@Article{ClementineDecamps2021,
author="Clementine Decamps
and Alexis Arnaud
and Florent Petitprez
and Mira Ayadi
and Aurelia Baures
and Lucile Armenoult
and Sergio Escalera
and Isabelle Guyon
and Remy Nicolle
and Richard Tomasini
and Aurelien de Reynies
and Jerome Cros
and Yuna Blum
and Magali Richard",
title="DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification",
journal="BMC Bioinformatics",
year="2021",
volume="22",
pages="473",
abstract="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.",
optnote="HUPBA; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3650), last updated on Thu, 27 Jan 2022 12:30:05 +0100",
opturl="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04381-4",
file=":http://refbase.cvc.uab.es/files/DAP2021.pdf:PDF"
}