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: | 0.3.0 |
| Depends: | R (≥ 4.0.0) |
| Imports: | rlang, Matrix, Matrix.utils, methods, tidyr, SingleCellExperiment, SummarizedExperiment, zinbwave, stats, pbapply, S4Vectors, dplyr, tools, reshape2, gtools, reticulate, keras, tensorflow, ggplot2, ggpubr, RColorBrewer |
| Suggests: | knitr, rmarkdown, BiocParallel, rhdf5, DelayedArray, DelayedMatrixStats, HDF5Array, testthat |
| Published: | 2022-05-24 |
| Author: | Diego Mañanes |
| 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 |
| Package source: | digitalDLSorteR_0.3.0.tar.gz |
| Windows binaries: | r-devel: digitalDLSorteR_0.3.0.zip, r-release: digitalDLSorteR_0.3.0.zip, r-oldrel: digitalDLSorteR_0.3.0.zip |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): digitalDLSorteR_0.3.0.tgz, r-oldrel (x86_64): digitalDLSorteR_0.3.0.tgz |
| Old sources: | digitalDLSorteR archive |
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