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 |
| Author: |
Diego Mañanes 
[aut, cre],
Carlos Torroja
[aut],
Fatima Sanchez-Cabo
[aut] |
| Maintainer: |
Diego Mañanes <dmananesc at cnic.es> |
| BugReports: |
https://github.com/diegommcc/digitalDLSorteR/issues |
| License: |
GPL-3 |
| URL: |
https://diegommcc.github.io/digitalDLSorteR/,
https://github.com/diegommcc/digitalDLSorteR |
| NeedsCompilation: |
no |
| SystemRequirements: |
Python (>= 2.7.0), TensorFlow
(https://www.tensorflow.org/) |
| Citation: |
digitalDLSorteR citation info |
| Materials: |
README NEWS |
| CRAN checks: |
digitalDLSorteR results |