Installation

# Basic installation
pip install scatrans

# With support for scVelo-based advanced mode and the gene feature generation CLI
pip install "scatrans[advanced,gene_features]" gseapy

# With support for pseudobulk differential expression using PyDESeq2
pip install "scatrans[pseudobulk]"

# Optional: Memento (Cell 2024) as an additional cell-level DE backend
pip install "scatrans[memento]"

The package ships precomputed gene feature tables (gene length + intron number) for both mouse and human. These are used for optional bias correction in active_score. You can also supply custom tables (e.g. from your own GTF) — see Gene Feature Attachment & CLI.

Install from source

git clone https://github.com/leelieber2025/scATrans.git
cd scATrans
pip install -e ".[dev]"

Logging

The package logs under the name scatrans. You can control verbosity with:

import logging
logging.getLogger("scatrans").setLevel(logging.INFO)

Quick data quality check

Before analysis, inspect the global unspliced fraction:

import scatrans as scat
ufrac = scat.qc.unspliced_global(adata)   # logs INFO + WARNING if > 50%

active_score automatically runs this check and records the value in diagnostics.