bioRxivBioinformaticsPreprint
Differential analysis of genomics count data with edge*
Abstract
The edgeR Bioconductor package is one of the most widely used tools for differential expression analysis of count-based genomics data. Despite its popularity, the R-only implementation limits its integration with the Python-centric ecosystem that has become dominant in single-cell genomics. We present edgePython, a Python port of edgeR 4.8.2 that extends the framework with a negative binomial-gamma mixed model for multi-subject single-cell analysis and empirical Bayes shrinkage of cell-level dispersion.
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