mSGR is a Bayesian framework that infers spatially varying gene regulatory networks from multi-resolution hierarchical spatial transcriptomics data using Gaussian process priors and variational Bayes inference.
Expression of expected values: 1.E∥Yi(Sk)−∑p j̸=iHk ij˜vk ij·I(zk ij >0)∥2 DenoteG ijk =E(H k ij˜vk ij·I(zk ij >0)) =p k ijHk ij ˆµ˜v(zk ij >0) and ˜Gik = ∑p j̸=iGijk
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Multi-resolution Spatial Graphical Regression Models for Hierarchical Spatial Transcriptomics Data
mSGR is a Bayesian framework that infers spatially varying gene regulatory networks from multi-resolution hierarchical spatial transcriptomics data using Gaussian process priors and variational Bayes inference.