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

John Salamon, Xiaoyan Qian, Mats Nilsson, David Lynn

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)

    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
    Number of pages8
    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

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