Aurélien de Reyniès

fastCNV predicts copy number variations from spatial transcriptomics and scRNA-seq data, showing high accuracy and speed improvements over existing methods.

This paper develops a deep learning model to predict molecular subtypes and outcomes in muscle-invasive bladder cancer using histopathological images instead of RNA sequencing.

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

DECONbench benchmarks deconvolution methods for quantifying tumor cell-type heterogeneity by providing datasets, tools, and performance evaluation.

The paper introduces mMCP-counter, a method to quantify immune and stromal cell populations in murine samples using gene expression, showing it outperforms existing methods and predicts treatment response.

The paper introduces a stabilized ICA method that improves reproducibility and interpretability of transcriptomic signals across cancer datasets, and applies it to colorectal cancer.

MS.liverK is an R package that implements tools for computing molecular subtypes and functional signatures in liver cancer using transcriptome data.

SMAP improves sequencing analysis of xenografts by accurately mapping reads to both host and graft genomes and enhancing fusion transcript detection.