Abstract:The growth of large-scale spatial gene expression data requires new computational tools to extract major trends in gene expression in their native spatial context. Here, we describe an unsupervised and interpretable computational framework to (1) pre-process 3D spatial gene expression datasets by imputation of missing voxels, (2) identify principal patterns (PPs) of 3D spatial gene expression profiles using the stability-driven non-negative matrix factorization (staNMF) technique, and (3) systematically compar… Show more
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