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 |
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 |
Reference manual: | SpatialDDLS.pdf |
Vignettes: |
Get started! Deconvolution of mouse lymph node samples (source) |
Package source: | SpatialDDLS_1.0.3.tar.gz |
Windows binaries: | r-devel: SpatialDDLS_1.0.3.zip, r-release: SpatialDDLS_1.0.3.zip, r-oldrel: SpatialDDLS_1.0.3.zip |
macOS binaries: | r-devel (arm64): SpatialDDLS_1.0.3.tgz, r-release (arm64): SpatialDDLS_1.0.3.tgz, r-oldrel (arm64): not available, r-devel (x86_64): SpatialDDLS_1.0.3.tgz, r-release (x86_64): SpatialDDLS_1.0.3.tgz, r-oldrel (x86_64): not available |
Old sources: | SpatialDDLS archive |
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