B-T.52: Spatial gene regulatory networks
Keywords
spatial transcriptomics, gene regulatory network
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Gene regulatory networks convey the relationship between regulators such as transcription factors and the expression of individual genes. They are useful for understanding changes in cell phenotype, particularly for complex diseases such as cancer.
Although methods to generate gene regulatory networks from bulk gene expression data are common, it is more challenging to deal with single cell and spatial transcriptomics data due to their sparsity. Here we present an approach to generate spatially resolved gene regulatory networks from spatial transcriptomics data called STOAT (Spatial TranscriptOmics to Assess Transcriptional regulation), building on the methods previously developed in the lab (PANDA, LIONESS) and also a recently published tool that helps with the generation of prior networks (SPONGE). STOAT adapts the LIONESS approach of generating sample-specific networks to the spatial transcriptomic reality of very sparse gene expression through the use of gene expression averaging.
We show supporting evidence for the validity of this method and also its application to a publically available datasets and samples from our collaborators, ranging from healthy samples to triple-negative breast cancer. In particular, we highlight the differences in the classification of individual spots using gene regulatory networks and gene expression data. We also identify a subset of spot clusters which are identified using both approaches, and discuss why this isn't the case for all of them and how our approach could be further improved.
Co-authors: Xavier Tekpli, Mariike Kuijjer
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