df['A_32_P167000'].mean()\n",
    "    m2 = APP_donor_age_occipital_lobe_df['A_24_P314159'].mean()\n",
    "    m3 = APP_donor_age_occipital_lobe_df['CUST_84_PI416408490'].mean()\n",
    "    APP_Mean_Expression_list_occipital_lobe.append(1/3*(m1+m2+m3))  \n",
    "                                                          \n",
    "\n",
    "fig,ax = plt.subplots(1,5,figsize=(20,5)) \n",
    "                                                          \n",
    "ax[0].scatter(Age_float_list, APP_Mean_Expression_list_frontal_lobe)\n",
    "ax[1].scatter(Age_float_list, APP_Mean_Expression_list_hippocampal_formation)\n",
    "ax[2].scatter(Age_float_list, APP_Mean_Expression_list_parietal_lobe)\n",
    "ax[3].scatter(Age_float_list, APP_Mean_Expression_list_temporal_lobe)\n",
    "ax[4].scatter(Age_float_list, APP_Mean_Expression_list_occipital_lobe) \n",
    "\n",
    "ax[0].set_xlabel('Age of Donor')\n",
    "ax[1].set_xlabel('Age of Donor')\n",
    "ax[2].set_xlabel('Age of Donor')\n",
    "ax[3].set_xlabel('Age of Donor')\n",
    "ax[4].set_xlabel('Age of Donor')\n",
    "\n",
    "ax[0].set_ylabel('APP Expression Level in Frontal Lobe')\n",
    "ax[1].set_ylabel('APP Expression Level in Hippocampal Formation')\n",
    "ax[2].set_ylabel('APP Expression Level in Parietal Lobe')\n",
    "ax[3].set_ylabel('APP Expression Level in Temporal Lobe')\n",
    "ax[4].set_ylabel('APP Expression Level in Occipital Lobe')\n",
    "\n",
    "plt.suptitle('APP Expression Level in Brain Regions vs. Age')\n",
    "plt.subplots_adjust(wspace = 1)\n",
    "plt.show()\n",
    "\n",
    "rs_APP_FL,ps_APP_FL = stats.stats.pearsonr(Age_float_list,APP_Mean_Expression_list_frontal_lobe)\n",
    "rs_APP_HF,ps_APP_HF = stats.stats.pearsonr(Age_float_list,APP_Mean_Expression_list_hippocampal_formation)\n",
    "rs_APP_PL,ps_APP_PL = stats.stats.pearsonr(Age_float_list,APP_Mean_Expression_list_parietal_lobe)\n",
    "rs_APP_TL,ps_APP_TL = stats.stats.pearsonr(Age_float_list,APP_Mean_Expression_list_temporal_lobe)\n",
    "rs_APP_OL,ps_APP_OL = stats.stats.pearsonr(Age_float_list,APP_Mean_Expression_list_occipital_lobe)\n",
    "\n",
    "print('rs_APP_FL='+str(rs_APP_FL),'ps_APP_FL='+str(ps_APP_FL))\n",
    "print('rs_APP_HF='+str(rs_APP_HF),'ps_APP_HF='+str(ps_APP_HF))\n",
    "print('rs_APP_PL='+str(rs_APP_PL),'ps_APP_PL='+str(ps_APP_PL))\n",
    "print('rs_APP_TL='+str(rs_APP_TL),'ps_APP_TL='+str(ps_APP_TL))\n",
    "print('rs_APP_OL='+str(rs_APP_OL),'ps_APP_OL='+str(ps_APP_OL))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"two\"></a>\n",
    "\n",
    "## PSEN1 Gene\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Scatter plots for PSEN1 Expression Level vs. Age in 5 Brain Regions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rs_PSEN1_FL=0.8476491799216823 ps_APP_FL=0.033048068464543334\n",
      "rs_PSEN1_HF=-0.3257352092104462 ps_APP_HF=0.5286779971019472\n",
      "rs_PSEN1_PL=0.5192627091685201 ps_APP_PL=0.29111131489183745\n",
      "rs_PSEN1_TL=0.9508787775936004 ps_APP_TL=0.003560079572588061\n",
      "rs_PSEN1_OL=0.5746944611003727 ps_APP_OL=0.23286154815498694\n"
     ]
    }
   ],
   "source": [
    "PSEN1_Mean_Expression_list_frontal_lobe = []\n",
    "PSEN1_Mean_Expression_list_hippocampal_formation = []\n",
    "PSEN1_Mean_Expression_list_parietal_lobe = []\n",
    "PSEN1_Mean_Expression_list_temporal_lobe = []\n",
    "PSEN1_Mean_Expression_list_occipital_lobe = []\n",
    "\n",
    "for age in Age_list:\n",
    "    PSEN1_age_frontal_lobe_df =PSEN1_age_region_df[PSEN1_age_region_df['top_level_structure_name']=='frontal lobe'] \n",
    "    PSEN1_donor_age_frontal_lobe_df = PSEN1_age_frontal_lobe_df[PSEN1_age_frontal_lobe_df['donor_age']==age]\n",
    "    #calculate the mean of each probe column first\n",
    "    m1 = PSEN1_donor_age_frontal_lobe_df['A_23_P205686'].mean()\n",
    "    m2 = PSEN1_donor_age_frontal_lobe_df['A_23_P340728'].mean()\n",
    "    #take the average of all three column means to obtain the overall average expression level for each donor \n",
    "    PSEN1_Mean_Expression_list_frontal_lobe.append(1/2*(m1+m2))\n",
    "    \n",
    "for age in Age_list:\n",
    "    PSEN1_age_hippocampal_formation_df =PSEN1_age_region_df[PSEN1_age_region_df['top_level_structure_name']=='hippocampal formation'] \n",
    "    PSEN1_donor_age_hippocampal_formation_df = PSEN1_age_hippocampal_formation_df[PSEN1_age_hippocampal_formation_df['donor_age']==age]\n",
    "    m1 = PSEN1_donor_age_hippocampal_formation_df['A_23_P205686'].mean()\n",
    "    m2 = PSEN1_donor_age_hippocampal_formation_df['A_23_P340728'].mean()\n",
    "    PSEN1_Mean_Expression_list_hippocampal_formation.append(1/2*(m1+m2))\n",
    "                                                          \n",
    "for age in Age_list:\n",
    "    PSEN1_age_parietal_lobe_df =PSEN1_age_region_df[PSEN1_age_region_df['top_level_structure_name']=='parietal lobe'] \n",
    "    PSEN1_donor_age_parietal_lobe_df = PSEN1_age_parietal_lobe_df[PSEN1_age_parietal_lobe_df['donor_age']==age]\n",
    "    m1 = PSEN1_donor_age_parietal_lobe_df['A_23_P205686'].mean()\n",
    "    m2 = PSEN1_donor_age_parietal_lobe_df['A_23_P340728'].mean()\n",
    "    PSEN1_Mean_Expression_list_parietal_lobe.append(1/2*(m1+m2))    \n",
    "                                                          \n",
    "for age in Age_list:\n",
    "    PSEN1_age_temporal_lobe_df =PSEN1_age_region_df[PSEN1_age_region_df['top_level_structure_name']=='temporal lobe'] \n",
    "    PSEN1_donor_age_temporal_lobe_df = PSEN1_age_temporal_lobe_df[PSEN1_age_temporal_lobe_df['donor_age']==age]\n",
    "    m1 = PSEN1_donor_age_temporal_lobe_df['A_23_P205686'].mean()\n",
    "    m2 = PSEN1_donor_age_temporal_lobe_df['A_23_P340728'].mean()\n",
    "    PSEN1_Mean_Expression_list_temporal_lobe.append(1/2*(m1+m2)) \n",
    "\n",
    "for age in Age_list:\n",
    "    PSEN1_age_occipital_lobe_df =PSEN1_age_region_df[PSEN1_age_region_df['top_level_structure_name']=='occipital lobe'] \n",
    "    PSEN1_donor_age_occipital_lobe_df = PSEN1_age_occipital_lobe_df[PSEN1_age_occipital_lobe_df['donor_age']==age]\n",
    "    m1 = PSEN1_donor_age_occipital_lobe_df['A_23_P205686'].mean()\n",
    "    m2 = PSEN1_donor_age_occipital_lobe_df['A_23_P340728'].mean()\n",
    "    PSEN1_Mean_Expression_list_occipital_lobe.append(1/2*(m1+m2))  \n",
    "                                                          \n",
    "\n",
    "fig,ax = plt.subplots(1,5,figsize=(20,5)) \n",
    "                                                          \n",
    "ax[0].scatter(Age_float_list, PSEN1_Mean_Expression_list_frontal_lobe)\n",
    "ax[1].scatter(Age_float_list, PSEN1_Mean_Expression_list_hippocampal_formation)\n",
    "ax[2].scatter(Age_float_list, PSEN1_Mean_Expression_list_parietal_lobe)\n",
    "ax[3].scatter(Age_float_list, PSEN1_Mean_Expression_list_temporal_lobe)\n",
    "ax[4].scatter(Age_float_list, PSEN1_Mean_Expression_list_occipital_lobe) \n",
    "\n",
    "ax[0].set_xlabel('Age of Donor')\n",
    "ax[1].set_xlabel('Age of Donor')\n",
    "ax[2].set_xlabel('Age of Donor')\n",
    "ax[3].set_xlabel('Age of Donor')\n",
    "ax[4].set_xlabel('Age of Donor')\n",
    "\n",
    "ax[0].set_ylabel('PSEN1 Expression Level in Frontal Lobe')\n",
    "ax[1].set_ylabel('PSEN1 Expression Level in Hippocampal Formation')\n",
    "ax[2].set_ylabel('PSEN1 Expression Level in Parietal Lobe')\n",
    "ax[3].set_ylabel('PSEN1 Expression Level in Temporal Lobe')\n",
    "ax[4].set_ylabel('PSEN1 Expression Level in Occipital Lobe')\n",
    "\n",
    "plt.suptitle('PSEN1 Expression Level in Brain Regions vs. Age')\n",
    "plt.subplots_adjust(wspace = 1)\n",
    "plt.show()\n",
    "\n",
    "rs_PSEN1_FL,ps_PSEN1_FL = stats.stats.pearsonr(Age_float_list,PSEN1_Mean_Expression_list_frontal_lobe)\n",
    "rs_PSEN1_HF,ps_PSEN1_HF = stats.stats.pearsonr(Age_float_list,PSEN1_Mean_Expression_list_hippocampal_formation)\n",
    "rs_PSEN1_PL,ps_PSEN1_PL = stats.stats.pearsonr(Age_float_list,PSEN1_Mean_Expression_list_parietal_lobe)\n",
    "rs_PSEN1_TL,ps_PSEN1_TL = stats.stats.pearsonr(Age_float_list,PSEN1_Mean_Expression_list_temporal_lobe)\n",
    "rs_PSEN1_OL,ps_PSEN1_OL = stats.stats.pearsonr(Age_float_list,PSEN1_Mean_Expression_list_occipital_lobe)\n",
    "\n",
    "print('rs_PSEN1_FL='+str(rs_PSEN1_FL),'ps_APP_FL='+str(ps_PSEN1_FL))\n",
    "print('rs_PSEN1_HF='+str(rs_PSEN1_HF),'ps_APP_HF='+str(ps_PSEN1_HF))\n",
    "print('rs_PSEN1_PL='+str(rs_PSEN1_PL),'ps_APP_PL='+str(ps_PSEN1_PL))\n",
    "print('rs_PSEN1_TL='+str(rs_PSEN1_TL),'ps_APP_TL='+str(ps_PSEN1_TL))\n",
    "print('rs_PSEN1_OL='+str(rs_PSEN1_OL),'ps_APP_OL='+str(ps_PSEN1_OL))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"three\"></a>\n",
    "\n",
    "## PSEN2 Gene\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Scatter plots for PSEN2 Expression Level vs. Age in 5 Brain Regions**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rs_PSEN2_FL=0.5175593668299068 ps_APP_FL=0.2929796678907134\n",
      "rs_PSEN2_HF=-0.5876259703098415 ps_APP_HF=0.22001592611750392\n",
      "rs_PSEN2_PL=0.48233728403733706 ps_APP_PL=0.332601778977072\n",
      "rs_PSEN2_TL=0.46038532476385347 ps_APP_TL=0.3582124174107423\n",
      "rs_PSEN2_OL=0.6776348234701106 ps_APP_OL=0.13912897767945095\n"
     ]
    }
   ],
   "source": [
    "PSEN2_Mean_Expression_list_frontal_lobe = []\n",
    "PSEN2_Mean_Expression_list_hippocampal_formation = []\n",
    "PSEN2_Mean_Expression_list_parietal_lobe = []\n",
    "PSEN2_Mean_Expression_list_temporal_lobe = []\n",
    "PSEN2_Mean_Expression_list_occipital_lobe = []\n",
    "\n",
    "for age in Age_list:\n",
    "    PSEN2_age_frontal_lobe_df =PSEN2_age_region_df[PSEN2_age_region_df['top_level_structure_name']=='frontal lobe'] \n",
    "    PSEN2_donor_age_frontal_lobe_df = PSEN2_age_frontal_lobe_df[PSEN2_age_frontal_lobe_df['donor_age']==age]\n",
    "    #calculate the mean of each probe column first\n",
    "    m1 = PSEN2_donor_age_frontal_lobe_df['A_23_P103398'].mean()\n",
    "    m2 = PSEN2_donor_age_frontal_lobe_df['A_23_P96985'].mean()\n",
    "    m3 = PSEN2_donor_age_frontal_lobe_df['A_24_P62521'].mean()\n",
    "    m4 = PSEN2_donor_age_frontal_lobe_df['CUST_16_PI416558187'].mean()\n",
    "    m5 = PSEN2_donor_age_frontal_lobe_df['CUST_526_PI416408490'].mean()\n",
    "    #take the average of all three column means to obtain the overall average expression level for each donor \n",
    "    PSEN2_Mean_Expression_list_frontal_lobe.append(1/5*(m1+m2+m3+m4+m5))\n",
    "    \n",
    "for age in Age_list:\n",
    "    PSEN2_age_hippocampal_formation_df =PSEN2_age_region_df[PSEN2_age_region_df['top_level_structure_name']=='hippocampal formation'] \n",
    "    PSEN2_donor_age_hippocampal_formation_df = PSEN2_age_hippocampal_formation_df[PSEN2_age_hippocampal_formation_df['donor_age']==age]\n",
    "    m1 = PSEN2_donor_age_hippocampal_formation_df['A_23_P103398'].mean()\n",
    "    m2 = PSEN2_donor_age_hippocampal_formation_df['A_23_P96985'].mean()\n",
    "    m3 = PSEN2_donor_age_hippocampal_formation_df['A_24_P62521'].mean()\n",
    "    m4 = PSEN2_donor_age_hippocampal_formation_df['CUST_16_PI416558187'].mean()\n",
    "    m5 = PSEN2_donor_age_hippocampal_formation_df['CUST_526_PI416408490'].mean()\n",
    "    PSEN2_Mean_Expression_list_hippocampal_formation.append(1/5*(m1+m2+m3+m4+m5))\n",
    "                                                          \n",
    "for age in Age_list:\n",
    "    PSEN2_age_parietal_lobe_df =PSEN2_age_region_df[PSEN2_age_region_df['top_level_structure_name']=='parietal lobe'] \n",
    "    PSEN2_donor_age_parietal_lobe_df = PSEN2_age_parietal_lobe_df[PSEN2_age_parietal_lobe_df['donor_age']==age]\n",
    "    m1 = PSEN2_donor_age_parietal_lobe_df['A_23_P103398'].mean()\n",
    "    m2 = PSEN2_donor_age_parietal_lobe_df['A_23_P96985'].mean()\n",
    "    m3 = PSEN2_donor_age_parietal_lobe_df['A_24_P62521'].mean()\n",
    "    m4 = PSEN2_donor_age_parietal_lobe_df['CUST_16_PI416558187'].mean()\n",
    "    m5 = PSEN2_donor_age_parietal_lobe_df['CUST_526_PI416408490'].mean()\n",
    "    PSEN2_Mean_Expression_list_parietal_lobe.append(1/5*(m1+m2+m3+m4+m5))    \n",
    "                                                          \n",
    "for age in Age_list:\n",
    "    PSEN2_age_temporal_lobe_df =PSEN2_age_region_df[PSEN2_age_region_df['top_level_structure_name']=='temporal lobe'] \n",
    "    PSEN2_donor_age_temporal_lobe_df = PSEN2_age_temporal_lobe_df[PSEN2_age_temporal_lobe_df['donor_age']==age]\n",
    "    m1 = PSEN2_donor_age_temporal_lobe_df['A_23_P103398'].mean()\n",
    "    m2 = PSEN2_donor_age_temporal_lobe_df['A_23_P96985'].mean()\n",
    "    m3 = PSEN2_donor_age_temporal_lobe_df['A_24_P62521'].mean()\n",
    "    m4 = PSEN2_donor_age_temporal_lobe_df['CUST_16_PI416558187'].mean()\n",
    "    m5 = PSEN2_donor_age_temporal_lobe_df['CUST_526_PI416408490'].mean()\n",
    "    PSEN2_Mean_Expression_list_temporal_lobe.append(1/5*(m1+m2+m3+m4+m5)) \n",
    "\n",
    "for age in Age_list:\n",
    "    PSEN2_age_occipital_lobe_df =PSEN2_age_region_df[PSEN2_age_region_df['top_level_structure_name']=='occipital lobe'] \n",
    "    PSEN2_donor_age_occipital_lobe_df = PSEN2_age_occipital_lobe_df[PSEN2_age_occipital_lobe_df['donor_age']==age]\n",
    "    m1 = PSEN2_donor_age_occipital_lobe_df['A_23_P103398'].mean()\n",
    "    m2 = PSEN2_donor_age_occipital_lobe_df['A_23_P96985'].mean()\n",
    "    m3 = PSEN2_donor_age_occipital_lobe_df['A_24_P62521'].mean()\n",
    "    m4 = PSEN2_donor_age_occipital_lobe_df['CUST_16_PI416558187'].mean()\n",
    "    m5 = PSEN2_donor_age_occipital_lobe_df['CUST_526_PI416408490'].mean()\n",
    "    PSEN2_Mean_Expression_list_occipital_lobe.append(1/5*(m1+m2+m3+m4+m5))  \n",
    "                                                          \n",
    "\n",
    "fig,ax = plt.subplots(1,5,figsize=(20,5)) \n",
    "                                                          \n",
    "ax[0].scatter(Age_float_list, PSEN2_Mean_Expression_list_frontal_lobe)\n",
    "ax[1].scatter(Age_float_list, PSEN2_Mean_Expression_list_hippocampal_formation)\n",
    "ax[2].scatter(Age_float_list, PSEN2_Mean_Expression_list_parietal_lobe)\n",
    "ax[3].scatter(Age_float_list, PSEN2_Mean_Expression_list_temporal_lobe)\n",
    "ax[4].scatter(Age_float_list, PSEN2_Mean_Expression_list_occipital_lobe) \n",
    "\n",
    "ax[0].set_xlabel('Age of Donor')\n",
    "ax[1].set_xlabel('Age of Donor')\n",
    "ax[2].set_xlabel('Age of Donor')\n",
    "ax[3].set_xlabel('Age of Donor')\n",
    "ax[4].set_xlabel('Age of Donor')\n",
    "\n",
    "ax[0].set_ylabel('PSEN2 Expression Level in Frontal Lobe')\n",
    "ax[1].set_ylabel('PSEN2 Expression Level in Hippocampal Formation')\n",
    "ax[2].set_ylabel('PSEN2 Expression Level in Parietal Lobe')\n",
    "ax[3].set_ylabel('PSEN2 Expression Level in Temporal Lobe')\n",
    "ax[4].set_ylabel('PSEN2 Expression Level in Occipital Lobe')\n",
    "\n",
    "plt.suptitle('PSEN2 Expression Level in Brain Regions vs. Age')\n",
    "plt.subplots_adjust(wspace = 1)\n",
    "plt.show()\n",
    "\n",
    "rs_PSEN2_FL,ps_PSEN2_FL = stats.stats.pearsonr(Age_float_list,PSEN2_Mean_Expression_list_frontal_lobe)\n",
    "rs_PSEN2_HF,ps_PSEN2_HF = stats.stats.pearsonr(Age_float_list,PSEN2_Mean_Expression_list_hippocampal_formation)\n",
    "rs_PSEN2_PL,ps_PSEN2_PL = stats.stats.pearsonr(Age_float_list,PSEN2_Mean_Expression_list_parietal_lobe)\n",
    "rs_PSEN2_TL,ps_PSEN2_TL = stats.stats.pearsonr(Age_float_list,PSEN2_Mean_Expression_list_temporal_lobe)\n",
    "rs_PSEN2_OL,ps_PSEN2_OL = stats.stats.pearsonr(Age_float_list,PSEN2_Mean_Expression_list_occipital_lobe)\n",
    "\n",
    "print('rs_PSEN2_FL='+str(rs_PSEN2_FL),'ps_APP_FL='+str(ps_PSEN2_FL))\n",
    "print('rs_PSEN2_HF='+str(rs_PSEN2_HF),'ps_APP_HF='+str(ps_PSEN2_HF))\n",
    "print('rs_PSEN2_PL='+str(rs_PSEN2_PL),'ps_APP_PL='+str(ps_PSEN2_PL))\n",
    "print('rs_PSEN2_TL='+str(rs_PSEN2_TL),'ps_APP_TL='+str(ps_PSEN2_TL))\n",
    "print('rs_PSEN2_OL='+str(rs_PSEN2_OL),'ps_APP_OL='+str(ps_PSEN2_OL))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"four\"></a>\n",
    "\n",
    "## APOE Gene"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Scatter plots for APOE Expression Level vs. Age in 5 Brain Regions**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rs_APOE_FL=-0.4329313011887044 ps_APOE_FL=0.3911750993749672\n",
