Statistical and Machine Learning Methods for Spatially Resolved Transcriptomics with Histology

Overview

Journal Comput Struct Biotechnol J

Specialty Biotechnology

Date 2021 Jul 21

PMID 34285782

Citations 36

Authors

Jian Hu

Amelia Schroeder

Kyle Coleman

Chixiang Chen

Benjamin J Auerbach

Mingyao Li

Affiliations

Soon will be listed here.

Abstract

Recent developments in spatially resolved transcriptomics (SRT) technologies have enabled scientists to get an integrated understanding of cells in their morphological context. Applications of these technologies in diverse tissues and diseases have transformed our views of transcriptional complexity. Most published studies utilized tools developed for single-cell RNA sequencing (scRNA-seq) for data analysis. However, SRT data exhibit different properties from scRNA-seq. To take full advantage of the added dimension on spatial location information in such data, new methods that are tailored for SRT are needed. Additionally, SRT data often have companion high-resolution histology information available. Incorporating histological features in gene expression analysis is an underexplored area. In this review, we will focus on the statistical and machine learning aspects for SRT data analysis and discuss how spatial location and histology information can be integrated with gene expression to advance our understanding of the transcriptional complexity. We also point out open problems and future research directions in this field.

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