Fig. 1: Overview of STdeconvolve.

a STdeconvolve takes as input a spatial transcriptomics (ST) gene counts matrix of D pixels (rows) by N genes (columns). A matrix of spatial coordinates for each of the D pixels can also be used for visualization. b STdeconvolve first feature selects genes for deconvolution, such as genes with counts in more than 5% and less than 95% of the pixels, and overdispersed across the pixels. STdeconvolve then guides the selection of the optimal number of cell types to be deconvolved, K. STdeconvolve finally applies LDA modeling. A graph representation of LDA modeling and the parameters to be learned is shown. Shaded circle indicates observed variables and clear circles indicate latent variables. c STdeconvolve outputs two matrices: (1) β, the deconvolved transcriptional profile matrix of K cell types over N’ feature selected genes, and (2) θ, the proportions of K cell types across the D pixels. The proportions of deconvolved cell types can then be visualized across the pixels.