edger

Brings bulk and pseudobulk transcriptomics to the tidyverse

Statistical Analysis of RNA-Seq Tools

Native R and Python sample-deletion robustness for omics: candidate certificates, minimum deletion bounds, Bioconductor model refits and donor-level pseudobulk

This RNAseq data analysis tutorial is created for educational purpose

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

A quick recap of widely used differential analyses methods in R for RNA-seq experiments

Differential expression analysis: DESeq2, edgeR, limma. Realized in python based on rpy2

Various tutorials on how to analyse transcriptomic data.

Generate HTML report for a set of genomic regions or DESeq2/edgeR results

Probabilistic outlier identification for bulk RNA sequencing data

Rosetta Stone between R and Python for bio

Some of the analytical processes and tools we use to provide rigorous and actionable results to our clients.

Differential expression analysis of miRNAs using edgeR and validated target retrieval with multiMiR.

Some of the analytical processes and tools we use to provide rigorous and actionable results to our clients.

RNA-Seq Data Analysis in R: From Raw Counts to Differential Expression Analysis

A R-based analysis of the effect of Cystic Fibrosis on the lung fibroblast transcriptome in humans.

Parallel companions for five RNA-seq functions that are slow at cohort scale and have no worker argument: sva::ComBat_seq, edgeR::normLibSizes, limma::lmFit, limma::duplicateCorrelation and limma::removeBatchEffect. Each runs the original unmodified with its work split across workers and returns output identical() to the original's.

Enhanced volcano plot using edgeR data

This project provides a reproducible workflow for bulk RNA-Seq data analysis, including preprocessing, quality control, differential expression analysis, and visualization using edgeR and limma-voom. It focuses on evaluating pipeline performance while analyzing RNA-Seq data from a case study on mouse mammary gland gene expression.

Web-based R Shiny application for multi-method RNA-seq differential expression analysis