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: 1.0.3 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: 2024-10-31 DOI: 10.32614/CRAN.package.SpatialDDLS Author: Diego Mañanes [aut, cre], Carlos Torroja [aut], Fatima Sanchez-Cabo [aut] Maintainer: Diego Mañanes <dmananesc at cnic.es> BugReports: https://github.com/diegommcc/SpatialDDLS/issues License: GPL-3 URL: https://diegommcc.github.io/SpatialDDLS/, https://github.com/diegommcc/SpatialDDLS NeedsCompilation: no SystemRequirements: Python (>= 2.7.0), TensorFlow (https://www.tensorflow.org/) Citation: SpatialDDLS citation info Materials: README NEWS CRAN checks: SpatialDDLS results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=SpatialDDLS to link to this page.
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