WISpR spatial deconvolution workflow overview.
<p><b>A</b>, UMAP visualization of scRNA-Seq data showcasing four distinct cell types, each depicted in a unique color. <b>B</b>, Gene expression profiles for individual cell types derived from the intersection of genes (N) in scRNA-Seq and spatial transcriptomics datas...
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2025
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| Summary: | <p><b>A</b>, UMAP visualization of scRNA-Seq data showcasing four distinct cell types, each depicted in a unique color. <b>B</b>, Gene expression profiles for individual cell types derived from the intersection of genes (N) in scRNA-Seq and spatial transcriptomics datasets. <b>C</b>, Representative <i>x</i>- and <i>y</i>-coordinates of spots in the spatial transcriptomics dataset, with each color indicating a zone profile identified through clustering analysis. <b>D</b>, Gene expression profiles for individual spatial zones based on a selected gene set from the intersection of scRNA-Seq and spatial transcriptomics datasets. <b>E</b>, Identification of differentially expressed genes specific to cell types () and zones (), defining cluster differentiation strength. The union of and forms the reference scRNA-Seq and spatial transcriptomics datasets (<i>G</i>) for WISpR deconvolution. <b>F</b>, WISpR algorithm summary. The spatial transcriptomics matrix (MxL) and reference scRNA-Seq matrix (MxQ) guide the prediction of the best sparse coefficient matrix (QxL), representing the number of genes (M) in <i>G</i> for each spot, number of spots (L), and number of reference cell types (Q). WISpR iteratively thresholds coefficients based on spot-specific weights (<i>w</i>) and thresholds (). <b>G</b>, Spatial deconvolution results reveal estimated abundance patterns, locations, and co-occurring cell type compositions for each reference cell type using the WISpR algorithm.</p> |
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