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An unsupervised deep learning method for data alignment, integration and estimation of per-cell differences in -omic data (e.g. gene expression) across datasets (conditions, tissues, species). See Johansen and Quon (2019) <doi:10.1101/504944> for more details.
Package details |
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Author | Nelson Johansen [aut, cre], Gerald Quon [aut] |
Bioconductor views | DimensionReduction NeuralNetwork SingleCell Transcriptomics |
Maintainer | Nelson Johansen <njjohansen@ucdavis.edu> |
License | GPL-3 |
Version | 1.3.0 |
URL | https://github.com/quon-titative-biology/scAlign |
Package repository | View on Bioconductor |
Installation |
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