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

Abstract 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 sampl...

Full description

Saved in:

Bibliographic Details
Published in:	BMC bioinformatics Vol. 22; no. 1; pp. 1 - 473
Main Authors:	Decamps, Clémentine, Arnaud, Alexis, Petitprez, Florent, Ayadi, Mira, Baurès, Aurélia, Armenoult, Lucile, Alcala, N., Arnaud, A., Avila Cobos, F., Batista, Luciana, Batto, A.-F., Blum, Y., Chuffart, F., Cros, J., Decamps, C., Dirian, L., Doncevic, D., Durif, G., Bahena Hernandez, S. Y., Jakobi, M., Jardillier, R., Jeanmougin, M., Jedynak, P., Jumentier, B., Kakoichankava, A., Kondili, Maria, Liu, J., Maie, T., Marécaille, J., Merlevede, J., Meylan, M., Nazarov, P., Newar, K., Nyrén, K., Petitprez, F., Novella Rausell, C., Richard, M., Scherer, M., Sompairac, N., Waury, K., Xie, T., Zacharouli, M.-A., Escalera, Sergio, Guyon, Isabelle, Nicolle, Rémy, Tomasini, Richard, de Reyniès, Aurélien, Cros, Jérôme, Blum, Yuna, Richard, Magali
Format:	Journal Article
Language:	English
Published:	London BioMed Central 02-10-2021 BMC
Subjects:	Adenocarcinoma Algorithms Benchmarking platform Benchmarks Cancer Cellular heterogeneity Computer applications Crowdsourcing Datasets Deconvolution DNA methylation Heterogeneity Life Sciences Methods Mimicry Omics integration Performance evaluation Quantitative Methods Software Source code Transcriptome Transcriptomes Tumors Cellular heterogeneity Deconvolution Benchmarking platform Transcriptome DNA methylation Omics integration Cancer
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Abstract 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: https://competitions.codalab.org/competitions/27453 .
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-021-04381-4