%0 Journal Article
%T DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification
%A Clementine Decamps
%A Alexis Arnaud
%A Florent Petitprez
%A Mira Ayadi
%A Aurelia Baures
%A Lucile Armenoult
%A Sergio Escalera
%A Isabelle Guyon
%A Remy Nicolle
%A Richard Tomasini
%A Aurelien de Reynies
%A Jerome Cros
%A Yuna Blum
%A Magali Richard
%J BMC Bioinformatics
%D 2021
%V 22
%F Clementine Decamps2021
%O HUPBA; no proj
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3650), last updated on Thu, 27 Jan 2022 12:30:05 +0100
%X Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data.
%U https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04381-4
%U http://refbase.cvc.uab.es/files/DAP2021.pdf
%P 473