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Belayer: Modeling Discrete and Continuous Spatial Variation in Gene Expression from Spatially Resolved Transcriptomics

Author(s): Ma, Cong; Chitra, Uthsav; Zhang, Shirley; Raphael, Benjamin J.

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Abstract: Spatially resolved transcriptomics (SRT) technologies measure gene expression at known locations in a tissue slice, enabling the identification of spatially varying genes or cell types. Current approaches for these tasks assume either that gene expression varies continuously across a tissue or that a slice con- tains a small number of regions with distinct cellular composition. We propose a model for SRT data that includes both continuous and discrete spatial variation in expression, and an algorithm, Belayer, to esti- mate the parameters of this model from layered tissues. Belayer models gene expression as a piecewise linear function of the relative depth of a tissue layer with possible discontinuities at layer boundaries. We use conformal maps to model relative depth and derive a dynamic programming algorithm to infer layer boundaries and gene expression functions. Belayer accurately identifies tissue layers and infers biologically meaningful spatially varying genes in SRT data from brain and skin tissue samples.
Publication Date: 2022
Citation: Ma, Cong, Chitra, Uthsav, Zhang, Shirley and Raphael, Benjamin J. "Belayer: Modeling Discrete and Continuous Spatial Variation in Gene Expression from Spatially Resolved Transcriptomics." Research in Computational Molecular Biology (2022): 372-373.
DOI: 10.1007/978-3-031-04749-7_33
Keywords: patially resolved transcriptomics, spatial variation, gene expression, layered tissues, segmented regression, conformal maps
Pages: 372 - 373
Type of Material: Conference Article
Journal/Proceeding Title: Research in Computational Molecular Biology
Version: Author's manuscript

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