digitalDLSorteR: Deconvolution of Bulk RNA-Seq Data Based on Deep Learning

Deconvolution of bulk RNA-Seq data using context-specific deconvolution models based on Deep Neural Networks using scRNA-Seq data as input. These models are able to make accurate estimates of the cell composition of bulk RNA-Seq samples from the same context using the advances provided by Deep Learning and the meaningful information provided by scRNA-Seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> for more details.

Version: 1.0.1
Depends: R (≥ 4.0.0)
Imports: rlang, grr, Matrix, methods, tidyr, SingleCellExperiment, SummarizedExperiment, zinbwave, stats, pbapply, S4Vectors, dplyr, tools, reshape2, gtools, reticulate, keras, tensorflow, ggplot2, ggpubr, scran, scuttle
Suggests: knitr, rmarkdown, BiocParallel, rhdf5, DelayedArray, DelayedMatrixStats, HDF5Array, testthat
Published: 2024-02-07
DOI: 10.32614/CRAN.package.digitalDLSorteR
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: digitalDLSorteR citation info
Materials: README NEWS
CRAN checks: digitalDLSorteR results


Reference manual: digitalDLSorteR.pdf
Vignettes: Building new deconvolution models


Package source: digitalDLSorteR_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): digitalDLSorteR_1.0.1.tgz, r-oldrel (arm64): digitalDLSorteR_1.0.1.tgz, r-release (x86_64): digitalDLSorteR_1.0.1.tgz, r-oldrel (x86_64): digitalDLSorteR_1.0.1.tgz
Old sources: digitalDLSorteR archive


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