Tumoral heterogeneity and multi-omics data integration

Tumour heterogeneity analysis by deconvolution and multi-omic data integration to elucidate cancer phenotypes

To evaluate the impact of gene expression on tumour progression and treatment resistance, clinicians regularly acquire bulk transcriptomic data from tumours. The downside of this popular technique is that the the heterogeneity of cell populations that compose tumoral samples is "hidden" in the bulk sequencing data.  Indeed, bulk RNAseq provides information on average gene expression throughout the sample compared to cell-specific expression acquired with single cell sequencing.

In this project we investigate different methods that allow to quantify cell-type specific expression from bulk RNAseq data using deconvolution techniques.

Logos des tutelles et de l'institut accueillant le Computational Biology and Bioinformatics Lab de bordeaux