Author

Petra Catalina Schwalie

F. Hoffmann-La Roche AGORCID 0000-0002-6004-8095Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:scRNA-seq· 5 Approach:statistical model· 5 Research Focus:cell type annotation· 3 Research Focus:batch correction / integration· 2 Research Focus:clustering· 2 Research Focus:differential expression· 2 Organism:human· 2 Organism:mouse· 2

Frequent Co-Authors

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 Anne BertoliniiD· 2 shared Beatrice Beck‐SchimmeriD· 2 shared

Activity by Year

2017-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Abdullah KahramanORCID

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5 papers

Recent·Most cited

Spatial Transcriptomics and Single-Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

Bettina Amberg, Fabian Köchl, Nadine Kumpesa, Megana K. Prasad, Filip Bochner, Mar Hernandez-Obiols, Michael B. Otteneder, Lucas Stalder, Frances C. Shaffo, Ali Nowrouzi, David M. Markusic, Hélène Haegel, Rebecca Xicluna, Marc Sultan, Björn Jacobsen, , Kerstin Hahn

This study uses spatial transcriptomics and single-nucleus RNA sequencing to analyze rAAV2 and rAAV9 vector effects in mouse livers, revealing sex-specific transcriptomic changes and novel transgene co-expression factors.

Gene Therapy · Mar 11, 2026Peer-reviewedMolecular 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

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research

Sophia Clara Mädler, Alice Julien-Laferrière, Luis Wyss, Miroslav Phan, Anthony Sonrel, Albert S. W. Kang, Eric Ulrich, Roland Schmucki, Jitao David Zhang, Martin Ebeling, Laura Badi, Tony Kam‐Thong, Petra Catalina Schwalie, Klas Hatje

Besca is a toolkit that streamlines scRNA-seq analysis with quality control, clustering, cell annotation, and bulk transcriptomics integration across datasets.

NAR Genomics and Bioinformatics · Oct 4, 2021Peer-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

ASAP: a Web-based platform for the analysis and inter-active visualization of single-cell RNA-seq data

Vincent Gardeux, Fabrice P.A. David, Adrian Shajkofci, Petra Catalina Schwalie, Bart Deplancke

ASAP provides a web-based platform for analyzing and visualizing scRNA-seq data, enabling users to perform analysis and visualization without requiring computational expertise.

Bioinformatics · May 23, 2017Peer-reviewedBioinformatics