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

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, t

SUBMITTER: Zhong C 

PROVIDER: S-EPMC10640398 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature