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SpatialDDLS: Deconvolution of Spatial Transcriptomics Data Based on Neural Networks
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.
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Package details |
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| Author | Diego Mañanes [aut, cre] (<https://orcid.org/0000-0001-7247-6794>), Carlos Torroja [aut] (<https://orcid.org/0000-0001-8914-3400>), Fatima Sanchez-Cabo [aut] (<https://orcid.org/0000-0003-1881-1664>) |
| Maintainer | Diego Mañanes <dmananesc@cnic.es> |
| License | GPL-3 |
| Version | 1.0.3 |
| URL | https://diegommcc.github.io/SpatialDDLS/ https://github.com/diegommcc/SpatialDDLS |
| Package repository | View on CRAN |
| Installation |
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