Author

Ina Maria Deutschmann

Centre National de la Recherche ScientifiqueORCID 0000-0002-3512-261XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Ecology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:scRNA-seq· 2 Research Focus:batch correction / integration· 1 Assay:CITE-seq· 1 Research Focus:clustering· 1 Research Focus:ecology & biodiversity· 1 Research Focus:gene regulatory network inference· 1 Approach:matrix factorization· 1

Frequent Co-Authors

Laura CantiniiD· 2 shared André Megali AmadoiD· 1 shared Carlota R. GazullaiD· 1 shared Gabriel Peyré AiD· 1 shared Geert-Jan HuizingiD· 1 shared Hugo SarmentoiD· 1 shared Isabel FerreraiD· 1 shared Janaína RigonatoiD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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Focal author (center)Selected co-authorORCID co-authorUnlinked name

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2 shared papersBioinformatics

Laura CantiniORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Ecological processes shaping marine microbial assemblages diverge between equatorial and temperate time-series

Pedro C. Junger, Vinicius Kavagutti, Ina Maria Deutschmann, Carlota R. Gazulla, Paula Huber, Maiara Menezes, Rodolfo Paranhos, André Megali Amado, Isabel Ferrera, Janaína Rigonato, Samuel Chaffron, Josep M. Gasol, Ramiro Logares, Hugo Sarmento

This study compares microbial community dynamics in equatorial and temperate marine time-series, finding distinct ecological processes shaping their assembly and stability.

Molecular Ecology · Jan 1, 2026Peer-reviewedEcology

Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS

Rémi Trimbour, Ina Maria Deutschmann, Laura Cantini

HuMMuS infers regulatory mechanisms from single-cell multi-omics data, improving predictions of TF targets, binding motifs, and regulatory regions.

Bioinformatics · Mar 9, 2024Peer-reviewedBioinformatics

Paired single-cell multi-omics data integration with Mowgli

Geert-Jan Huizing, Ina Maria Deutschmann, Gabriel Peyré A, Laura Cantini

Mowgli integrates paired single-cell multi-omics data using a combination of NMF and OT, improving clustering and interpretability, and is benchmarked against state-of-the-art methods.

Nature Communications · Nov 24, 2023Peer-reviewedBioinformatics