Analysis and visualization of spatial transcriptomic data

TL;DR

This paper reviews the core concepts of spatial genomics technology and provides a comprehensive overview of current analysis and visualization methods for spatial transcriptomic data, emphasizing the importance of spatial information in understanding tissue organization.

Contribution

It offers a detailed summary of spatial transcriptomics technologies and systematically reviews existing analysis and visualization methods, highlighting recent advancements in the field.

Findings

Summarizes core concepts of spatial genomics technology

Provides a comprehensive review of analysis methods

Highlights recent developments in visualization techniques

Abstract

Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. RNA molecules are measured by in situ sequencing, in situ hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.