scatrans 0.9.9.dev2

Single-cell Active Transcription Analysis

scATrans

scATrans is a Python toolkit for single-cell differential analysis. It is primarily designed for datasets that contain spliced/unspliced (or mature/nascent) RNA layers. In this setting it computes a composite active transcription score that integrates differential expression with reference-based excess unspliced RNA to rank genes.

It also supports conventional differential expression workflows (no velocity data required) using scanpy, PyDESeq2 pseudobulk, linear mixed models, or optional Memento. Functional enrichment (ORA, GSEA, GO, KEGG) uses bundled gene sets with consistent universe handling, and a set of visualization functions is provided.

📚 Full documentation, tutorials, and the complete API reference are on Read the Docs: https://scatrans.readthedocs.io

Installation

pip install scatrans

# Optional extras: advanced (scVelo) mode, pseudobulk DE (PyDESeq2), Memento, GSEA
pip install "scatrans[advanced,gene_features,pseudobulk]" gseapy

See Installation for extras, source installs, and logging setup.

Quickstart

import scatrans as scat

# One-liner pipeline: score → filter → GO enrichment
result = scat.run_default_pipeline(
    adata,
    groupby="condition",
    target_group="Disease",
    reference_group="Control",
    sample_col="sample",   # optional; auto-selects pseudobulk when >=3 replicates/group
    organism="mouse",
)
print(result["candidates"].head())
print(result["enrichment"].head())

See the Quickstart for a complete end-to-end walkthrough, the Tutorials for fully worked, real-data notebooks (with and without RNA-velocity layers), and the User Guide for DE backends, enrichment, plotting, and advanced options.

Before reporting results in a paper

active_score is a composite heuristic rank, not a p-value or FDR on its own. See Statistical Guidance for what each output column means, safe vs. unsafe uses, and a reporting checklist before you cite scATrans results in a manuscript or supplement.

License

Software (Python source) is licensed under Apache License 2.0. Bundled gene-set data (GO, KEGG) carries its own licensing terms — see License before commercial use.