Author

Francis Jacob

University of BaselORCID 0000-0002-0446-1942Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:scRNA-seq· 3 Approach:statistical model· 3 Research Focus:batch correction / integration· 1 Research Focus:cell type annotation· 1 Assay:CITE-seq· 1 Approach:deep learning· 1 Assay:long-read sequencing· 1

Frequent Co-Authors

Niko BeerenwinkeliD· 3 shared Pedro FerreiraiD· 3 shared Abdullah KahramaniD· 2 shared Alva Rani JamesiD· 2 shared André Fedier· 2 shared André KahlesiD· 2 shared Andrea Jacobs· 2 shared Anja IrmischiD· 2 shared

Activity by Year

2020-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBioinformatics

Niko BeerenwinkelORCID

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Author profile →

3 papers

Recent·Most cited

De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data

Arthur Dondi, Nico Borgsmüller, Pedro Ferreira, Brian J. Haas, Francis Jacob, Viola A Heinzelmann-Schwarz, Niko Beerenwinkel

LongSom detects somatic variants in LR scRNA-seq data without normal samples, revealing clonal heterogeneity in ovarian cancer.

bioRxiv · Mar 6, 2024Cancer Biology

scROSHI - robust supervised hierarchical identification of single cells

Michael Prummer, Anne Bertolini, Lars Bosshard, Florian Barkmann, Josephine Yates, Valentina Boeva, Ruedi H. Aebersold, Melike Ak, Faisal Alquaddoomi, Jonas B. Albinus, Ilaria Alborelli, Sonali Andani, Per-Olof Attinger, Marina Bacac, Daniel Baumhoer, , Berend Snijder

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

NAR Genomics and Bioinformatics · Jun 1, 2022Peer-reviewedBioinformatics

SCIM: Universal Single-Cell Matching with Unpaired Feature Sets

Stefan G. Stark, Joanna Ficek, Francesco Locatello, Ximena Bonilla, Stéphane Chevrier, Franziska Singer, Ruedi H. Aebersold, Faisal Alquaddoomi, Jonas B. Albinus, Ilaria Alborelli, Sonali Andani, Per-Olof Attinger, Marina Bacac, Daniel Baumhoer, Beatrice Beck‐Schimmer, , Vipin T. Sreedharan

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.

Bioinformatics · Sep 14, 2020Peer-reviewedBioinformatics