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Tiesmeyer, S.* ; Müller-Bötticher, N.* ; Malt, A.* ; Ma, L.* ; Marco-Salas, S.* ; Kiessling, P.* ; Horn, P.* ; Guillot, A.* ; Kuemmerle, L. ; Tacke, F.* ; Theis, F.J. ; Kuppe, C.* ; Nilsson, M.* ; Eils, R.* ; Long, B.* ; Ishaque, N.*

Identifying 3D signal overlaps in spatial transcriptomics data with ovrlpy.

Nat. Biotechnol., DOI: 10.1038/s41587-026-03004-8 (2026)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Imaging-based spatially resolved transcriptomics can localize transcripts within tissue sections in three dimensions. However, cell segmentation, which assigns transcripts to cells, is usually performed in two dimensions and spatial doublets in the vertical dimension result in segmented cells containing transcripts originating from multiple cell types. Here we present a computational tool called ovrlpy that identifies overlapping cells, tissue folds and inaccurate cell segmentation by analyzing transcript localization in three dimensions.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Pattern Recognition (psychology) ; Signal (programming Language) ; Transcriptome ; Signal Processing ; Noisy Data
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Zeitschrift Nature Biotechnology
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute for Intelligent Biotechnologies (IBIO)
Förderungen Deutsche Forschungsgemeinschaft (German Research Foundation)
Bundesministerium fr Bildung und Forschung (Federal Ministry of Education and Research)