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

Cell Syst. 2018 May 23;6(5):626-630.e3. doi: 10.1016/j.cels.2018.03.010. Epub 2018 May 9.

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.

Keywords: Cytoscape; data visualization; gene expression; in situ sequencing; network biology; spatial co-expression; spatial transcriptomics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Breast Neoplasms / genetics
  • Computational Biology / methods*
  • Computers
  • Exome Sequencing / methods
  • Gene Expression
  • Gene Expression Profiling / methods*
  • Gene Regulatory Networks / genetics*
  • Genes, erbB-2 / genetics
  • Humans
  • In Situ Hybridization / methods
  • Software