SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks.

ABSTRACT:

Summary

Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data. SpatialDDLS leverages single-cell RNA sequencing data to simulate mixed transcriptional profiles with predefined cellular composition, which are subsequently used to train a fully connected neural network to uncover cell type diversity within each spot. By comparing it with two state-of-the-art spatial deconvolution methods, we demonstrate that SpatialDDLS is an accurate and fast alternative to the available sta

SUBMITTER: Mananes D 

PROVIDER: S-EPMC10881086 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature