https://github.com/saketkc/pysctransform

https://github.com/saketkc/pysctransform

single-cell-rna-seq

Last synced: 24 days ago
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• Host: GitHub
• URL: https://github.com/saketkc/pysctransform
• Owner: saketkc
• License: other
• Created: 2020-09-11T05:01:27.000Z (about 4 years ago)
• Default Branch: develop
• Last Pushed: 2022-08-09T00:59:23.000Z (about 2 years ago)
• Last Synced: 2023-03-12T08:18:19.540Z (over 1 year ago)
• Topics: single-cell-rna-seq
• Language: Jupyter Notebook
• Homepage:
• Size: 4.08 MB
• Stars: 12
• Watchers: 2
• Forks: 6
• Open Issues: 3
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE

Awesome Lists containing this project

README

        
==============

pySCTranscform

==============

SCTransform for Python - interfaces with `Scanpy `_

=============

Demo Notebook

=============

See `demo `_.

=============

Installation 

=============

Using conda

-------------

We recommend using `conda `_ for installing pySCTransform.

.. code-block:: bash

    conda create -n pysct louvain scanpy

    conda activate pysct

    pip install git+https://github.com/saketkc/pysctransform.git@glmgp

If you would like to use `glmGamPoi `_, a faster estimator, ``rpy2`` and ``glmGamPoi`` need to be installed as well:

.. code-block:: bash

    conda create -n pysct louvain scanpy rpy2 bioconductor-glmgampoi

    conda activate pysct

    pip install git+https://github.com/saketkc/pysctransform.git

==========

Quickstart

==========

.. code-block:: python

    import scanpy as sc

    from pysctransform import SCTransform

   

    pbmc3k = sc.read_h5ad("./pbmc3k.h5ad")

    # Get pearson residuals for 3K highly variable genes

    residuals = SCTransform(pbmc3k, var_features_n=3000)

    pbmc3k.obsm["pearson_residuals"] = residuals

    # Peform PCA on pearson residuals

    pbmc3k.obsm["X_pca"] = sc.pp.pca(pbmc3k.obsm["pearson_residuals"])

    # Clustering and visualization

    sc.pp.neighbors(pbmc3k, use_rep="X_pca")

    sc.tl.umap(pbmc3k, min_dist=0.3)

    sc.tl.louvain(pbmc3k)

    sc.pl.umap(pbmc3k, color=["louvain"], legend_loc="on data", show=True)

    

.. image:: https://raw.githubusercontent.com/saketkc/pySCTransform/develop/notebooks/output_images/pbmc3k_pysct.png

    :target: https://github.com/saketkc/pySCTransform/blob/develop/notebooks/demo.ipynb 

  

.. code-block:: python

    # Perform variance stabilization using 'v2' regularization

    from pysctransform import vst

    from pysctransform.plotting import plot_residual_var

    vst_out_3k = vst(umi = pbmc3k.X.T,

                     gene_names=pbmc3k.var_names.tolist(),

                     cell_names=pbmc3k.obs_names.tolist(),

                     method="fix-slope",

                     exclude_poisson=True

                    )

    plot_residual_var(vst_out_3k)

    

    

.. image:: https://raw.githubusercontent.com/saketkc/pySCTransform/develop/notebooks/output_images/pysct_glmgp_residvar.png

    :target: https://github.com/saketkc/pySCTransform/blob/develop/notebooks/demo.ipynb 

=====

Notes

=====

* ``batch_var`` is currently not supported