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) for more details.


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0.1.1 by Diego Mañanes, 2 days ago,

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Authors: Diego Mañanes [aut, cre] , Carlos Torroja [aut] , Fatima Sanchez-Cabo [aut]

Documentation:   PDF Manual  

GPL-3 license

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

System requirements: Python (>= 2.7.0), TensorFlow (

See at CRAN