CRAN/E | digitalDLSorteR

digitalDLSorteR

Deconvolution of Bulk RNA-Seq Data Based on Deep Learning

Installation

About

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.

Citation digitalDLSorteR citation info
diegommcc.github.io/digitalDLSorteR/
github.com/diegommcc/digitalDLSorteR
System requirements Python (>= 2.7.0), TensorFlow (https://www.tensorflow.org/)
Bug report File report

Key Metrics

Version 1.0.1
R ≥ 4.0.0
Published 2024-02-07 73 days ago
Needs compilation? no
License GPL-3
CRAN checks digitalDLSorteR results

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Maintainer

Maintainer

Diego Mañanes

dmananesc@cnic.es

Authors

Diego Mañanes

aut / cre

Carlos Torroja

aut

Fatima Sanchez-Cabo

aut

Material

README
NEWS
Reference manual
Package source

Vignettes

Building new deconvolution models

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

digitalDLSorteR archive

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

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