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InSituCor: a toolkit for discovering non-trivial spatial correlations in spatial transcriptomics

Danaher, P.; McGuire, D.; Patrick, M.; Kroeppler, D.; Zhai, H.; Schmid, J.; Beechem, J. M.

2023-09-22 bioinformatics
10.1101/2023.09.19.558514 bioRxiv
Show abstract

Spatial transcriptomics presents the best kind of problem: how to find the many biological insights hidden within complex datasets. Spatially correlated genes can reveal high-interest phenomena like cell-cell interactions and latent variables. We introduce InSituCor, a toolkit for discovering modules of spatially correlated genes. A major contribution is that InSituCor returns only correlations not explainable by obvious factors like the cell type landscape; this spares precious analyst effort for non-trivial findings. InSituCor supports both unbiased discovery of whole-dataset correlations and knowledge-driven exploration of genes of interest. As a special case, it evaluates ligand-receptor pairs for spatial co-regulation.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.