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 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 implementation The 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 . Contact fscabo@cnic.es Supplementary information Supplementary data are available at Bioinformatics online.

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