Author

Félix Raimundo

Google (United States)

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3 Immunology· 1

Signature Assays, Organisms & Methods

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Assay:scRNA-seq· 3 Research Focus:dimensionality reduction· 2 Approach:statistical model· 2 Assay:ATAC-seq· 1 Research Focus:batch correction / integration· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1

Frequent Co-Authors

Céline VallotiD· 3 shared Jean‐Philippe VertiD· 3 shared A. Bolon· 1 shared Adeline PageiD· 1 shared Audrey Merienne· 1 shared Benjamin GilletiD· 1 shared C. Dubois· 1 shared Célia GardetiD· 1 shared

Activity by Year

2020-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBioinformatics

Céline VallotORCID

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Author profile →

4 papers

Recent·Most cited

Targeting a shared neoepitope derived from non-canonical translation of c-MYC oncogene in cancer cells

Estelle Baulu, A. Bolon, Ema Etchegaray, Félix Raimundo, Audrey Merienne, Juliette Martin, J. Grandsire, T. Richard, Laurie Tonon, C. Dubois, Yann Estornes, Rasha E. Boulos, Olivier Tabone, Paola Bonaventura, Adeline Page, , Stéphane Depil

The paper identifies a shared neoepitope from non-canonical c-MYC translation in cancer cells that activates T cells to kill tumors without harming normal cells.

bioRxiv · May 23, 2024 · PreprintImmunology

Best practices for single-cell histone modification analysis

Félix Raimundo, Pacôme Prompsy, Jean‐Philippe Vert, Céline Vallot

The paper benchmarks scHPTM analysis pipelines to identify best practices for experimental design and data analysis.

bioRxiv · Sep 21, 2022 · PreprintBioinformatics

Machine learning for single cell genomics data analysis

Félix Raimundo, Laetitia Meng-Papaxanthos, Céline Vallot, Jean‐Philippe Vert

This review surveys recent machine learning approaches for analyzing single-cell omics data from 2019-2020, focusing on methods for dimensionality reduction, batch normalization, cell type classification, trajectory inference, and multimodal integration.

Current Opinion in Systems Biology · Apr 28, 2021Peer-reviewedBioinformatics

Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis

Félix Raimundo, Céline Vallot, Jean‐Philippe Vert

This study benchmarks dimensionality reduction methods for scRNA-seq by varying parameters and finds that some methods benefit from tuning while others perform well with defaults.

Genome biology · Aug 24, 2020Peer-reviewedBioinformatics