"Hidden Markov random field models for cell-type assignment of spatiall" by Cheng Zhong, Tian Tian et al.
 

Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics

Document Type

Article

Publication Date

11-1-2023

Abstract

Motivation: The recent development of spatially resolved transcriptomics (SRT) technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data. They first conduct clustering analysis and then aggregate cluster-level expression based on the clustering results. This workflow fails to leverage the marker gene information efficiently. On the other hand, other cell annotation methods designed for single-cell RNA-seq data utilize the cell-type marker genes information but fail to use spatial information in SRT data. Results: We introduce a statistical spatial transcriptomics cell assignment model, SPAN, to annotate clusters of cells or spots into known types in SRT data with prior knowledge of predefined marker genes and spatial information. The SPAN model annotates cells or spots from SRT data using predefined overexpressed marker genes and combines a mixture model with a hidden Markov random field to model the spatial dependency between neighboring spots. We demonstrate the effectiveness of SPAN against spatial and nonspatial clustering algorithms through extensive simulation and real data experiments.

Identifier

85176972222 (Scopus)

Publication Title

Bioinformatics

External Full Text Location

https://doi.org/10.1093/bioinformatics/btad641

e-ISSN

13674811

ISSN

13674803

PubMed ID

37944045

Issue

11

Volume

39

Grant

ACI-1548562

Fund Ref

National Science Foundation

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