Author

Shashwat Sahay

Berlin Institute of Health at Charité - Universitätsmedizin BerlinORCID 0000-0002-8533-6149Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Assay:spatial transcriptomics· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:deconvolution· 1 Research Focus:dimensionality reduction· 1 Assay:DNA methylation· 1 Approach:experimental assay· 1

Frequent Co-Authors

Naveed IshaqueiD· 2 shared Niklas Müller‐BötticheriD· 2 shared Roland EilsiD· 2 shared Christoph PlassiD· 1 shared Jörn WalteriD· 1 shared Michael SchereriD· 1 shared Nikita Vedeneev· 1 shared Pavlo LutsikiD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersBioinformatics

Naveed IshaqueORCID

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3 papers

Recent·Most cited

SpatialLeiden - Spatially-aware Leiden clustering

Niklas Müller‐Bötticher, Shashwat Sahay, Roland Eils, Naveed Ishaque

SpatialLeiden integrates spatial embeddings into Leiden clustering to create a spatially aware method that performs well compared to existing spatial clustering techniques.

Genome biology · Feb 7, 2025Peer-reviewedBioinformatics

SSAM-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data

Sebastian Tiesmeyer, Shashwat Sahay, Niklas Müller‐Bötticher, Roland Eils, Sebastian D. Mackowiak, Naveed Ishaque

SSAM-lite provides a lightweight, web-based tool for rapid, segmentation-free analysis of spatially resolved transcriptomics data, enabling broad accessibility without requiring computational expertise.

Frontiers in Genetics · Feb 28, 2022Peer-reviewedBioinformatics

Reference-free deconvolution of complex DNA methylation data - a systematic protocol

Michael Scherer, Petr V. Nazarov, Réka Tóth, Shashwat Sahay, Tony Kaoma, Valentin J. Maurer, Nikita Vedeneev, Christoph Plass, Thomas Lengauer, Jörn Walter, Pavlo Lutsik

This paper introduces a reference-free deconvolution protocol for DNA methylomes to identify hidden cell populations in complex samples, applied to lung cancer data from TCGA.

Nature Protocols · Sep 25, 2020Peer-reviewedBioinformatics