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PySCTransform

Python package to perform normalization and variance-stabilization of single-cell data

Install / Use

/learn @saketkc/PySCTransform
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

============== 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_var is currently not supported
View on GitHub
GitHub Stars28
CategoryDevelopment
Updated13d ago
Forks14

Languages

Jupyter Notebook

Security Score

75/100

Audited on Mar 25, 2026

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