DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.
Benchmarking platform
Cancer
Cellular heterogeneity
DNA methylation
Deconvolution
Omics integration
Transcriptome
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
02 Oct 2021
02 Oct 2021
Historique:
received:
30
10
2020
accepted:
20
09
2021
entrez:
3
10
2021
pubmed:
4
10
2021
medline:
6
10
2021
Statut:
epublish
Résumé
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. 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. 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 .
Sections du résumé
BACKGROUND
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
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
CONCLUSIONS
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 .
Identifiants
pubmed: 34600479
doi: 10.1186/s12859-021-04381-4
pii: 10.1186/s12859-021-04381-4
pmc: PMC8487526
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
473Subventions
Organisme : Université Grenoble Alpes
ID : ANR-15-IDEX-02
Organisme : EIT Health
ID : activities 19359
Organisme : EIT Health
ID : 20377
Investigateurs
N Alcala
(N)
A Arnaud
(A)
F Avila Cobos
(F)
Luciana Batista
(L)
A-F Batto
(AF)
Y Blum
(Y)
F Chuffart
(F)
J Cros
(J)
C Decamps
(C)
L Dirian
(L)
D Doncevic
(D)
G Durif
(G)
S Y Bahena Hernandez
(SY)
M Jakobi
(M)
R Jardillier
(R)
M Jeanmougin
(M)
P Jedynak
(P)
B Jumentier
(B)
A Kakoichankava
(A)
Maria Kondili
(M)
J Liu
(J)
T Maie
(T)
J Marécaille
(J)
J Merlevede
(J)
M Meylan
(M)
P Nazarov
(P)
K Newar
(K)
K Nyrén
(K)
F Petitprez
(F)
C Novella Rausell
(C)
M Richard
(M)
M Scherer
(M)
N Sompairac
(N)
K Waury
(K)
T Xie
(T)
M-A Zacharouli
(MA)
Informations de copyright
© 2021. The Author(s).
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