Benchmarking and integration of methods for deconvoluting spatial transcriptomic data.

ABSTRACT:

Motivation

The rapid development of spatial transcriptomics (ST) approaches has provided new insights into understanding tissue architecture and function. However, the gene expressions measured at a spot may contain contributions from multiple cells due to the low-resolution of current ST technologies. Although many computational methods have been developed to disentangle discrete cell types from spatial mixtures, the community lacks a thorough evaluation of the performance of those deconvolution methods.

Results

Here, we present a comprehensive benchmarking of 14 deconvolution methods on four datasets. Furthermore, we investigate the robustness of different methods to sequencing depth, spot size and the choice of normalization. Moreover, we propose a new ensemble learning-b

SUBMITTER: Yan L 

PROVIDER: S-EPMC9825747 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature