Cell segmentation-free inference of cell types from in situ transcriptomics data

Inaccurate cell segmentation has been the major problem for cell-type identification and tissue characterization of the in situ spatially resolved transcriptomics data. Here we show a robust cell segmentation-free computational framework (SSAM), for identifying cell types and tissue domains in 2D an...

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Autores principales:	Jeongbin Park, Wonyl Choi, Sebastian Tiesmeyer, Brian Long, Lars E. Borm, Emma Garren, Thuc Nghi Nguyen, Bosiljka Tasic, Simone Codeluppi, Tobias Graf, Matthias Schlesner, Oliver Stegle, Roland Eils, Naveed Ishaque
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/a175c0b017934a918d3257520493c81c
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spelling	oai:doaj.org-article:a175c0b017934a918d3257520493c81c2021-12-02T18:06:34ZCell segmentation-free inference of cell types from in situ transcriptomics data10.1038/s41467-021-23807-42041-1723https://doaj.org/article/a175c0b017934a918d3257520493c81c2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23807-4https://doaj.org/toc/2041-1723Inaccurate cell segmentation has been the major problem for cell-type identification and tissue characterization of the in situ spatially resolved transcriptomics data. Here we show a robust cell segmentation-free computational framework (SSAM), for identifying cell types and tissue domains in 2D and 3D.Jeongbin ParkWonyl ChoiSebastian TiesmeyerBrian LongLars E. BormEmma GarrenThuc Nghi NguyenBosiljka TasicSimone CodeluppiTobias GrafMatthias SchlesnerOliver StegleRoland EilsNaveed IshaqueNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jeongbin Park Wonyl Choi Sebastian Tiesmeyer Brian Long Lars E. Borm Emma Garren Thuc Nghi Nguyen Bosiljka Tasic Simone Codeluppi Tobias Graf Matthias Schlesner Oliver Stegle Roland Eils Naveed Ishaque Cell segmentation-free inference of cell types from in situ transcriptomics data
description	Inaccurate cell segmentation has been the major problem for cell-type identification and tissue characterization of the in situ spatially resolved transcriptomics data. Here we show a robust cell segmentation-free computational framework (SSAM), for identifying cell types and tissue domains in 2D and 3D.
format	article
author	Jeongbin Park Wonyl Choi Sebastian Tiesmeyer Brian Long Lars E. Borm Emma Garren Thuc Nghi Nguyen Bosiljka Tasic Simone Codeluppi Tobias Graf Matthias Schlesner Oliver Stegle Roland Eils Naveed Ishaque
author_facet	Jeongbin Park Wonyl Choi Sebastian Tiesmeyer Brian Long Lars E. Borm Emma Garren Thuc Nghi Nguyen Bosiljka Tasic Simone Codeluppi Tobias Graf Matthias Schlesner Oliver Stegle Roland Eils Naveed Ishaque
author_sort	Jeongbin Park
title	Cell segmentation-free inference of cell types from in situ transcriptomics data
title_short	Cell segmentation-free inference of cell types from in situ transcriptomics data
title_full	Cell segmentation-free inference of cell types from in situ transcriptomics data
title_fullStr	Cell segmentation-free inference of cell types from in situ transcriptomics data
title_full_unstemmed	Cell segmentation-free inference of cell types from in situ transcriptomics data
title_sort	cell segmentation-free inference of cell types from in situ transcriptomics data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a175c0b017934a918d3257520493c81c
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Cell segmentation-free inference of cell types from in situ transcriptomics data

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