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spMOCA is a spatially informed statistical model developed to infer gene co-expression networks across spatial locations in a biologically meaningful way. spMOCA builds upon the principle that gene expression in spatial transcriptomics data is influenced by both spatial dependency and gene-gene interaction. Using a count matrix as input, spMOCA integrates these elements to yield a more accurate and nuanced understanding of gene co-expression networks with spatial contexts. spMOCA employs an efficient optimization algorithm for estimating gene-gene correlation, which is scalable to datasets with tens of thousands of spatial locations and tens of thousands of genes, surpassing the capabilities of existing methods.
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if you have questions, feel free to leave messages on the github issues or contact me through email: chichun_tan@brown.edu