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Abstract:
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The analysis of spatially-resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations in the tissue enables delineation of spatial co-regulatory patterns. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for increasing spatial correlation. We have discussed in-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using the SpaceX method.
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