ArticleBioinformatics (Oxford, England)2024
SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- SpatioFreq: A Deep Learning Framework for Decoding Cellular and Tissue Landscapes Across Organisms Using Spatial Transcriptomics.Interdisciplinary sciences, computational life sciences · 2026Article
- KGLAR: Deconvoluting Spatial Transcriptomics Data with Single-cell Transcriptomes through Knowledge-guided NMF and Least Angle Regression.Interdisciplinary sciences, computational life sciences · 2026Article
- WDGTA: a wavelet-denoised graph attention framework for spatial transcriptomics deconvolution.Briefings in bioinformatics · 2026Article
- Article
- CSsingle: a unified tool for robust decomposition of bulk and spatial transcriptomic data across diverse single-cell references.Nucleic acids research · 2026Article
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- Mouse-Specific Single cell cytokine activity prediction and Estimation (MouSSE).PLoS computational biology · 2025Article
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Spatial transcriptomics in cancer research and potential clinical impact: a narrative review.Journal of cancer research and clinical oncology · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
summarySpatial 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.