Glm pca devel

.github/workflows/test.yml

@@ -44,18 +44,18 @@ jobs:
         which sincei
         sincei --help
     - name: Generate coverage report
+      if: matrix.os == 'ubuntu-latest'
       run: |
         pip install pytest
         pip install pytest-cov
-        pytest --cov=./ --cov-report=xml
+        pytest --cov=./
     - name: Upload coverage to Codecov
+      if: matrix.os == 'ubuntu-latest'
       uses: codecov/codecov-action@v3
       with:
         token: ${{ secrets.CODECOV_TOKEN }}
-        directory: ./coverage/reports/
         env_vars: OS,PYTHON
         fail_ci_if_error: true
-        files: ./coverage1.xml,./coverage2.xml,!./cache
         flags: unittests
         name: codecov-umbrella
         verbose: true

README.md

@@ -4,7 +4,7 @@
 
 ## sincei: A user-friendly toolkit for QC, counting, clustering and plotting of single-cell (epi)genomics data.
 
-[![DOI](https://zenodo.org/badge/271841139.svg)](https://zenodo.org/badge/latestdoi/271841139) [![Documentation Status](https://readthedocs.org/projects/sincei/badge/?version=latest)](https://sincei.readthedocs.io/en/latest/?badge=latest) [![PyPI Version](https://img.shields.io/pypi/v/sincei.svg?style=plastic)](https://pypi.org/project/sincei/) [![test](https://github.com/vivekbhr/sincei/actions/workflows/test.yml/badge.svg)](https://github.com/vivekbhr/sincei/actions/workflows/test.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![DOI](https://zenodo.org/badge/271841139.svg)](https://zenodo.org/badge/latestdoi/271841139) [![Documentation Status](https://readthedocs.org/projects/sincei/badge/?version=latest)](https://sincei.readthedocs.io/en/latest/?badge=latest) [![PyPI Version](https://img.shields.io/pypi/v/sincei.svg?style=plastic)](https://pypi.org/project/sincei/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![test](https://github.com/vivekbhr/sincei/actions/workflows/test.yml/badge.svg)](https://github.com/vivekbhr/sincei/actions/workflows/test.yml) [![codecov](https://codecov.io/gh/vivekbhr/sincei/graph/badge.svg?token=VRTMITHHBI)](https://codecov.io/gh/vivekbhr/sincei)
 
 ## Features
 

docs/conf.py

@@ -55,9 +55,13 @@ def get_version(path=VPATH):
     "sphinx.ext.autosummary",
     "sphinxarg.ext",
     "sphinx_toolbox.collapse",
+    "nbsphinx",
 ]
 #    'numpydoc'
 
+# Do not execute tutorial notebooks
+nbsphinx_execute = "never"
+
 # This is needed to suppress autosummary reordering
 numpydoc_show_class_members = False
 
@@ -74,12 +78,13 @@ def get_version(path=VPATH):
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
-html_theme = 'sphinx_rtd_theme'
+html_theme = "sphinx_rtd_theme"
 on_rtd = os.environ.get("READTHEDOCS", None) == "True"
 
 if not on_rtd:  # only import and set the theme if we're building docs locally
     import sphinx_rtd_theme
-    html_theme = 'sphinx_rtd_theme'
+
+    html_theme = "sphinx_rtd_theme"
     html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 
 # Add any paths that contain custom static files (such as style sheets) here,

docs/content/tutorials.rst

@@ -1,9 +1,21 @@
 Tutorials
 ===========
 
+Tutorials for using sincei on the command line
+-----------------------------------------------
+
 .. toctree::
     :maxdepth: 1
 
     tutorials/sincei_tutorial_sortChIC.rst
     tutorials/sincei_tutorial_10x.rst
     tutorials/sincei_tutorial_10xATAC.rst
+
+Tutorials for using sincei inside python
+-----------------------------------------
+
+.. toctree::
+    :maxdepth: 1
+
+    tutorials/snmC2Tseq_preprocessing
+    tutorials/GLM_PCA_analysis

docs/content/tutorials/sincei_tutorial_10x.rst

@@ -19,9 +19,9 @@ and processed using the standard 10x genomics `cellranger-arc
 workflow <https://support.10xgenomics.com/single-cell-multiome-atac-gex/software/pipelines/latest/algorithms/overview>`__.
 Below is the structure of the output directory from the workflow:
 
-.. code:: {bash}
+.. code:: bash
 
-   <output_dir>/outs:
+   <output_di>/outs:
    ├── analysis
    ├── atac_cut_sites.bigwig
    ├── atac_fragments.tsv.gz
@@ -49,7 +49,7 @@ sincei. These files can also be produced as part of the
 For convenience, we provide a subset of this data (only chromosome 2)
 `here <>`__
 
-.. code:: {bash}
+.. code:: bash
 
    mkdir 10x_multiome && wget -O 10x_multiome/10x_multiome_testdata.tar.gz https://figshare.com/ndownloader/files/41303289
    tar -xvzf 10x_multiome/10x_multiome_testdata.tar.gz ## releases 7 files
@@ -65,7 +65,7 @@ sincei also allows you to extract per barcode count distributions,
 indicating which barcodes should be removed. This can be done using the
 ``scFilerBarcodes`` tool.
 
