scATrans

scATrans is a single-cell differential-analysis toolkit with an RNA-velocity twist: standard DE selects the changed genes, then scATrans partitions them by mechanism — transcription-driven vs stabilization-driven — from the nascent (unspliced) signal, a call a fold-change alone cannot make.

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.

📚 Documentation, tutorials, and API reference: Read the Docs.

Installation

pip install scatrans
# or: conda install -c conda-forge -c bioconda scatrans

Optional extras (scVelo, gene features, PyDESeq2, Memento, GSEA) and a source / editable dev setup are covered in the Installation guide.

Quickstart

import scatrans as scat

# DE selects the changed genes; scATrans partitions them by MECHANISM.
result = scat.partition_de_by_mechanism(
    adata,
    groupby="condition", target_group="Disease", reference_group="Control",
    organism="mouse",
    de="builtin",            # or a DE method name / precomputed DE table / callable
    gene_sets=my_pathways,   # optional -> program-level mechanism table
)
result.regime      # reliability pre-flight
result.selected    # DE-selected genes + per-gene mechanism annotation
result.programs    # decisive program-level transcription-vs-stabilization calls

See the Quickstart, Tutorials, and User Guide for the full workflow, DE backends, enrichment, plotting, and reporting guidance (Statistical Guidance).

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.

Author

Zhao Li (李钊)
Email: leelieber@gmail.com

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