Spatially resolved gene expression profiles are key to understand tissue organization and function. However, novel 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).
For a more detailed explanation of
SPOTlight consider looking at our
> Elosua-Bayes M, Nieto P, Mereu E, Gut I, Heyn H.
SPOTlight: seeded NMF regression to deconvolute
spatial transcriptomics spots with single-cell transcriptomes.
Nucleic Acids Res. 2021;49(9):e50. doi: 10.1093
library(ggplot2) library(SPOTlight) library(SingleCellExperiment) library(SpatialExperiment) library(scater) library(scran)
SPOTlight is a tool that enables the deconvolution of cell types and cell type
proportions present within each capture location comprising mixtures of cells.
Originally developed for 10X’s Visium - spatial transcriptomics - technology, it
can be used for all technologies returning mixtures of cells.
SPOTlight is based on learning topic profile signatures, by means of an NMFreg
model, for each cell type and finding which combination of cell types fits best
the spot we want to deconvolute. Find below a graphical abstract visually
summarizing the key steps.