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"text": [ - "[1] \"../../..\"\n" - ] - } - ], + "outputs": [], "source": [ "# define the base directory\n", "base_dir <- file.path(\n", @@ -56,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": { "vscode": { "languageId": "r" @@ -90,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "vscode": { "languageId": "r" @@ -110,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "vscode": { "languageId": "r" @@ -216,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "vscode": { "languageId": "r" @@ -390,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "vscode": { "languageId": "r" @@ -593,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "vscode": { "languageId": "r" @@ -734,7 +726,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "vscode": { "languageId": "r" @@ -841,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "vscode": { "languageId": "r" @@ -1053,7 +1045,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "vscode": { "languageId": "r" @@ -1084,7 +1076,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "vscode": { "languageId": "r" @@ -1107,6 +1099,191 @@ "output_type": "display_data" } ], + "source": [ + "width <- 14\n", + "height <- 14\n", + "options(repr.plot.width = width, repr.plot.height = height, units = \"cm\")\n", + "# set pallette\n", + "viridis_pal_custom <- viridis::viridis_pal(option = \"C\")(5)\n", + "\n", + "data_split_plate_map_full <- (\n", + " raw_map(\n", + " data = updated_platemap$label,\n", + " well = updated_platemap$well_id,\n", + " plate = 384, # number of wells in plate apriori known\n", + " size = 14 # size of the wells displayed\n", + " )\n", + " + theme_dark()\n", + " + ggplot2::geom_point(\n", + " aes(shape = updated_platemap$Metadata_cell_type),\n", + " size = 5\n", + " )\n", + " + labs(fill = \"Data Split\", shape = \"Cell Type\")\n", + " # change legend text size for fill\n", + "\n", + " + guides(shape = guide_legend(override.aes = list(size = 12), nrow = 1))\n", + " + guides(fill = guide_legend(override.aes = list(size = 12),ncol = 2))\n", + " + theme(\n", + " legend.title = element_text(size = 18,hjust = 0.5),\n", + " legend.text = element_text(size = 16),\n", + " )\n", + " # cell type legend\n", + " + scale_shape_manual(\n", + " values = c(\n", + " 'Blank' = 0,\n", + " 'PBMC' = 19,\n", + " 'SH-SY5Y' = 8\n", + " )\n", + " )\n", + " # data split legend\n", + " + scale_fill_manual(\n", + " values = c(\n", + " 'Blank' = \"grey\",\n", + " 'Pool' = viridis_pal_custom[3],\n", + " 'Holdout' = viridis_pal_custom[4],\n", + " 'Treatment Holdout' = viridis_pal_custom[5]\n", + " \n", + " )\n", + " )\n", + " + theme(\n", + " axis.text.x = element_text(size = 16),\n", + " axis.text.y = element_text(size = 16)\n", + " )\n", + "\n", + ")\n", + "data_split_plate_map_full\n", + "ggsave(\n", + " filename = \"../figures/data_split_plate_map_full.png\",\n", + " plot = data_split_plate_map_full,\n", + " width = width,\n", + " height = height,\n", + " units = \"in\",\n", + " dpi = 600\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 15
well_idcell_typeincubation.inducer..h.inhibitorinhibitor_concentrationinhibitor_concentration_unitinducer1inducer1_concentrationinducer1_concentration_unitinducer2inducer2_concentrationinducer2_concentration_unitMetadata_cell_typelabeln
<chr><chr><int><chr><dbl><chr><chr><chr><chr><chr><dbl><chr><fct><fct><int>
1A016NANABlankBlankNA
2A026NANABlankBlankNA
3A036NANABlankBlankNA
4A046NANABlankBlankNA
5A056NANABlankBlankNA
6A066NANABlankBlankNA
\n" + ], + "text/latex": [ + "A data.frame: 6 × 15\n", + "\\begin{tabular}{r|lllllllllllllll}\n", + " & well\\_id & cell\\_type & incubation.inducer..h. & inhibitor & inhibitor\\_concentration & inhibitor\\_concentration\\_unit & inducer1 & inducer1\\_concentration & inducer1\\_concentration\\_unit & inducer2 & inducer2\\_concentration & inducer2\\_concentration\\_unit & Metadata\\_cell\\_type & label & n\\\\\n", + " & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & A01 & & 6 & & NA & & & & & & NA & & Blank & Blank & NA\\\\\n", + "\t2 & A02 & & 6 & & NA & & & & & & NA & & Blank & Blank & NA\\\\\n", + "\t3 & A03 & & 6 & & NA & & & & & & NA & & Blank & Blank & NA\\\\\n", + "\t4 & A04 & & 6 & & NA & & & & & & NA & & Blank & Blank & NA\\\\\n", + "\t5 & A05 & & 6 & & NA & & & & & & NA & & Blank & Blank & NA\\\\\n", + "\t6 & A06 & & 6 & & NA & & & & & & NA & & Blank & Blank & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 15\n", + "\n", + "| | well_id <chr> | cell_type <chr> | incubation.inducer..h. <int> | inhibitor <chr> | inhibitor_concentration <dbl> | inhibitor_concentration_unit <chr> | inducer1 <chr> | inducer1_concentration <chr> | inducer1_concentration_unit <chr> | inducer2 <chr> | inducer2_concentration <dbl> | inducer2_concentration_unit <chr> | Metadata_cell_type <fct> | label <fct> | n <int> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | A01 | | 6 | | NA | | | | | | NA | | Blank | Blank | NA |\n", + "| 2 | A02 | | 6 | | NA | | | | | | NA | | Blank | Blank | NA |\n", + "| 3 | A03 | | 6 | | NA | | | | | | NA | | Blank | Blank | NA |\n", + "| 4 | A04 | | 6 | | NA | | | | | | NA | | Blank | Blank | NA |\n", + "| 5 | A05 | | 6 | | NA | | | | | | NA | | Blank | Blank | NA |\n", + "| 6 | A06 | | 6 | | NA | | | | | | NA | | Blank | Blank | NA |\n", + "\n" + ], + "text/plain": [ + " well_id cell_type incubation.inducer..h. inhibitor inhibitor_concentration\n", + "1 A01 6 NA \n", + "2 A02 6 NA \n", + "3 A03 6 NA \n", + "4 A04 6 NA \n", + "5 A05 6 NA \n", + "6 A06 6 NA \n", + " inhibitor_concentration_unit inducer1 inducer1_concentration\n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "5 \n", + "6 \n", + " inducer1_concentration_unit inducer2 inducer2_concentration\n", + "1 NA \n", + "2 NA \n", + "3 NA \n", + "4 NA \n", + "5 NA \n", + "6 NA \n", + " inducer2_concentration_unit Metadata_cell_type label n \n", + "1 Blank Blank NA\n", + "2 Blank Blank NA\n", + "3 Blank Blank NA\n", + "4 Blank Blank NA\n", + "5 Blank Blank NA\n", + "6 Blank Blank NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# remove SHSY5Y from the platemap\n", + "updated_platemap <- updated_platemap[updated_platemap$Metadata_cell_type != \"SH-SY5Y\",]\n", + "head(updated_platemap)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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AgIgI7AAAAAAgIgI7AAAAAIiIwA4AAAAAIiKwAwAAAICICOwAAAAAICICOwAAAACIiMAOAAAAACJSlXYBhBBCc3PzDTfcsGTJkhDCPffc09DQULape3t7H3/88QULFqxevXrHjh1DhgyZMGHCjBkzTjzxxOrq6jIU0NHR8fDDDz///PPvvfdeV1dXQ0PD3nvvfcwxx8yaNauysrIMBezi2Wef/fnPfx5COOuss2bPnp30dBdeeOGqVas+7rczZ868+OKLk66hvb39D3/4w6JFizZt2lRVVTV27Nijjz76pJNOSno7vPbaa5966qlP/v9861vf+ta3vpVoGa2trQ899NCLL764bt267u7uIUOGfP7zn58+ffpJJ51UW1ub6NQFnZ2djzzyyKJFi9auXdvV1TVixIgpU6Z84xvf2GeffZKYrp/dJp/Pz58//6mnnnr33Xe3b9/e2Ni4//77n3jiiYceemjZakiiMfZzzOQaYz8LSK4xFrdWB7Ex9qeARBtj/9dAEr3xf5w96cbYz5efXGPsZwFJNMYB7deD3gOL6CqD2wMHVEASPXBAYybRA0t5UWU+OASAAoFd+ubNm3fLLbd0dHSUf+qWlpYrrrji3XffDSFUVFQ0NjZu27ZtxYoVK1asePzxx+fOnTt8+PBEC1i9evWcOXOam5tDCFVVVUOHDm1tbV22bNmyZcueeOKJOXPmlDO7DCG0tLTceOON5Zxx+/btIYTa2tqPPAAtQ2C0evXqyy+/fMuWLSGEESNG9PT0rFy5cuXKlU8//fTcuXNHjhyZ3NS1tbVDhgz5uN92dnbmcrmKimSvAn7ttdeuvPLK9vb26urqCRMmNDY2btmyZfny5cuXL3/kkUd+/OMf77777okWsGHDhssvv3z9+vUhhCFDhowYMWLz5s1PP/30n/70pwsuuOC4444b3On62W2y2exPfvKTF198MYRQU1MzYsSI1tbWRYsWLVq06PTTTz/nnHPKUEMSjbGfYybXGPtZQHKNsbi1OoiNsZ8FJNcY+78GkuiN/Zk90cbYz5efXGPsZwFJNMYB7deD3gOL6CqD2wMHVEASPXBAYybRA0t5UeU/OASAAoFdmlpaWm644YYXX3yxoaFh1qxZ8+bNK+fs+Xz+pz/96bvvvltfX3/uuefOnDmzqqqqu7v74Ycfvu2221avXv2b3/zmkksuSa6Azs7OK6+8srm5effddz/vvPMOPvjgTCazY8eO//7v/77nnnvefPPNW2655aKLLkqugA/793//97a2ttra2q6urvLM2N7eHkK45JJLjjjiiPLMuLOOjo45c+Zs2bJlypQp559//rhx40IIy5cvv+qqq9auXfub3/zm0ksvTW72884777zzzvvIX61ater73/9+fX39V7/61eQK6Ozs/NnPftbe3j558uRLLrlk1KhRhZ+/++67V1xxxYYNG66++upf/OIXyRWQzWbnzp27fv364cOHX3TRRYcddlgmk2lpabn55pubmppuuOGGffbZZ+LEiYMy14C6ze233/7iiy/W1tb+3d/9XaEzdHV1Pfjgg3fccccDDzywzz77zJw5M7kakmiM/R8zocbY/wISaoylrNVBaYwDKiCJxjigAga9N/Z/9oQa44C2wCQaY/8LSKIxDnS/HtweONDZB70HDqiAJHrggMZMogeW+KLKf3AIAAWeYZemBQsWvPjii1OmTLnhhhv+6q/+qsyzL1u27I033gghfO9735s1a1ZVVVUIoaam5vTTTz/llFNCCAsXLuzs7EyugPnz52/atCmTyVx22WWHHHJIJpMJIdTX15955pmFz88XLFjQ09OTXAG7eOyxx1544YUDDjjggAMOKM+MuVxux44dIYQyX0jY57777mtubp4wYcJll11WOCMNIUyePPnCCy/cb7/96uvry7n++2Sz2WuvvTabzZ599tm77bZbchMtW7Zs69atIYTzzjuv76Q0hLDPPvsUbjd7/fXXC5/wJ2ThwoVr1qwJIVx44YXTpk0r7AIjR4685JJLDjzwwFwud+eddw7WXP3vNlu3bn3ooYdCCN/5znf6OkNtbe3s2bNPPvnkEMKdd96Zz+eTqyGJxtj/MRNqjP0vIKHGWPRaHazG2P8CEmqMA1oDg94bS9+qS2yMA9oFkmiM/S8gicY4oP160HvgQLvKoPfAARWQRA8c0JhJ9MBSXlT5Dw4BoI/ALk3V1dXnnHPO3Llzx4wZU/7Z29vbp0yZst9++x111FG7/KrwiJZsNrtp06ZEazj00ENnzpw5fvz4XX4+bdq0EEJ3d3dLS0uiBfTZuHHjrbfeWl1dff7555dnxvCXq0hCCEOHDi3bpH3y+fwTTzwRQjjzzDN3ucXs0EMP/eUvf3nhhReW5zmGu/iv//qvlStXHnDAASeeeGKiE/XdavTh27v22GOPwj8Kt+YlZOnSpSGEcePGHX744Tv/PJPJnHHGGSGEJUuWtLW1Dcpc/e82zz//fE9Pz9ChQz9839mpp54aQti0aVPhzCehGpJojP0fM6HGOKAXlURjLG6tDmJjHNCfoPCPwW2M/S8gid5Y+lZdYmPsfwEJNcb+F5BEYxzQfj3oPXCgXWXQe+CACkiiBw50zEHvgUW/qFQODgGgj1ti03TiiScWPjlMxYwZM2bMmPGRv+qrqq6uLrkCTjjhhBNOOOETCshkMiNGjEiugD75fP7aa6/t7Ow855xz9tprrzLMWNB3zpPKFXZvvvnm1q1bKysrdzkpStfGjRvvvffeysrK888/P+m9o+9vvXr16n333XfnX7333nshhJqamr4T1CRs3rw5hPDhc5IQwoEHHhhCyGazb7755mGHHVb6XP3vNsuXLw8hTJo0qXANws722GOPMWPGNDc3v/rqq0Vca9DPGpJojP0fM6HG2P8CEmqMRazVwW2M/S8gocbY/wKS6I0lbtWlN8b+F5BQY+x/AUk0xgHt14PeAwfaVQa9Bw6ogCR64IDGTKIHFvei0jo4BIA+Ars0pZjWfbLCg5Z33333sWPHln/2vqsbDjrooJqamjLM+MADDyxfvnzy5Mnf+MY3yjBdn74LSXp6eu65555XXnmlpaWlurp6/PjxRx111IwZMxLdQlauXBlCGDduXF1d3bp165566qlVq1Z1dXXttttu06dPP+KII1LZPm+++ebu7u5TTjmlDAfH++yzz/Tp059//vn/+I//+Kd/+qe+y0lWrlz5+9//PoRwxhlnJLoFFp4c/5G39vRFFWvXrh2UwK7/f83COXnfbYC7GDduXHNz89q1a5OrIYkNb1DGLKUxll5AiY2xiAIGtzH2v4CEGmP/l0qiN5a4AZTeGPtfQEKNsf8FlLMxho/ar5Prgf2ZPZT34HBAbS2Jg8P+j5nQweEnFJDWwSEA9BHYsat33nnn0UcfDSEUnlZTNvl8fuvWratWrbrvvvuWLVs2atSov//7vy/DvKtXr77rrrvq6ur+4R/+ocwRVd956fe///2dvwlu1apVTU1NkydP/uEPf9jY2JjQ7Bs2bAghjBo16rHHHrvpppt6e3v7fjVv3rypU6f+8z//c5kv/VuxYsXixYuHDBly5plnlmfGSy655KabbnrmmWfOO++8z3/+88OGDduyZcvatWsbGxv/5m/+Zvbs2YnO/rnPfS6EsGbNmnw+v8u2t27dusI/+jaSsik8vurjvgSz8POy3aseD42xbNJtjCG+3qgx9hn0xviR+3XZemBaXaW4ApKotj9jJtoDP6GAFHsgAPQR2PH/WbVq1RVXXNHb2ztz5syvfOUrZZv3pptuevjhhwv//tznPjd79uzTTjtt+PDhSc+bzWavueaanp6e7373u4WThHLqO+UYPXr0BRdcMHXq1IaGhg0bNjzyyCMPPfTQ8uXLf/GLX1x55ZUJzV44E37//fd//etfn3DCCSeffPLuu+/e1tb2zDPP3HXXXcuWLbvmmmv+5V/+JaHZP9Idd9wRQjj11FMTPRvfWU1NzbRp09atW7d8+fJ333237+dTp07dd999kz5GP/zwwx9++OHNmzc//vjju9wB9Lvf/a7wj8LT98up8ODtj7t+ofDzRL+OJkIaYzml2xhDfL1RY+wzuI3x4/br8vTAtLpKcQUkUW1/xky0B35CAen2QADoI7Dj/3nhhReuuuqqzs7OGTNmXHTRReWcuqKioqKiIpfLhRBaW1vffPPNJUuWzJw5M+l577rrrpUrV06bNu3jHpiSqAMOOOCHP/xhRUXFwQcf3HdusOeee5577rmjR4++7bbbXn755SVLlhxyyCFJzF644ai5uXn27NlnnXVW4YejR48+44wzqqqqbr311sWLF7/xxhv7779/ErN/2Ouvv758+fKqqqqTTjqpPDOGv5wMDBs27Lzzzjv00ENHjhz5wQcfNDU13XfffQsXLjzrrLMKzzhPyCGHHDJ58uTly5cX7nf78pe/XF9fX7iO4M9//nPhSUnluSu8/4r7fthPNY2xzNJtjCGy3qgxJtQYi96vB6UHpthViiggiWr7OWZyPfCTC0i3BwJAH4Ed/9d99913++235/P5008//eyzzy7z9f/nnnvuueee29vbu379+oULFxZufFi8ePEll1ySXCVvvPHG/fff39jY+L3vfS+hKT7Zbrvttttuu33kr0477bT77ruvra3thRdeSOi8tO/bDwtfe7ezr3/96/fcc09HR8dLL71UtsDuj3/8YwhhxowZH3cj0qBramp6+OGHKysrL7vssv3226/wwz333POb3/zmF7/4xTlz5txxxx1HHHHEhAkTEiogk8n84Ac/mDNnzsqVK2+++eabb7658PO6urqLL7747rvvbm5uHjJkSEKzf5y6urr29vaurq6P/G3h5+WvKi0aY/ml2xhDZL1RY0yiMX7yfp10D0y3qwy0gCSq7f+YCfXATy4g9R4IAH0EdoTu7u7rrrtuwYIFNTU1559/fiq3ZhRUVVVNmDDhm9/85tSpUy+99NKmpqajjjrq6KOPTmKurq6ua665JpfLXXjhhWU7Eeq/ysrKvfba69VXX/3ggw8SmmLYsGEhhLq6ug9/21plZeW4cePefvvt5GbfRUdHx/PPPx9C+PKXv1yeGUMITz/9dAhh0qRJfSelfQ477LDCdRxNTU2JPl1o5MiRv/rVr55++umXXnpp8+bNQ4cO3X///Y877rgxY8Zcd911IYS+J76XzahRo5qbmz/uCU1btmwp/H/KW1QKNMbPZmMMMfVGjXHQG2N/9uvkemDqXWVABSRRbXFjDmIP/B8LiLwHAvBZI7D7rOvu7p47d+7SpUtHjx79ox/96Itf/GLaFYUQwoEHHjhhwoS1a9e+/PLLCZ2XPvvss+vWrauqqrr77rvvvvvunX+1fv36EMKDDz64YMGCEMJVV12Vym2JhSedV1dXJzT+5z//+RBCd3d3b29vVdWuraDwPX2F/5bBokWLenp6amtrv/SlL5VnxhDCpk2bwl8ecP5hhXO2jRs3Jl1GZWXlrFmzZs2atfMP33///cKDtPbZZ5+kC9jFhAkT3nzzzY/8DsR8Pl/4/sTCxvO/mMb4mW2MIabeqDEObmPs536dUA9MvasMqIAkqi19zBJ7YH8KiL8HAvCZIrD7TOvt7f3pT3+6dOnS8ePHX3nllaNHjy7n7D//+c/Xr19/+OGH//Vf//WHf1t4ZEnhv0konPX19vbu/ETtnbW0tBQ+YE+uhoULF65bt278+PFHHnnkh8tbs2ZNCGHcuHEJzT558uRMJpPL5V577bUpU6bs/Kt8Pl/4Mr6xY8cmNPsuli5dGkKYNGlSOQ9/6+vrw18ul/iwbdu2hYRzgU/Q1NQUQthzzz3Hjx9f5qmnTJny5JNPvvbaa93d3bv8Od55553W1tYQQjnjg/LTGD/LjTHE1Bs1xl2U0hj7v18n0QPT7SoDLSCJavs/ZkI9sJ8FxNADAaCPwO4z7bbbbis8wvknP/lJ+a/8z2azK1eubGlp+fCX361Zs6ZwUlXSxCIAACAASURBVLTXXnslNPvxxx9//PHHf+Sv/vVf//Xll18+66yzZs+endDsBU899dTixYvHjBkzderUXR6I88gjjxSuI5g+fXpCs48aNepLX/rS0qVLf//73x900EE7P8PlySefLHxRY3JPidrFihUrQtmvJttvv/1ef/31119/fevWrbvc+/b+++9v2LAhhJDoY6pWrVp1/fXXb9y48Ve/+tXOd3i1tLQUHlyVyuOup0+fXl9fv2PHjkcfffS0007b+Vf3339/CGHvvfcu/3V/5aQxfuSvPiONMcTUGzXGQWyM/d+vk+iB6XaVgRaQRLX9HzOhHtjPAmLogQDQp0z3uxGhlStXPvjggyGEiy++OJXDxzPOOKOioqKlpeXyyy9/5ZVXCp9V9vT0PPvssz/+8Y/z+fyQIUOOPfbY8hdWNrNnz85kMs3NzZdffvnbb79d+GFnZ+f999//29/+NoRwzDHH7LvvvskVcPbZZ1dWVi5btuzqq68uXDeRz+cXLVp06623hhAOPfTQDz/DKAmdnZ2FB0KV+UbLr33ta9XV1R0dHVdddVXhLrCC1atXX3XVVSGEMWPGHHPMMckVMH78+G3btrW1tf3bv/1b4TarEMI777xz2WWXtbW1TZgw4etf/3pys3+choaGwtnI7bff/thjj2Wz2RBCR0fHb3/726ampkwm8+1vf7v8VZWNxpi61BtjiKM3aoyD2BgHtF8Peg9MvasMqIAkqh3QmEn0wNT/BABQnMygfD89xTnrrLO6u7sL/87lcp2dneH//+qx0047LbnnOl9//fXz5s0Ln/hlZ7Nnz070g8R58+bdeOONhRsQKioqhg4d2tbWVtgmhwwZcumll5btCq+dlfND1Mcee+zXv/514Xygvr6+oaGhpaWl8D+nTZv2gx/8oK6uLtEC5s+ff9111/X29lZWVo4ZM6a9vX379u0hhIkTJ86ZM2f48OGJzl6wZs2aCy64IIRw2WWXTZs2rQwz9lm0aNHVV1/d2dlZeJZ84fFM69evz+VyI0eOvPzyyydOnJhoAa+++uqcOXO6uroymUzhYpbCvTZ77LHH3LlzP+67MoswoG6Tz+evueaaZ555JoRQW1vb2NhY2Cwzmcx3vvOdU045JdEakmiM/R8zocY4oBeVRGMsfa2W2BgHVEASjXGga2Bwe2MR639wG+OACkiiMQ6ogEFvjAPdrwe3Bw509kHvgQMqIIkeONAxB70HDsqLcoUdAOXnltg0bd++vaenZ5cfFu73Keg7YktCV1fXh2fcxYfLG1yzZs066KCDHnnkkZdffnnDhg3t7e319fXjxo075JBDvva1r30WvonyhBNOmDRp0kMPPfTKK69s2rSppaVl2LBh++6773HHHXfkkUfufCtWQo499tiJEyc+8MADy5Yt27x5c01NzX777XfMMcecdNJJZXtqUt8W2NDQUJ4Z+xx55JE33njjww8/vGTJkg0bNrz//vv19fX77bff4YcfftJJJw0dOjTpAg466KDrr7/+j3/849KlSzdv3pzJZCZOnHj00UefcsoptbW1gzjRgLpNJpO5+OKLp0+f/vjjj7/zzjuFO+MmT5586qmnlnJhUT9rSKIx9n/MhBrjgF5UEo0x3bebgRaQRGMc6BoY3N5YxPof3MY4oAKSaIwDKmDQG+NA9+vB7YEDnX3Q99YBFZBEDxzomIPeA2M44gWAIrjCDgAAAAAi4hl2AAAAABARgR0AAAAARERgF5G2trbCM63Tsnnz5nQf4bFhw4YUZ+/t7W1ubk6xgI6OjsLXEaalpaWl7zkvqdi4cWPh++BSkcvldv5KxPLr7OzcunVrigVs27btE57vUwYffPBB4SnjaUm3BXV3d2/ZsiXFAtrb29va2lIsYMuWLUk/Su+TpbsBZLPZwtfCpqWjo6O1tTXFAlpbW3fs2JFiAZs2bUrxPSifz2/cuDGt2UMIXV1d6bYgh8EAsAuBXUTuvffeRx99NMUCrrzyynfffTfFAr797W+nOPvatWsvv/zyFAt4/PHHf/e736VYwA033LBkyZIUCzj//PMLX4eXivb29osuuiit2UMIL7zwwn/+53+mWMCdd9759NNPp1jAj370o3QTk3Rb0Ntvv/2zn/0sxQIefPDBBx54IMUCfvnLX7722mspFvC3f/u3KT7Y94MPPrj00kvTmj2E0NTUdNttt6VYwM033/zcc8+lWMA//uM/Fr6ONhW9vb3f/e5305o9hPDqq69ee+21KRZw7733PvLIIykWkPphMADsQmAHAAAAABER2AEAAABARAR2AAAAABCRqrQLKMZLL72UxENG8vl8S0vLqFGjih5h8+bNo0ePLnrxlpaWjo6OefPmpVVAQ0PDkiVL1qxZU9ziPT09O3bsGDZsWNEFfOELXyjl5W/btq2+vr66urq4xbdu3Tps2LAU1//777+/Y8eOFAsIIbzxxhvt7e3FLZvP57ds2VJKARMmTPjTn/5UVVVkX+ro6MhkMvX19cUt3tXVNW7cuBTX/9q1a7PZbIoFtLe3r1mzJsUCRo4cuXjx4tdff724xbu6urq7uxsbG4suoMQWtHXr1sbGxsrKyuIW/+CDD+rq6lJc/x988EEul0uxgOrq6ldeeaXoL17I5XKtra0jR44suoCJEyfOmzcvk8kUt3h7e3t1dXVtbW3Ri48ePTrF9b9q1ap034N6enpWrlxZytc+lFjA2LFjm5qaGhoailu8s7Mzm80WvXgul9t7771LWf9btmwZOXJk0RvwunXrKioqUtwAHAaXfhT3cWbNmpXEsAAkLZPi85WLdtttt61atWrQh83n852dnUWf7YcQduzYUeLimUymrq4urQK2bdvW0NBQ9NlmLpfr6ekp+lwlhLBly5ZSAtPu7u6qqqqKiiKvG81ms+3t7cOHDy+6gBLXf2dnZy6XGzJkSFoFtLe319TU1NTUpFVAS0vLiBEjij7Z6OnpyWQyRed9+Xy+tbV1xIgRxS0eSn753d3d3d3dQ4cOTauAjo6OioqKFFtQa2trY2Nj0btwLpfr7e0tZQMusQV1dXXV1NQUvQH39vZ2dHSUcrJXegvK5/Mpvglu27ZtyJAhpezCXV1dpWzAJeYdPT09FRUVpbyHtrW1pfge1NXV1dvbW3TeVHoB27dvr6qqKuUoosQCCp/bFd2Cent78/l80Z8ahhBaWlpKSZx37NhRV1dXygbc2dlZymcepb8HlfKpW+kFpH4YXGL9n+CKK65IYlgAkuaWWAAAAACIiMAOAAAAACIisAMAAACAiAjsAAAAACAiAjsAAAAAiIjADgAAAAAiIrADAAAAgIgI7AAAAAAgIgI7AAAAAIiIwA4AAAAAIlKVdgHFyGaz+Xx+0IctjFniyKUsnnoBhcWLHiH/FyUWUMqyJdZfYgGlL55uAan/BUtcvMS/YOobQOrrP/U1EP5XtKASp069/s/4BhBK6yGf9vegGPagT+8aiKT+UpZNvf7SR/iMbwAfp6enp7q6OomRAUjUpzKwa2tr6+rqGvRh8/l8b29vKSOXuHg2mw0hpFhALpfr7u6uqCjyustcLld4CUXL5/MlvvxcLpfJZIpbPJfL5XK5FNd/T09P6WugxA2gUENaBRReftF/wWw2m8lkStkI090AstlsDAUUvXjpBRRaUNEbQD6fL/HjnNJ3wHw+X8oGnO4GUKg/9fegonfh0t/EQ2lvwYUWVPR7aD6fT30DyGazqR8FlSLdFpTL5Qp/xKILKH0HDCF8qltQJpNJvQWleBhcegf7OJs2bdpzzz2TGBmARH0qA7sRI0Zs3bp10IfN5/OdnZ11dXWljFDi4plMJsUCuru7a2trKysri1u8EPfU1tYWXUBHR0eJ9VdVVRV9pJXNZnt6elJc/yGEXC6XYgG9vb01NTU1NTVpFbBjx466urqiTzZ6enoymUxVVZFtLfUO0N3dXXoPKWXxXC5XUVGRYgFdXV21tbWlnCwVtuGiCyi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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 840, + "width": 840 + } + }, + "output_type": "display_data" + } + ], "source": [ "width <- 14\n", "height <- 14\n", From 4785046e715e801d5d453a3306f62c1d1e027e7b Mon Sep 17 00:00:00 2001 From: MikeLippincott <1michaell2017@gmail.com> Date: Thu, 8 Feb 2024 11:20:26 -0700 Subject: [PATCH 2/3] update --- .../figures/data_split_plate_map.png | Bin 821794 -> 825298 bytes .../figures/data_split_plate_map_full.png | Bin 1001200 -> 1007906 bytes .../notebooks/S7.ipynb | 34 ++++---- figures/S7.supplemental_figure7/scripts/S7.r | 78 ++++++++++++++++-- 4 files changed, 89 insertions(+), 23 deletions(-) diff --git a/figures/S7.supplemental_figure7/figures/data_split_plate_map.png b/figures/S7.supplemental_figure7/figures/data_split_plate_map.png index b3936d8accbba4410cca54b5b3252d03036e91aa..d63389506d3e63bf56cd7f2927c7280a5cf2aaf1 100644 GIT binary patch literal 825298 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