2026-04-29T15:25:25Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/251282025-12-18T13:00:50Zcom_20.500.12105_19604com_20.500.12105_2051col_20.500.12105_19605

00925njm 22002777a 4500 dc Mañanes, Diego author Rivero-García, Inés author Relaño, Carlos author Torres, Miguel author Sancho, David author Jimenez-Carretero, Daniel author Torroja, Carlos author Sánchez-Cabo, Fátima author 2024-02-01 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 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. Bioinformatics. 2024 Feb 1;40(2):btae072. 1367-4803 38366652 https://hdl.handle.net/20.500.12105/25128 SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks.