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DESCRIPTION
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DESCRIPTION
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Package: SpatialDDLS
Type: Package
Title: Deconvolution of Spatial Transcriptomics Data Based on Neural Networks
Version: 1.0.2
Authors@R: c(
person(given = "Diego", family = "Mañanes", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7247-6794")),
person(given = "Carlos", family = "Torroja", email = "[email protected]", role = "aut", comment = c(ORCID = "0000-0001-8914-3400")),
person(given = "Fatima", family = "Sanchez-Cabo", email = "[email protected]", role = "aut", comment = c(ORCID = "0000-0003-1881-1664")))
Maintainer: Diego Mañanes <[email protected]>
Description: 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.
License: GPL-3
URL: https://diegommcc.github.io/SpatialDDLS/, https://github.com/diegommcc/SpatialDDLS
BugReports: https://github.com/diegommcc/SpatialDDLS/issues
Encoding: UTF-8
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
SystemRequirements: Python (>= 2.7.0), TensorFlow (https://www.tensorflow.org/)
RoxygenNote: 7.2.3
Roxygen: list(markdown = TRUE)
Collate:
'AllClasses.R'
'AllGenerics.R'
'SpatialDDLS.R'
'dnnModel.R'
'evalMetrics.R'
'interGradientsDL.R'
'loadData.R'
'plotSpatialCoor.R'
'simMixedSpots.R'
'simSingleCell.R'
'spatialClustering.R'
'utils.R'
VignetteBuilder: knitr