Deconvolution of spatial transcriptomics data using deconvolution models based on deep neural networks and single-cell RNA-seq data. These models are able to make accurate estimates of the cell composition of spots in spatial transcriptomics datasets from the same context using the advances provided by deep learning and the meaningful information provided by single-cell RNA-Seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> to get an overview of the method, but its application to spatial transcriptomics data will be available soon.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
rlang, grr, Matrix, methods, tidyr, SpatialExperiment, SingleCellExperiment, SummarizedExperiment, zinbwave, stats, pbapply, S4Vectors, dplyr, tools, reshape2, gtools, reticulate, keras, tensorflow, ggplot2, ggpubr, RColorBrewer, grDevices |
| Suggests: |
knitr, rmarkdown, BiocParallel, rhdf5, DelayedArray, DelayedMatrixStats, HDF5Array, testthat |
| Published: |
2023-05-07 |
| Author: |
Diego Mañanes 
[aut, cre],
Carlos Torroja
[aut],
Fatima Sanchez-Cabo
[aut] |
| Maintainer: |
Diego Mañanes <dmananesc at cnic.es> |
| BugReports: |
https://github.com/diegommcc/SpatialDDLS/issues |
| License: |
GPL-3 |
| URL: |
https://diegommcc.github.io/SpatialDDLS/,
https://github.com/diegommcc/SpatialDDLS |
| NeedsCompilation: |
no |
| SystemRequirements: |
Python (>= 2.7.0), TensorFlow
(https://www.tensorflow.org/) |
| Materials: |
README NEWS |
| CRAN checks: |
SpatialDDLS results |