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: 0.2.0
Depends: R (≥ 4.0.0)
Imports: rlang, Matrix, Matrix.utils, methods, tidyr, SingleCellExperiment, SummarizedExperiment, splatter, zinbwave, stats, pbapply, S4Vectors, dplyr, tools, reshape2, gtools, edgeR, reticulate, keras, tensorflow, ggplot2, ggpubr, RColorBrewer
Suggests: knitr, rmarkdown, BiocParallel, rhdf5, DelayedArray, DelayedMatrixStats, HDF5Array, testthat
Published: 2022-03-10
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: HDF5 files as back-end
Keras/TensorFlow installation and configuration
Building new deconvolution models
Using pre-trained context-specific deconvolution models
Performance of a real model: deconvolution of colorectal cancer samples


Package source: digitalDLSorteR_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): not available, r-release (x86_64): digitalDLSorteR_0.2.0.tgz, r-oldrel: not available
Old sources: digitalDLSorteR archive


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