      "rs_APOE_HF=0.0063604288480007464 ps_APOE_HF=0.9904594853837456\n",
      "rs_APOE_PL=-0.41975714833121713 ps_APOE_PL=0.40734405609992064\n",
      "rs_APOE_TL=-0.6067761273556179 ps_APOE_TL=0.20153639761635195\n",
      "rs_APOE_OL=-0.6344004761609291 ps_APOE_OL=0.17606095100681327\n"
     ]
    }
   ],
   "source": [
    "APOE_Mean_Expression_list_frontal_lobe = []\n",
    "APOE_Mean_Expression_list_hippocampal_formation = []\n",
    "APOE_Mean_Expression_list_parietal_lobe = []\n",
    "APOE_Mean_Expression_list_temporal_lobe = []\n",
    "APOE_Mean_Expression_list_occipital_lobe = []\n",
    "\n",
    "for age in Age_list:\n",
    "    APOE_age_frontal_lobe_df =APOE_age_region_df[APOE_age_region_df['top_level_structure_name']=='frontal lobe'] \n",
    "    APOE_donor_age_frontal_lobe_df = APOE_age_frontal_lobe_df[APOE_age_frontal_lobe_df['donor_age']==age]\n",
    "    #calculate the mean of each probe column first\n",
    "    m1 = APOE_donor_age_frontal_lobe_df['A_23_P164650'].mean()\n",
    "    m2 = APOE_donor_age_frontal_lobe_df['A_24_P788772'].mean()\n",
    "    m3 = APOE_donor_age_frontal_lobe_df['A_32_P93036'].mean()\n",
    "    #take the average of all three column means to obtain the overall average expression level for each donor \n",
    "    APOE_Mean_Expression_list_frontal_lobe.append(1/3*(m1+m2+m3))\n",
    "    \n",
    "for age in Age_list:\n",
    "    APOE_age_hippocampal_formation_df =APOE_age_region_df[APOE_age_region_df['top_level_structure_name']=='hippocampal formation'] \n",
    "    APOE_donor_age_hippocampal_formation_df = APOE_age_hippocampal_formation_df[APOE_age_hippocampal_formation_df['donor_age']==age]\n",
    "    m1 = APOE_donor_age_hippocampal_formation_df['A_23_P164650'].mean()\n",
    "    m2 = APOE_donor_age_hippocampal_formation_df['A_24_P788772'].mean()\n",
    "    m3 = APOE_donor_age_hippocampal_formation_df['A_32_P93036'].mean()\n",
    "    APOE_Mean_Expression_list_hippocampal_formation.append(1/3*(m1+m2+m3))\n",
    "                                                          \n",
    "for age in Age_list:\n",
    "    APOE_age_parietal_lobe_df =APOE_age_region_df[APOE_age_region_df['top_level_structure_name']=='parietal lobe'] \n",
    "    APOE_donor_age_parietal_lobe_df = APOE_age_parietal_lobe_df[APOE_age_parietal_lobe_df['donor_age']==age]\n",
    "    m1 = APOE_donor_age_parietal_lobe_df['A_23_P164650'].mean()\n",
    "    m2 = APOE_donor_age_parietal_lobe_df['A_24_P788772'].mean()\n",
    "    m3 = APOE_donor_age_parietal_lobe_df['A_32_P93036'].mean()\n",
    "    APOE_Mean_Expression_list_parietal_lobe.append(1/3*(m1+m2+m3))    \n",
    "                                                          \n",
    "for age in Age_list:\n",
    "    APOE_age_temporal_lobe_df =APOE_age_region_df[APOE_age_region_df['top_level_structure_name']=='temporal lobe'] \n",
    "    APOE_donor_age_temporal_lobe_df = APOE_age_temporal_lobe_df[APOE_age_temporal_lobe_df['donor_age']==age]\n",
    "    m1 = APOE_donor_age_temporal_lobe_df['A_23_P164650'].mean()\n",
    "    m2 = APOE_donor_age_temporal_lobe_df['A_24_P788772'].mean()\n",
    "    m3 = APOE_donor_age_temporal_lobe_df['A_32_P93036'].mean()\n",
    "    APOE_Mean_Expression_list_temporal_lobe.append(1/3*(m1+m2+m3)) \n",
