Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using 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> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.
| Version: |
1.0.1 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
rlang, grr, Matrix, methods, SpatialExperiment, SingleCellExperiment, SummarizedExperiment, zinbwave, stats, pbapply, S4Vectors, dplyr, reshape2, gtools, reticulate, keras, tensorflow, FNN, ggplot2, ggpubr, scran, scuttle |
| Suggests: |
knitr, rmarkdown, BiocParallel, rhdf5, DelayedArray, DelayedMatrixStats, HDF5Array, testthat, ComplexHeatmap, grid, bluster, lsa, irlba |
| Published: |
2024-03-15 |
| 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 |