PySCTransform
Python package to perform normalization and variance-stabilization of single-cell data
Install / Use
/learn @saketkc/PySCTransformREADME
============== pySCTranscform
SCTransform for Python - interfaces with Scanpy <https://scanpy.readthedocs.io/en/stable/>_
============= Demo Notebook
See demo <notebooks/demo.ipynb>_.
============= Installation
Using conda
We recommend using conda <https://docs.conda.io/en/latest/>_ for installing pySCTransform.
.. code-block:: bash
conda create -n pysct louvain scanpy pysctransform
conda activate pysct
========== 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
========= CITATION
Choudhary, Saket, and Rahul Satija. "Comparison and evaluation of statistical error models for scRNA-seq." Genome Biology 23.1 (2022): 27.
===== Notes
batch_varis currently not supported
