SpatialDDLS: Deconvolution of Spatial Transcriptomics Data Based on Neural Networks

Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.

Version: 1.0.2
Depends: R (≥ 4.0.0)
Imports: rlang, grr, Matrix, methods, SpatialExperiment, SingleCellExperiment, SummarizedExperiment, zinbwave, stats, pbapply, S4Vectors, dplyr, reshape2, gtools, reticulate, keras, tensorflow, FNN, ggplot2, ggpubr, scran, scuttle
Suggests: knitr, rmarkdown, BiocParallel, rhdf5, DelayedArray, DelayedMatrixStats, HDF5Array, testthat, ComplexHeatmap, grid, bluster, lsa, irlba
Published: 2024-04-26
DOI: 10.32614/CRAN.package.SpatialDDLS
Author: Diego Mañanes ORCID iD [aut, cre], Carlos Torroja ORCID iD [aut], Fatima Sanchez-Cabo ORCID iD [aut]
Maintainer: Diego Mañanes <dmananesc at>
License: GPL-3
NeedsCompilation: no
SystemRequirements: Python (>= 2.7.0), TensorFlow (
Citation: SpatialDDLS citation info
Materials: README NEWS
CRAN checks: SpatialDDLS results


Reference manual: SpatialDDLS.pdf
Vignettes: Get started! Deconvolution of mouse lymph node samples


Package source: SpatialDDLS_1.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): SpatialDDLS_1.0.2.tgz, r-oldrel (arm64): not available, r-release (x86_64): SpatialDDLS_1.0.2.tgz, r-oldrel (x86_64): not available
Old sources: SpatialDDLS archive


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