Package: scAlign
Version: 1.4.51
Date: 2019-02-11
Title: An alignment and integration method for single cell genomics
Description: 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.
Authors@R:
   c(person(given = "Nelson",
            family = "Johansen",
            role = c("aut", "cre"),
            email = "njjohansen@ucdavis.edu"),
     person(given = "Gerald",
            family = "Quon",
            role = c("aut"),
            email = "gquon@ucdavis.edu"))
URL: https://github.com/quon-titative-biology/scAlign
BugReports: https://github.com/quon-titative-biology/scAlign/issues
biocViews: SingleCell, Transcriptomics, DimensionReduction,
        NeuralNetwork
Depends: R (>= 3.6), SingleCellExperiment (>= 1.4), Seurat (>= 2.3.4), tensorflow,
         purrr, irlba, Rtsne, ggplot2, methods, utils, FNN
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
SystemRequirements: python (< 3.7), tensorflow
RoxygenNote: 7.1.0
License: GPL-3
LazyData: false
Encoding: UTF-8
git_url: https://git.bioconductor.org/packages/scAlign
git_branch: master
git_last_commit: cf44fb3
git_last_commit_date: 2019-05-02
Date/Publication: 2019-05-02
NeedsCompilation: no
Packaged: 2019-05-03 03:13:41 UTC; biocbuild
