DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

Clémentine Decamps, Alexis Arnaud, Florent Petitprez, Mira Ayadi, Aurélia Baurès, Lucile Armenoult, N. Alcala, A. Arnaud, F. Avila Cobos, Luciana Batista, A. F. Batto, Y. Blum, F. Chuffart, J. Cros, C. Decamps, L. Dirian, D. Doncevic, G. Durif, S. Y. Bahena Hernandez, M. JakobiR. Jardillier, M. Jeanmougin, P. Jedynak, B. Jumentier, A. Kakoichankava, Maria Kondili, J. Liu, T. Maie, J. Marécaille, J. Merlevede, M. Meylan, P. Nazarov, K. Newar, K. Nyrén, F. Petitprez, C. Novella Rausell, M. Richard, M. Scherer, N. Sompairac, K. Waury, T. Xie, M. A. Zacharouli, Sergio Escalera, Isabelle Guyon, Rémy Nicolle, Richard Tomasini, Aurélien de Reyniès, Jérôme Cros, Yuna Blum, Magali Richard*, HADACA consortium, Petr Nazarov

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)


Background: 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. Results: We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring. Conclusion: DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at:

Original languageEnglish
Article number473
Pages (from-to)473
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - 2 Oct 2021


  • Benchmarking platform
  • Cancer
  • Cellular heterogeneity
  • Deconvolution
  • DNA methylation
  • Omics integration
  • Transcriptome


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