DESpace: spatially variable gene detection via differential expression testing of spatial clusters.

ABSTRACT:

Motivation

Spatially resolved transcriptomics (SRT) enables scientists to investigate spatial context of mRNA abundance, including identifying spatially variable genes (SVGs), i.e. genes whose expression varies across the tissue. Although several methods have been proposed for this task, native SVG tools cannot jointly model biological replicates, or identify the key areas of the tissue affected by spatial variability.

Results

Here, we introduce DESpace, a framework, based on an original application of existing methods, to discover SVGs. In particular, our approach inputs all types of SRT data, summarizes spatial information via spatial clusters, and identifies spatially variable genes by performing differential gene expression testing between clusters. Furthermore, our fram

SUBMITTER: Cai P 

PROVIDER: S-EPMC10868334 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature