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.">

SpatialDDLS: Deconvolution of Spatial Transcriptomics Data Based on Neural Networks (original) (raw)

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 ORCID iD [aut, cre], Carlos Torroja ORCID iD [aut], Fatima Sanchez-CaboORCID iD [aut]
Maintainer: Diego Mañanes
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

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