Associate Team presentation
The SWAGR Associate Team is part of the Inria@SiliconValley program, and aims at bringing together a statistical workforce for advanced genomics using RNAseq.
SWAGR brings together the expertise of:
in an effort to improve RNAseq data analysis methods by developing a flexible, robust, and mathematically principled framework for detecting differential gene expression.
Gene expression, measured through the RNAseq technology, has the potential of revealing deep and complex biological mechanisms underlying human health. However, there is currently a critical limitation in widely adopted approaches for the analysis of such data, as edgeR, DESeq2 and limma-voom can all be shown to fail to control the type-I error, leading to an inflation of false positives in analysis results. This problem is exacerbated when studying single-cell RNA-seq data where sample sizes are much larger due to the finer cellular resolution. False positives are an important issue in all of science. In particular in biomedical research when costly studies are failing to reproduce earlier results, this is a pressing issue.
- Doubly-robust evaluation of high-dimensional surrogate markers (preprint arXiv:2012.01236)
- crossurr R package (beta)
- ccdf R package (beta)
- dearseq: a variance component score test for RNA-seq differential analysis that effectively controls the false discovery rate (Gauthier, Agniel, Thiébaut & Hejblum, NAR Genomics and Bioinformatics, 2020)
- dearseq R package on Bioconductor
- Time-course gene set analysis of longitudinal RNA-seq data (Agniel & Hejblum, Biostatistics, 2017)
- tcgsaseq R package on CRAN
- develop a rigorous statistical framework modeling complex transcriptomic studies using RNAseq, including bulk and single-cell studies
- implement an open-source software as a Bioconductor R package, and a user friendly web-application will be made available to help dissemination
- analyze clinical studies to yield significant biological results, in particular in vaccine trials