Brings bulk and pseudobulk transcriptomics to the tidyverse

Statistical Analysis of RNA-Seq Tools

This RNAseq data analysis tutorial is created for educational purpose

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

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

Various tutorials on how to analyse transcriptomic data.

Probabilistic outlier identification for bulk RNA sequencing data

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.

Enhanced volcano plot using edgeR data

🧬Custom Bulk RNA-seq workflow for QC, DESeq2 differential expression, and visualization in R 🧬 (RNA-seq, bioinformatics, bulk-rnaseq, visualization, DESeq2, edgeR)

Sea lion urine comparison with spectral counting.

Galaxy wrappers for SARTools (Statistical Analysis of RNA-Seq Tools)

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.

Analyses combining ATAC-seq, RRBS, and RNA-seq data for purple urchins

some scripts to process transcriptomics and Chip-seq data

Rstudio files created during my internship at IAB. Internship supervised by Florent Chuffart.

Singularity image w/ R-libraries for analysing DNA methylation alterations