PyDESeq2

Differential expression analysis for RNA-seq data using negative binomial generalized linear models with size factor normalization and empirical Bayes shrinkage.

2 shared topics • 2 shared operations

Use when performing differential gene expression analysis on RNA-seq count data using limma-voom. Covers the full workflow: count filtering with filterByExpr, TMM normalization via edgeR calcNormFacto

2 shared topics • 2 shared operations

Use when working with nf-core/differentialabundance — a reproducible Nextflow pipeline for differential abundance analysis of count data from RNA-seq, ATAC- seq, proteomics, or any feature-by-sample c

2 shared topics • 2 shared operations

scde is a Bioconductor R package for Bayesian single-cell differential expression analysis using mixture models. It fits a per-cell error model (negative binomial + Poisson noise) to raw count data, t

2 shared topics • 2 shared operations

tximport -- R/Bioconductor package for importing transcript-level abundance, estimated counts, and transcript lengths from quantification tools (Salmon, Kallisto, RSEM, StringTie, Sailfish) and summar

1 shared topic • 3 shared operations