-.. code:: {bash}
+.. code:: bash
 
    barcodes=737K-arc-v1.txt # cellranger-arc barcodes in this case
    for r in 1 2
@@ -95,13 +95,13 @@ of the barcode counts.
 2. scATAC-seq analysis
 ----------------------
 
-Please follow - :doc:`this tutorial <sincei_tutorial_10xATAC>` for further analysis of scATAC-seq
+Please follow :doc:`this tutorial <sincei_tutorial_10xATAC>` for further analysis of scATAC-seq
 samples from the above data.
 
 3. scRNA-seq analysis
 ---------------------
 
-Please follow `this tutorial <>`__ for further analysis of scRNA-seq
+Please follow :doc: `this tutorial <sincei_tutorial_10xATAC>` for further analysis of scRNA-seq
 samples from the above data.
 
 4. Joint analysis

docs/content/tutorials/sincei_tutorial_10xATAC.rst

@@ -12,7 +12,7 @@ using sincei (see the parent tutorial for explanation).
 
 Define common bash variables:
 
-.. code:: {bash}
+.. code:: bash
 
    # create dir
    mkdir sincei_output/atac
@@ -39,7 +39,7 @@ quality cells in this data, such as:
 -  high level of secondary/supplementary alignments (filtered using
    ``--samFlagExclude/Include``)
 
-.. code:: {bash}
+.. code:: bash
 
 
    for r in 1 2
@@ -65,7 +65,7 @@ quality cells in this data, such as:
 visualized using the `MultiQC tool <https://multiqc.info/docs/>`__, to
 select appropriate list of cells to include for counting.
 
-.. code:: {bash}
+.. code:: bash
 
    multiqc sincei_output/atac/ # results in multiqc_report.html
 
@@ -85,7 +85,7 @@ count only high quality reads from our whitelist of barcodes.
 We avoid counting reads in blacklisted regions of the human genome
 (–blacklist).
 
-.. code:: {bash}
+.. code:: bash
 
    ## merge intervals from 2 peaks bed files
    for f in cellranger_output_rep*/outs/atac_peaks.bed; do awk '{if(NR>51) {print $0}}' $f >> repmerged.bed; done
@@ -122,7 +122,7 @@ statistics at region and cell level (labelled as .regions.tsv and
 visualized using the `MultiQC tool <https://multiqc.info/docs/>`__, to
 select appropriate metrics to filter out the unwanted cells/regions.
 
-.. code:: {bash}
+.. code:: bash
 
    # list the metrics we can use to filter cells/regions
    for r in 1 2; do scCountQC -i sincei_output/atac/scCounts_atac_peaks_rep${r}.loom --describe; done
@@ -140,7 +140,7 @@ cells with <500 and >10000 detected bins (``--filterRegionArgs``). Also,
 we exclude the regions that are detected in too few cells (<100) or in
 >90% of cells (``--filterCellArgs``).
 
-.. code:: {bash}
+.. code:: bash
 
    for r in 1 2
    do scCountQC -i sincei_output/atac/scCounts_atac_peaks_rep${r}.loom \
@@ -167,7 +167,7 @@ counts can now be combined for downstream analysis. We provide a tool
 common features. Concatenating the filtered cells for the 2 replicates
 would result in a total of >12K cells.
 
-.. code:: {bash}
+.. code:: bash
 
    scCombineCounts \
    -i sincei_output/atac/scCounts_atac_peaks_filtered_rep1.loom \
@@ -183,7 +183,7 @@ would result in a total of >12K cells.
 Finally, we will apply LSA on the filtered dataset to reduce the
 dimentionality to 30 Topics, combined with louvain clustering.
 
-.. code:: {bash}
+.. code:: bash
 
    scClusterCells -i sincei_output/atac/scCounts_atac_peaks_filtered.merged.loom \
    -m LSA --clusterResolution 1 \
@@ -202,7 +202,7 @@ a biologically meaningful way.
 We can color our UMAP output from ``scClusterCells`` with the cell-type
 information based on FACS-sorting from sortChIC.
 
-.. code:: {r}
+.. code:: r
 
    library(dplyr)
    library(magrittr)
@@ -275,7 +275,7 @@ files, except that we can ask for a normalized bulk signal (specified
 using ``--normalizeUsing`` option) . Below, we produce CPM-normalized
 bigwigs with 1kb bins.
 
-.. code:: {bash}
+.. code:: bash
 
    scBulkCoverage -p 20 --cellTag CB --normalizeUsing CPM --binSize 1000 \
    --minMappingQuality 10 --samFlagInclude 64 --samFlagExclude 2048 \

docs/content/tutorials/sincei_tutorial_sortChIC.rst

@@ -188,7 +188,7 @@ We can color our UMAP output from :ref:`scClusterCells` with the cell-type infor
         ggsave(plot=pl, "sincei_output/UMAP_compared_withOrig.png", dpi=300, width = 11, height = 6)
 
 
-.. image:: ./../images/UMAP_compared_withOrig.png
+.. image:: ./../images/UMAP_compared_withOrig_sortChIC.png
    :height: 800px
    :width: 1600 px
    :scale: 50 %