Elise Amblard

Routine FFPE sections support clinically compatible single-nucleus transcriptomics across six human cancer types

This paper develops a workflow for single-nucleus transcriptomics from FFPE tumor samples, showing reproducible cellular composition analysis across six cancer types.

A robust workflow to benchmark deconvolution of multi-omic data

This paper benchmarks deconvolution algorithms for multi-omic data, identifying their strengths and limitations across different conditions and data types.

HDMAX2-surv: high-dimensional mediation analysis of survival data with application to pancreatic cancer

HDMAX2-surv identifies epigenetic mediators of survival outcomes in pancreatic cancer using a two-step framework with latent factor modeling and survival models.

Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data

This paper shows that reducing hubness in single-cell RNA-seq data improves clustering and trajectory inference compared to traditional dimensionality reduction methods.