A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.

ABSTRACT: Spatial transcriptomics (ST) technologies allow researchers to examine transcriptional profiles along with maintained positional information. Such spatially resolved transcriptional characterization of intact tissue samples provides an integrated view of gene expression in its natural spatial and functional context. However, high-throughput sequencing-based ST technologies cannot yet reach single cell resolution. Thus, similar to bulk RNA-seq data, gene expression data at ST spot-level reflect transcriptional profiles of multiple cells and entail the inference of cell-type composition within each ST spot for valid and powerful subsequent analyses. Realizing the critical importance of cell-type decomposition, multiple groups have developed ST deconvolution methods. The aim of this work is t

SUBMITTER: Chen J 

PROVIDER: S-EPMC9294426 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature