SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning.

ABSTRACT: Spatially resolved transcriptomics (SRT) has advanced our understanding of the spatial patterns of gene expression, but the lack of single-cell resolution in spatial barcoding-based SRT hinders the inference of specific locations of individual cells. To determine the spatial distribution of cell types in SRT, we present SpaDecon, a semi-supervised learning approach that incorporates gene expression, spatial location, and histology information for cell-type deconvolution. SpaDecon was evaluated through analyses of four real SRT datasets using knowledge of the expected distributions of cell types. Quantitative evaluations were performed for four pseudo-SRT datasets constructed according to benchmark proportions. Using mean squared error and Jensen-Shannon divergence with the benchmark propor

SUBMITTER: Coleman K 

PROVIDER: S-EPMC10082183 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature