Package: simpleSingleCell
Title: A step-by-step workflow for low-level analysis of single-cell
        RNA-seq data with Bioconductor
Version: 1.0.3
Date: 2017-09-19
Authors@R: c(person(role=c("aut", "cre"), "Aaron", "Lun", email = "alun@wehi.edu.au"),
        person(role="aut", "Davis", "McCarthy", email = "davis@ebi.ac.uk"),
        person(role="aut", "John", "Marioni", email = "marioni@ebi.ac.uk"))
Description: This workflow implements a low-level analysis pipeline for scRNA-seq data using scran, scater and other Bioconductor packages.
             It describes how to perform quality control on the libraries, normalization of cell-specific biases, basic data exploration 
             and cell cycle phase identification. Procedures to detect highly variable genes, significantly correlated genes and 
             subpopulation-specific marker genes are also shown. These analyses are demonstrated on a range of publicly available scRNA-seq data sets. 
Depends: R (>= 3.3.0), BiocStyle, knitr, BiocParallel, Rtsne,
        mvoutlier, destiny, readxl, SingleCellExperiment, scater,
        org.Mm.eg.db, scran, limma, pheatmap, dynamicTreeCut, cluster,
        edgeR, TxDb.Mmusculus.UCSC.mm10.ensGene
License: Artistic-2.0
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
biocViews: SingleCell, RNASeq
Suggests: knitr, rmarkdown
NeedsCompilation: no
URL: https://www.bioconductor.org/help/workflows/simpleSingleCell/
Packaged: 2017-10-24 16:19:03 UTC; jenkins
Author: Aaron Lun [aut, cre],
  Davis McCarthy [aut],
  John Marioni [aut]
Maintainer: Aaron Lun <alun@wehi.edu.au>
