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.


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 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