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A Hidden Markov Random Field Model for Detecting Domain Organizations from Spatial Transcriptomic Data

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Computational Methods for Single-Cell Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1935))

Abstract

Cells in complex tissues are organized by distinct microenvironments and anatomical structures. This spatial environment of cells is thought to be important for division of labor and other specialized functions of tissues. Recently developed spatial transcriptomic technologies enable the quantification of expression of hundreds of genes while accounting for cells’ spatial coordinates, providing an opportunity to study spatially organized structures. Here, we describe a computational pipeline for detecting the spatial organization of cells based on a hidden Markov random field model. We illustrate this pipeline with data generated from multiplexed smFISH from the adult mouse visual cortex.

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Authors and Affiliations

  1. Dana-Farber Cancer Institute, Boston, MA, USA

    Qian Zhu

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  1. Qian Zhu
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Correspondence to Qian Zhu .

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Editors and Affiliations

  1. Dana–Farber Cancer Institute and Harvard Chan, School of Public Health, Boston, MA, USA

    Guo-Cheng Yuan

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Zhu, Q. (2019). A Hidden Markov Random Field Model for Detecting Domain Organizations from Spatial Transcriptomic Data. In: Yuan, GC. (eds) Computational Methods for Single-Cell Data Analysis. Methods in Molecular Biology, vol 1935. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9057-3_16

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  • DOI: https://doi.org/10.1007/978-1-4939-9057-3_16

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9056-6

  • Online ISBN: 978-1-4939-9057-3

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