WISpR spatial deconvolution workflow overview.

A, UMAP visualization of scRNA-Seq data showcasing four distinct cell types, each depicted in a unique color. 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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Bibliographic Details
Main Author:	Nuray Sogunmez Erdogan (21533872) (author)
Other Authors:	Deniz Eroglu (21533875) (author)
Published:	2025
Subjects:	Genetics Biotechnology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified unlike conventional approaches understanding cellular responses resolution cellular maps preserving biological coherence predicted cellular landscapes machine learning algorithm e ., spatially decode tissue organization induced sparse regression functionally consistent cell distinct cell types leveraging sparse cell integrating reference single cell type medleys spatial transcriptomics spot spatial transcriptomics sparse deconvolution integrates spot cell data spatial datasets type variations type localization type distributions type arrangements wispr ), unmatched datasets specific hyperparameters signaling however practical utility permissive algorithms neglecting biology neglect biology methods rely introduce weight inaccurate predictions grounded constraints genome coverage driven modeling cancerous tissues
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WISpR spatial deconvolution workflow overview.

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