1551885470861

Raphael Bacher

and 18 more

IntroductionState of the art for cancer heterogeneity deconvolution from methylation dataset+ limitation for state of the arts methodsState of the art for benchmarkingeg :eg :eg :eg :AN EXAMINATION OF PROCEDURES FOR DETERMINING THE NUMBER OF CLUSTERS IN A DATA SET (Milligan et al.)Comprehensive benchmarking and ensemble approaches for metagenomic classifiers (McIntyre et al.)A comprehensive database for benchmarking imaging systems (Panetta et al.)--> Importance of a robust database--> Importance of reliable metrics to evaluate the different  tested approachesState of the art for data challenge organization (DREAM challenge, MVA Master yearly challenge, AMPS Hackathon, Codalab platform eg)CDS Saclay https://arxiv.org/abs/1705.07099SSMPG 2015 Aussois https://www.biorxiv.org/content/early/2016/05/24/055046State of the art for data challenge organization (DREAM challenge, MVA Master yearly challenge, AMPS Hackathon, Codalab platform eg)- Ghouila A, Genome Research, 2018- Gönen M, Cell Systems, 2017 (DREAM challenge) https://doi.org/10.1016/j.cels.2017.09.004- Seyednasrollah F, JCO Clinical Cancer Informatic, 2018 (DREAM challenge)Material and methodsData Challenge organisation+  program, location+   Amount of participants: 34 from 5 different countries and from different backgrounds (bioinfo/applied maths/statisticians/computer science)+ The challenge existed of two parts+   Amount of participants: 34 from 5 different countries and from different backgrounds (bioinfo/applied maths/statisticians/computer science) + Short courses on biology & statistics+ Invited speakers to present methods Data Challenge platform+ CodalabThe organization set-up a platform to share the original simulated DNA methylation, uploading and running scripts+ Limitations / requirements: 3 minutes running code challenge 1, RData object in challenge 2 phase 1, Running time 20 minutes slightly more noise in phase 2, Goal / introduction dataData Challenge simulated dataset and scoring metric+ Simulated dataset 1+ Simulated dataset 2+ scoring metric (MAE type 1, MAE type 2)Statistical methods for confounders consideration and feature selectionStatistical methods for k determinationStatistical methods for deconvolution