Rachel Cuttat

tinydenseR detects differential cell state density across samples in single-cell data, outperforming existing methods in speed and memory usage.

This study shows that reduced TREM2 proteolytic cleavage in mice increases microglial heterogeneity and accelerates Abeta pathology in Alzheimer's disease.

The paper develops scAR, a machine learning model to remove ambient noise from single-cell omics data across multiple technologies, showing high denoising accuracy.

This study compares cell-type development and transcriptomes in human retinal organoids and adult retinas, finding convergence in transcriptomes and cell-type-specific disease gene expression.

The paper benchmarks scRNA-seq methods and introduces CellSIUS, a tool that improves detection of rare cell populations and identifies their functional signatures in human stem cell differentiation.