differential-expression-analysis

A Snakemake workflow and MrBiomics module for performing and visualizing differential analyses of NGS data powered by the R package limma.

A Snakemake workflow and MrBiomics module for performing differential expression analyses (DEA) on (multimodal) sc/snRNA-seq data powered by the R package Seurat.

Differential Expression Analysis tool box R lang package for omics data

An R package to plot interactive three-way differential expression analysis

R package that performs sparse factor analysis and differential gene expression discovery simultaneously on single-cell CRISPR screening data.

RNA-seq Analysis Pipeline Testing and Optimization Resource - Intelligent pipeline selection and comprehensive benchmarking.

Multi-omics module that includes RNAseq, Epigenetics, and integrated multi-omics analyses developed as part of the NIGMS Sandbox project

Approaches to identify Differential Rhythmicity

A comprehensive quality-control and quantification RNA-seq pipeline

Software for intuitively doing Differential Gene Expression (DGE) analysis on Windows and GNU\Linux, based on R packages.

An awesome Oxford Nanopore Pipeline for direct RNA-sequencing

Material used for a course on transcriptomics, covering transcriptome assembly, transcriptome functional annotation, differential expression analysis, and functional annotation data mining

Consensus pathway analysis module developed by the University of Nevada at Reno

A library for differential expression and gene set enrichment analysis based on hydra and pydantic.

crispyx is a Python package for scalable CRISPR and Perturb-seq screen analysis on disk-backed AnnData files. It provides streaming quality control, normalization, pseudobulk aggregation, and differential expression without loading the full count matrix into memory.

An R/Bioconductor package for DamID differential binding, gene transcription and chromatin accessibility analysis.

A Python package for mechanism-aware single-cell differential analysis. It partitions DE genes into transcription-driven versus stabilization-driven classes using nascent (unspliced) RNA residuals. Also supports conventional DE, functional enrichment (ORA, GSEA, GO, KEGG), and visualization.

GWAS of Postmortem Brain Samples Sheds Light on the Development of Schizophrenia and Bipolar Disorder

Performing RNA-Seq data analysis and quality control using DESeq2 in R.