    "\n",
    "for age in Age_list:\n",
    "    APOE_age_occipital_lobe_df =APOE_age_region_df[APOE_age_region_df['top_level_structure_name']=='occipital lobe'] \n",
    "    APOE_donor_age_occipital_lobe_df = APOE_age_occipital_lobe_df[APOE_age_occipital_lobe_df['donor_age']==age]\n",
    "    m1 = APOE_donor_age_occipital_lobe_df['A_23_P164650'].mean()\n",
    "    m2 = APOE_donor_age_occipital_lobe_df['A_24_P788772'].mean()\n",
    "    m3 = APOE_donor_age_occipital_lobe_df['A_32_P93036'].mean()\n",
    "    APOE_Mean_Expression_list_occipital_lobe.append(1/3*(m1+m2+m3))  \n",
    "                                                          \n",
    "\n",
    "fig,ax = plt.subplots(1,5,figsize=(20,5)) \n",
    "                                                          \n",
    "ax[0].scatter(Age_float_list, APOE_Mean_Expression_list_frontal_lobe)\n",
    "ax[1].scatter(Age_float_list, APOE_Mean_Expression_list_hippocampal_formation)\n",
    "ax[2].scatter(Age_float_list, APOE_Mean_Expression_list_parietal_lobe)\n",
    "ax[3].scatter(Age_float_list, APOE_Mean_Expression_list_temporal_lobe)\n",
    "ax[4].scatter(Age_float_list, APOE_Mean_Expression_list_occipital_lobe) \n",
    "\n",
    "ax[0].set_xlabel('Age of Donor')\n",
    "ax[1].set_xlabel('Age of Donor')\n",
    "ax[2].set_xlabel('Age of Donor')\n",
    "ax[3].set_xlabel('Age of Donor')\n",
    "ax[4].set_xlabel('Age of Donor')\n",
    "\n",
    "ax[0].set_ylabel('APOE Expression Level in Frontal Lobe')\n",
    "ax[1].set_ylabel('APOE Expression Level in Hippocampal Formation')\n",
    "ax[2].set_ylabel('APOE Expression Level in Parietal Lobe')\n",
    "ax[3].set_ylabel('APOE Expression Level in Temporal Lobe')\n",
    "ax[4].set_ylabel('APOE Expression Level in Occipital Lobe')\n",
    "\n",
    "plt.suptitle('APOE Expression Level in Brain Regions vs. Age')\n",
    "plt.subplots_adjust(wspace = 1)\n",
    "plt.show()\n",
    "\n",
    "rs_APOE_FL,ps_APOE_FL = stats.stats.pearsonr(Age_float_list,APOE_Mean_Expression_list_frontal_lobe)\n",
    "rs_APOE_HF,ps_APOE_HF = stats.stats.pearsonr(Age_float_list,APOE_Mean_Expression_list_hippocampal_formation)\n",
    "rs_APOE_PL,ps_APOE_PL = stats.stats.pearsonr(Age_float_list,APOE_Mean_Expression_list_parietal_lobe)\n",
    "rs_APOE_TL,ps_APOE_TL = stats.stats.pearsonr(Age_float_list,APOE_Mean_Expression_list_temporal_lobe)\n",
    "rs_APOE_OL,ps_APOE_OL = stats.stats.pearsonr(Age_float_list,APOE_Mean_Expression_list_occipital_lobe)\n",
    "\n",
    "print('rs_APOE_FL='+str(rs_APOE_FL),'ps_APOE_FL='+str(ps_APOE_FL))\n",
    "print('rs_APOE_HF='+str(rs_APOE_HF),'ps_APOE_HF='+str(ps_APOE_HF))\n",
    "print('rs_APOE_PL='+str(rs_APOE_PL),'ps_APOE_PL='+str(ps_APOE_PL))\n",
    "print('rs_APOE_TL='+str(rs_APOE_TL),'ps_APOE_TL='+str(ps_APOE_TL))\n",
    "print('rs_APOE_OL='+str(rs_APOE_OL),'ps_APOE_OL='+str(ps_APOE_OL))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based upon the 20 scatter plots of expression level of the 4 genes vs. age of the donors in 5 different brain regions; and their corresponding p-values for correlation, we could conclude that there is a positive correlation between the expression level of the PSEN1 gene and age of the donor in the frontal and temporal lobes since the corresponding p-values are 0.033 and 0.035 and r-value respectively for these two dataset and their r-values are 0.848 and 0.951 respectively .  For the remaining 18 datasets, all their p-values are greater than 0.05, thus we could not conclude that there is a significant correlation within these datasets. \n",
