Matrix factorization and deconvolution methods to quantify tumor heterogeneity in cancer research

Successful treatment of cancer is still a challenge and this is partly due to a wide heterogeneity of cancer composition across patient population. Unfortunately, accounting for such heterogeneity is very difficult. Clinical evaluation of tumor heterogeneity often requires the expertise of anatomical pathologists and radiologistes.

This challenge will be dedicated to the quantification of intra-tumor heterogeneity using appropriate statistical methods on cancer omics data.

In particular, it will focus on estimating cell types and proportion in biological samples based on averaged DNA methylation and full patient history.

The goal is to explore various statistical methods for source separation/deconvolution analysis (Non-negative Matrix Factorization, Surrogate Variable Analysis, Principal component Analysis, Latent Factor Models, ...). Participants will be made aware of several pitfalls when analyzing omics data (large datasets, missing data, confounding factors…).

Participants will work in interdisciplinary teams.

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