digitalDLSorteR - 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) <doi:10.3389/fgene.2019.00978> for more details.
Last updated 27 days ago
deconvolutiondeep-learningrna-seqsingle-cell
6.10 score 9 stars 5 scripts 97 downloads
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.
Last updated 27 days ago
deconvolutiondeep-learningneural-networkspatial-transcriptomics
5.13 score 3 stars 1 scripts 478 downloads