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

SummarySpatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available plaXorms 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 (scRNA-seq) 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 faster alternative to the available state-of-the art tools. Availability and implementationThe R package SpatialDDLS is available via CRAN-The Comprehensive R Archive Network: https://CRAN.R-project.org/package=SpatialDDLS. A detailed manual of the main functionalities implemented in the package can be found at https://diegommcc.github.io/SpatialDDLS. Contactfscabo@cnic.es Supplementary informationSupplementary data are available at Bioinformatics online.