Magali Richard

Magali Richard

I am a CNRS researcher in bioinformatics, with a background in molecular biology, genetics, and computational biology. My research focuses on tumor heterogeneity, method development, and cancer biology, and I also promote collaborative science by organizing data challenges.

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Posts

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Blog Post number 1

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portfolio

publications

PenDA, a rank-based method for personalized differential analysis: Application to lung cancer

Published in , 1900

This manuscript describes a novel method, named PenDA, to perform differential analysis of gene expression at the individual level. In PenDA, a gene is considered as deregulated in one sample of interest (e.g., a tumor) if its local ordering relatively to other genes with similar expressions is perturbed compared to its ordering in a set of control samples (e.g., normal tissues).

Recommended citation: Richard M, Decamps C, Chuffart F, Brambilla E, Rousseaux S, Khochbin S, Jost D. PenDA, a rank-based method for personalized differential analysis: Application to lung cancer. PLoS Comput Biol. 2020 May 11;16(5):e1007869. doi: 10.1371/journal.pcbi.1007869. PMID: 32392248; PMCID: PMC7274464.
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Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software

Published in , 1900

This manuscript compares three software packages that infer cell type proportions based on methylation data. We here evaluate key factors affecting performance of deconvolution pipelines. We examine to what extent cell-type proportions can be accurately inferred when accounting for measured confounding factors.

Recommended citation: Decamps, C., Privé, F., Bacher, R. et al. Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software. BMC Bioinformatics 21, 16 (2020). https://doi.org/10.1186/s12859-019-3307-2
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DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

Published in , 1900

Here we propose an innovative public digital benchmarking platform, open source, and freely available for the scientific community, including both high quality benchmarking datasets and reference computational methods. The platform can be used to assess the performance of newly developed methods, which are automatically compared to the existing ones in a user-friendly fashion.

Recommended citation: Decamps C, Arnaud A, Petitprez F, Ayadi M, Baurès A, Armenoult L; HADACA consortium; Escalera S, Guyon I, Nicolle R, Tomasini R, de Reyniès A, Cros J, Blum Y, Richard M. DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification. BMC Bioinformatics. 2021 Oct 2;22(1):473. doi: 10.1186/s12859-021-04381-4. PMID: 34600479; PMCID: PMC8487526.
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Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform

Published in , 1900

We introduce Codabench, a meta-benchmark platform, that is capable of flexible and easy benchmarking and supports reproducibility. Codabench is an important step toward benchmarking and reproducible research. It has been used in various communities including graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning. Codabench is ready to help trendy research, e.g., artificial intelligence (AI) for science and data-centric AI.

Recommended citation: Xu Z, Escalera S, Pavão A, Richard M, Tu WW, Yao Q, Zhao H, Guyon I. Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform. Patterns (N Y). 2022 Jun 24;3(7):100543. doi: 10.1016/j.patter.2022.100543. PMID: 35845844; PMCID: PMC9278500.
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Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma

Published in , 1900

To allow rapid PDAC molecular subtyping and study PDAC heterogeneity, we develop PACpAInt, a multi-step deep learning model. PACpAInt correctly predicts tumor subtypes at the whole slide level on surgical and biopsies specimens and independently predicts survival. PACpAInt highlights the presence of a minor aggressive Basal contingent that negatively impacts survival in 39% of RNA-defined classical cases.

Recommended citation: Saillard, C., Delecourt, F., Schmauch, B. et al. Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma. Nat Commun 14, 3459 (2023). https://doi-org.ins2i.bib.cnrs.fr/10.1038/s41467-023-39026-y
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DECOMICS, a shiny application for unsupervised cell type deconvolution and biological interpretation of bulk omic data

Published in , 1900

Our article presents a user-friendly Shiny application for estimating and identifying cell type composition from bulk transcriptomes using unsupervised approaches. Additionally, the application offers guidance for conducting analyses and interpreting the biological implications of the results.

Recommended citation: Slim Karkar, Ashwini Sharma, Carl Herrmann, Yuna Blum, Magali Richard, DECOMICS, a shiny application for unsupervised cell type deconvolution and biological interpretation of bulk omic data<\b>, Bioinformatics Advances, Volume 4, Issue 1, 2024, vbae136, https://doi.org/10.1093/bioadv/vbae136
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AI Competitions and Benchmarks, Practical issues: Proposals, grant money, sponsors, prizes, dissemination, publicity

Published in , 1900

This book explains how AI competitions and benchmarks are created, run, and used. It brings together lessons from experienced organizers in academia, industry, and non-profits. Covering topics like datasets, evaluation, platforms, and incentives, it shows how challenges drive research, education, and innovation. Designed for researchers, engineers, and organizers, it is a practical guide to understanding and building impactful AI competitions : book URL

Recommended citation: Richard, M., Blum, Y., Guinney, J., Stolovitzky, G., & Pavão, A. (2024). AI Competitions and Benchmarks, Practical issues: Proposals, grant money, sponsors, prizes, dissemination, publicity. arXiv preprint arXiv:2401.04452.
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hdmax2, an R package to perform high dimension mediation analysis

Published in , 1900

This manuscript introduces HDMAX2, a statistical method and R package developed to conduct mediation analysis in high-dimensional settings.

Recommended citation: Jumentier, Aurélie Nakamura, Johanna Lepeule, Olivier François and Magali Richard (2025) hdmax2, an R package to perform high dimension mediation analysis, Peer Community Journal, 5: e107.
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Redefining phenotypic intratumor heterogeneity of pancreatic ductal adenocarcinoma: a bottom-up approach

Published in , 1900

Herein, we developed a panel of antibodies that could easily be used by researchers and pathologists. The purpose of this panel was to classify patients according to the two main subtypes of PDAC, roughly basal-like or classical. To achieve this, we selected markers through a stringent and multistep process.

Recommended citation: Hilmi M, Delecourt F, Raffenne J, Bourega T, Dusetti N, Iovanna J, Blum Y, Richard M, Neuzillet C, Couvelard A, Tihy M, de Mestier L, Rebours V, Nicolle R, Cros J. Redefining phenotypic intratumor heterogeneity of pancreatic ductal adenocarcinoma: a bottom-up approach. J Pathol. 2025 Apr;265(4):448-461. doi: 10.1002/path.6398. Epub 2025 Feb 11. PMID: 39935174; PMCID: PMC11880971.
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Group lasso based selection for high-dimensional mediation analysis

Published in , 1900

This paper presents a two-step procedure for high-dimensional mediation analysis. The first step selects a reduced number of candidate mediators using an ad-hoc lasso penalty. The second step applies a procedure we previously developed to estimate the mediated and direct effects, accounting for the correlation structure among the retained candidate mediators.

Recommended citation: Jérolon, A., Alarcon, F., Pittion, F., Richard, M., François, O., Birmelé, E. E., & Perduca, V. (2024). Group lasso based selection for high-dimensional mediation analysis. arXiv preprint arXiv:2409.20036.
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DNA methylation and immune infiltration mediate the impact of tobacco exposure on pancreatic adenocarcinoma outcome: a high-dimensional mediation analysis

Published in , 1900

In this work, we developed HDMAX2-surv, a novel framework for high-dimensional mediation analysis specifically adapted to censored survival data. Our approach integrates computational immune deconvolution with causal discovery and serial mediation analysis, addressing a critical methodological gap in understanding how molecular intermediates shape clinical outcomes.

Recommended citation: DNA methylation and immune infiltration mediate the impact of tobacco exposure on pancreatic adenocarcinoma outcome: a high-dimensional mediation analysis. Florence Pittion, Elise Amblard, Emilie Devijver, Adeline Samson, Nelle Varoquaux, Magali Richard. bioRxiv 2025.09.09.675033; doi: https://doi.org/10.1101/2025.09.09.675033
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A robust workflow to benchmark deconvolution of multi-omic data

Published in , 1900

This manuscript presents a comprehensive and unbiased evaluation framework for benchmarking deconvolution algorithms across transcriptomic and methylomic data, addressing critical gaps in existing studies and providing key advances in the quantification of tumor heterogeneity from bulk molecular data.

Recommended citation: Amblard, E., Bertrand, V., Barbot, H. et al. A robust workflow to benchmark deconvolution of multi-omic data. Genome Biol 26, 429 (2025). https://doi.org/10.1186/s13059-025-03897-9
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How to organise a scientific competition to benchmark methods and algorithms in computational biology?

Published in , 1900

This paper provides a comprehensive guide for organizing scientific competitions in bioinformatics, based on our experience with HADACA3, a data challenge focused on deconvolution algorithms for predicting cellular composition in cancer, from multi-omics data.

Recommended citation: Nicolas Homberg, Lucie Lamothe, Elise Amblard, Hugo Barbot, Morgane Térézol, et al. How to organise a scientific competition to benchmark methods and algorithms in computational biology? 2026. ⟨hal-05445539⟩
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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