InsituNet converts *in situ* sequencing data into interactive network-based visualizations, where each transcript is a node in the network and edges represent the spatial co-expression relationships between transcripts. InsituNet profiles and facilitates comparison of different 2D tissue regions by identifying spatial co-expressions that occur between transcripts both significantly more, and less, than statistically expected, given the frequency of the transcripts in the tissue.
**Published in:** [https://www.cell.com/cell-systems/pdf/S2405-4712(18)30107-8.pdf Cell Systems]
**Please cite:** *Salamon, J, Lynn, D, Qian, X & Nilsson, M 2018, 'Network visualization and analysis of spatially aware gene expression data with InsituNet' Cell Systems, vol 6, pp. 1-5.*
- Import spatially-resolved 2D gene expression datasets from csv files directly into Cytoscape
- Interactively explore datasets within an OpenGL window embedded within the control panel
- Select (irregularly-shaped) regions of interest for comparison to other regions
- Statistical and thresholding options for assessment of co-expression significance
- Network style based on proportion of transcripts and strength of relationship within selected areas
- Continuous layout synchronization of generated networks to aid comparison.
- [https://bitbucket.org/lynnlab/insitunet/ Source code]
- [https://bytebucket.org/lynnlab/insitunet/wiki/InsituNet_Documentation.pdf Documentation (pdf)]
- [https://sala6.bitbucket.io/ Documentation (html)]
- [https://bitbucket.org/lynnlab/insitunet/src/master/datasets/ Example datasets]
- [https://sala6.bitbucket.io/#x1-240006 Tutorials]
- [https://www.cell.com/cell-systems/pdf/S2405-4712(18)30107-8.pdf Publication]