Health Data Challenge: Matrix factorization and deconvolution methods to quantify tumor heterogeneity in cancer research

Dear all, Registration is now open for the Health data challenge (2nd edition): "Matrix factorization and deconvolution methods to quantify tumor heterogeneity in cancer », which will take place form November 25, 2019, to November 29, 2019, in Aussois (French Alps): This challenge will be dedicated to the quantification of intra-tumor heterogeneity using appropriate statistical methods on (DNA) methylome and transcriptomic data in cancer. In particular, it will focus on estimating cell types and proportion in biological samples (in vivo and in silico mixtures) for which transcriptome and/or methylome profiles have been generated. 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, different type of technologies/omics…). This challenge will also be a unique opportunity to compare the performance of deconvolution methods between transcriptome and methylome data, which might have a great impact on clinical practice. Please find further information here (detailed program, registration, location...)
Date de début
Date de fin
Lieu

Aussois
France

Type d'événement
Colloque