Author

Simon J. Baumgart

Novo Nordisk (Denmark)

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

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Assay:scRNA-seq· 2 Assay:spatial transcriptomics· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Research Focus:deconvolution· 1

Frequent Co-Authors

Charles PykeiD· 2 shared Henning HvidiD· 2 shared Sikander HayatiD· 2 shared Vivek DasiD· 2 shared Alban Laus Obel Slabowska· 1 shared Anne BableriD· 1 shared Christian M. StegmanniD· 1 shared Judith C. SluimeriD· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

Charles PykeORCID

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Author profile →

3 papers

Recent·Most cited

A Benchmark of state-of-the-art Deconvolution Methods in Spatial Transcriptomics: Insights from Cardiovascular Disease and Chronic Kidney Disease

Alban Laus Obel Slabowska, Charles Pyke, Henning Hvid, Leon Eyrich Jessen, Simon J. Baumgart, Vivek Das

This study benchmarks three deconvolution methods in spatial transcriptomics for cardiovascular and kidney diseases, finding comparable performance with method-specific trade-offs.

Frontiers in Bioinformatics · Mar 27, 2024Peer-reviewedBioinformatics

Encompassing view of spatial and single-cell RNA-seq renews the role of the microvasculature in human atherosclerosis

Tore Bleckwehl, Sidrah Maryam, Anne Babler, Michael Nyberg, Markus Høybye Bosteen, Maurice Halder, Charles Pyke, Henning Hvid, Louise Marie Voetmann, Judith C. Sluimer, Vivek Das, Simon J. Baumgart, Rafael Kramann, Sikander Hayat

The paper integrates single-cell RNA-seq and spatial transcriptomics data to reveal cellular interactions and changes in human atherosclerosis.

bioRxiv · Dec 15, 2023 · PreprintBioinformatics

MACA: Marker-based automatic cell-type annotation for single cell expression data

Yang Xu, Simon J. Baumgart, Christian M. Stegmann, Sikander Hayat

MACA is a tool for automatic cell-type annotation in single-cell transcriptomics datasets, showing improved accuracy and speed compared to existing methods.

Bioinformatics · Dec 17, 2021Peer-reviewedBioinformatics