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John Salamon (1), Xiaoyan Qian (3), Mats Nilsson (3), David Lynn (1,2) ((1) South Australian Health and Medical Research Institute, Adelaide, Australia (2) Flinders University, Bedford Park, Australia (3) Stockholm University, Stockholm, Sweden)Gene expression studies typically homogenise samples before sequencing, discarding spatial information on where transcripts are expressed. In situ sequencing is a novel method to generate spatially-resolved, in situ RNA localization and expression data. Few methods currently exist to analyze and visualize the complex relationships that exist between these transcripts or identify how these transcriptional profiles change in different regions of the tissue or across different tissue sections. Here, we present InsituNet, an innovative new application that converts in situ sequencing data into interactive network-based visualisations, where each transcript is a node in the network and edges represent the spatial co-expression relationships between transcripts. InsituNet is able to identify unexpected relationships given the frequency of the transcripts in the tissue. The user is able to select (irregularly-shaped) regions of interest in the section for comparison to other regions. One can also compare how the transcriptional network changes across different tissue sections (e.g. healthy vs. disease).