SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes.

ABSTRACT: Spatially resolved gene expression profiles are key to understand tissue organization and function. However, spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). Simulating varying reference quantities

SUBMITTER: Elosua-Bayes M 

PROVIDER: S-EPMC8136778 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature