Package: r-cran-seurat
Version: 5.4.0-1
Architecture: amd64
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Installed-Size: 4378
Depends: r-api-4.0, r-cran-seuratobject (>= 5.0.2), r-cran-cluster, r-cran-cowplot, r-cran-fastdummies, r-cran-fitdistrplus, r-cran-future, r-cran-future.apply, r-cran-generics (>= 0.1.3), r-cran-ggplot2 (>= 3.3.0), r-cran-ggrepel, r-cran-ggridges, r-cran-httr, r-cran-ica, r-cran-igraph, r-cran-irlba, r-cran-jsonlite, r-cran-kernsmooth, r-cran-lifecycle, r-cran-lmtest, r-cran-mass, r-cran-matrix (>= 1.5-0), r-cran-matrixstats, r-cran-miniui, r-cran-patchwork, r-cran-pbapply, r-cran-plotly (>= 4.9.0), r-cran-png, r-cran-progressr, r-cran-rann, r-cran-rcolorbrewer, r-cran-rcpp (>= 1.0.7), r-cran-rcppannoy (>= 0.0.18), r-cran-rcpphnsw, r-cran-reticulate, r-cran-rlang, r-cran-rocr, r-cran-rspectra, r-cran-rtsne, r-cran-scales, r-cran-scattermore (>= 1.2), r-cran-sctransform (>= 0.4.1), r-cran-shiny, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-tibble, r-cran-uwot (>= 0.1.10), r-cran-rcppeigen, r-cran-rcppprogress, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14)
Recommends: r-cran-ape, r-cran-base64enc, r-bioc-biobase, r-bioc-biocgenerics, r-cran-data.table, r-bioc-deseq2, r-bioc-delayedarray, r-bioc-genomicranges, r-bioc-genomeinfodb, r-bioc-glmgampoi, r-cran-ggrastr, r-cran-hdf5r, r-bioc-iranges, r-cran-leidenbase, r-bioc-limma, r-cran-magrittr, r-cran-metap, r-cran-mixtools, r-bioc-monocle, r-cran-rsvd, r-cran-r.utils, r-bioc-rtracklayer, r-bioc-s4vectors, r-cran-sf (>= 1.0-0), r-cran-sp, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-vgam
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=Seurat
Description: Tools for Single Cell Genomics
 A toolkit for quality control, analysis, and exploration of single cell
 RNA sequencing data. 'Seurat' aims to enable users to identify and
 interpret sources of heterogeneity from single cell transcriptomic
 measurements, and to integrate diverse types of single cell data. See
 Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>,
 Macosko E, Basu A, Satija R, et al (2015)
 <doi:10.1016/j.cell.2015.05.002>, and Butler A and Satija R (2017)
 <doi:10.1101/164889> for more details.
