Inferring histology-associated gene expression gradients in spatial transcriptomic studies.

ABSTRACT: Spatially resolved transcriptomics has revolutionized RNA studies by aligning RNA abundance with tissue structure, enabling direct comparisons between histology and gene expression. Traditional approaches to identifying signature genes often involve preliminary data grouping, which can overlook subtle expression patterns in complex tissues. We present Spatial Gradient Screening, an algorithm which facilitates the supervised detection of histology-associated gene expression patterns without prior data grouping. Utilizing spatial transcriptomic data along with single-cell deconvolution from injured mouse cortex, and TCR-seq data from brain tumors, we compare our methodology to standard differential gene expression analysis. Our findings illustrate both the advantages and limitations of clust

SUBMITTER: Kueckelhaus J 

PROVIDER: S-EPMC11343836 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature