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Bayesian segmentation of spatially resolved transcriptomics data

Viktor Petukhov, Ruslan A. Soldatov, Konstantin Khodosevich, Peter V. Kharchenko
doi: https://doi.org/10.1101/2020.10.05.326777
Viktor Petukhov
1Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
2Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Ruslan A. Soldatov
2Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Konstantin Khodosevich
1Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Peter V. Kharchenko
2Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
3Harvard Stem Cell Institute, Cambridge, MA, USA
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  • For correspondence: peter_kharchenko@hms.harvard.edu
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Abstract

Spatial transcriptomics is an emerging stack of technologies, which adds spatial dimension to conventional single-cell RNA-sequencing. New protocols, based on in situ sequencing or multiplexed RNA fluorescent in situ hybridization register positions of single molecules in fixed tissue slices. Analysis of such data at the level of individual cells, however, requires accurate identification of cell boundaries. While many existing methods are able to approximate cell center positions using nuclei stains, current protocols do not report robust signal on the cell membranes, making accurate cell segmentation a key barrier for downstream analysis and interpretation of the data. To address this challenge, we developed a tool for Bayesian Segmentation of Spatial Transcriptomics Data (Baysor), which optimizes segmentation considering the likelihood of transcriptional composition, size and shape of the cell. The Bayesian approach can take into account nuclear or cytoplasm staining, however can also perform segmentation based on the detected transcripts alone. We show that Baysor segmentation can in some cases nearly double the number of the identified cells, while reducing contamination. Importantly, we demonstrate that Baysor performs well on data acquired using five different spatially-resolved protocols, making it a useful general tool for analysis of high-resolution spatial data.

Competing Interest Statement

P.V.K serves on the Scientific Advisory Board to Celsius Therapeutics, Inc. Other authors declare no conflict of interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 06, 2020.
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Bayesian segmentation of spatially resolved transcriptomics data
Viktor Petukhov, Ruslan A. Soldatov, Konstantin Khodosevich, Peter V. Kharchenko
bioRxiv 2020.10.05.326777; doi: https://doi.org/10.1101/2020.10.05.326777
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Bayesian segmentation of spatially resolved transcriptomics data
Viktor Petukhov, Ruslan A. Soldatov, Konstantin Khodosevich, Peter V. Kharchenko
bioRxiv 2020.10.05.326777; doi: https://doi.org/10.1101/2020.10.05.326777

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