    "\n",
    "Based on our boxplot data of the APP gene in the first dataset, the cerebellum cortex tends to have the lowest expression value in contrast to the claustrum region having the highest expression value. For the boxplots of PSEN1, hippocampal formation had the lowest expression value while white matter had the highest. For PSEN2, white matter was the lowest while the dorsal thalamus had the highest expression value. For APOE, the globus pallidus had the highest value and sulci & spaces, the lowest. The heatmap of these gene expression values based on the brain regions also provide additional visualization of each gene’s highest and lowest levels of expression to support this.  These results indicate a level of significance to which there are differential expressions based on the regions of the brain for genes associated with Alzeimer’s  disease.\n",
    "\n",
    "According to the boxplot and heatmap for the second dataset, it clearly shows that there is a significant level of gene expression across the APP gene and PSEN 1 gene. It shows that the GABAergic and Glutamatergic neurons of these cell types within the primary motor cortex showed the highest level of expression. Also,  it shows that PSEN1 and APOE genes did not have any expression in the cell types of the primary motor cortex, aside from a few outliers present in the APOE gene. Throughout the dataset, it showed that there is indeed differential expression of genes within the primary motor cortex. This can be seen with the high and low expression levels between the APP, PSEN1, PSEN2, and APOE gene. \n",
    "\n",
    "There were a couple of limitations to our analysis with the first being that our data lacked information from people with Alzheimer’s disease. Furthermore, the sample size is very small as there were only six donors for the entire dataset on the gene expression levels in brain regions. Another major limitation is that the cell type dataset was limited to one cortex of the brain instead of multiple cortexes. It is also notable that the other factors besides the age of the donors are not controlled in this data set (e.g: gender, ethnicity, etc.).\n",
    "\n",
    "Additional experiments that we could do would be to examine these four gene expressions within patients that have AD as well as gathering additional data on the cell types associated with the brain regions. Increasing the sample size across a broader range of age groups would also be another way we can obtain a stronger correlation. Other experiments that would be interesting would be to see if gender or ethnicity creates any disparity in the amount of risk of AD onset by analyzing any difference in gene expression in the brain and for cell types. We could also investigate the correlation between the age and particular cell types in the brain if we have a dataset of the gene expression in various cell types labelled with the donors’ ages.\n"
   ]
  }
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