Network Visualization and Analysis of Spatially Aware Gene Expression Data with InsituNet

John Salamon, Xiaoyan Qian, Mats Nilsson, David John Lynn

Research output: Contribution to journalArticle

1 Citation (Scopus)
36 Downloads (Pure)

Abstract

In situ sequencing methods generate spatially resolved RNA localization and expression data at an almost single-cell resolution. Few methods, however, currently exist to analyze and visualize the complex data that is produced, which can encode the localization and expression of a million or more individual transcripts in a tissue section. Here, we present InsituNet, an application that converts in situ sequencing data into interactive network-based visualizations, where each unique transcript is a node in the network and edges represent the spatial co-expression relationships between transcripts. InsituNet is available as an app for the Cytoscape platform at http://apps.cytoscape.org/apps/insitunet. InsituNet enables the analysis of the relationships that exist between these transcripts and can uncover how spatial co-expression profiles change in different regions of the tissue or across different tissue sections. Salamon et al. present InsituNet, a software application that converts spatially resolved in situ transcriptomics data into interactive network-based visualizations. InsituNet enables the statistical analysis of spatial co-expression between transcripts and can uncover how spatial co-expression profiles change in different regions of the tissue or across different tissue sections.

Original languageEnglish
Pages (from-to)626-630.e3
JournalCell Systems
Volume6
Issue number5
DOIs
Publication statusPublished - 23 May 2018

Keywords

  • Cytoscape
  • data visualization
  • gene expression
  • in situ sequencing
  • network biology
  • spatial co-expression
  • spatial transcriptomics

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

Cite this