A robust workflow to benchmark deconvolution of multi-omic data

Our results highlight the strengths and limitations of various algorithms, and provides practical guidance for selecting the best method based on data type and analysis context. This benchmark sets a new standard for evaluating deconvolution methods and analysing tumour heterogeneity.

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Publication Details

Journal
Genome biology
Published
2025-12-17
DOI
https://doi.org/10.1186/s13059-025-03897-9
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

A robust workflow to benchmark deconvolution of multi-omic data

Florent Chuffart, Mira Ayadi, Vincent Bertrand, David Causeur et al.
Genome biology
Radiomics and Machine Learning in Medical Imaging
article

A robust workflow to benchmark deconvolution of multi-omic data

Florent Chuffart, Mira Ayadi, Vincent Bertrand, David Causeur, Jérôme Cros, Slim Karkar, Aurélia Baurès, Elise Amblard, Yasmina Kermezli, Lucile Armenoult, Magali Richard, Luis Manuel Peña, Yuna Blum, Hugo Barbot
article en

Abstract

Our results highlight the strengths and limitations of various algorithms, and provides practical guidance for selecting the best method based on data type and analysis context. This benchmark sets a new standard for evaluating deconvolution methods and analysing tumour heterogeneity.

Genome biologyVol. 26(1)
Institut polytechnique de Grenoble (FR), Centre National de la Recherche Scientifique (FR), Université de Bordeaux (FR), Inserm (FR), Université Paris Cité (FR), Hôpital Beaujon (FR), Techniques of Informatics and Microelectronics for Integrated Systems Architecture (FR), La Ligue Contre le Cancer (FR), Assistance Publique – Hôpitaux de Paris (FR), Laboratoire d'Informatique de Grenoble (FR), Institut de Biochimie et Génétique Cellulaires (FR), Institut pour l'avancée des biosciences (FR), Institut de recherche mathématique de Rennes (FR), Université de Rennes (FR), Université Grenoble Alpes (FR)
Agence Nationale de la Recherche, Institut National de la Santé et de la Recherche Médicale, EIT Health
Openalex Percentile: Top 45%
Radiomics and Machine Learning in Medical Imaging
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