Package: BASiCS
Type: Package
Title: Bayesian Analysis of Single-Cell Sequencing data
Version: 1.0.1
Date: : 2018-03-18
Authors@R: c(person("Catalina", "Vallejos", role=c("aut", "cre"),
        email="cnvallej@uc.cl"), person("Nils", "Eling", 
        role=c("aut"), email="eling@ebi.ac.uk"),  
        person("Sylvia", "Richardson", role = c("ctb")), 
        person("John", "Marioni", role=c("ctb"))) 
Maintainer: Catalina A. Vallejos <cnvallej@uc.cl>
Description: Single-cell mRNA sequencing can uncover novel cell-to-cell
 heterogeneity in gene expression levels in seemingly homogeneous populations 
 of cells. However, these experiments are prone to high levels of technical noise, 
 creating new challenges for identifying genes that show genuine heterogeneous 
 expression within the population of cells under study. BASiCS (Bayesian Analysis 
 of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to 
 perform statistical analyses of single-cell RNA sequencing datasets in the context 
 of supervised experiments (where the groups of cells of interest are known a priori, 
 e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation 
 (global scaling) and technical noise quantification (based on spike-in genes). 
 BASiCS provides an intuitive detection criterion for highly (or lowly) 
 variable genes within a single group of cells. Additionally, BASiCS can 
 compare gene expression patterns between two or more pre-specified groups of cells. 
 Unlike traditional differential expression tools, BASiCS quantifies changes 
 in expression that lie beyond comparisons of means, also allowing the study 
 of changes in cell-to-cell heterogeneity. The latter are quantified via a 
 biological over-dispersion parameter that measures residual over-dispersion 
 (with respect to Poisson sampling) after normalisation and technical noise removal. 
License: GPL (>= 2)
Depends: R (>= 3.4), SingleCellExperiment
Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp (>=
        0.11.3), methods, coda, scran, testthat, data.table,
        matrixStats, graphics, KernSmooth, grDevices, stats, utils
Suggests: knitr, BiocStyle
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
biocViews: Normalization, Sequencing, RNASeq, Software, GeneExpression,
        Transcriptomics, SingleCell, DifferentialExpression, Bayesian,
        CellBiology
URL: https://github.com/catavallejos/BASiCS
BugReports: https://github.com/catavallejos/BASiCS/issues
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-03-19 01:53:02 UTC; biocbuild
Author: Catalina Vallejos [aut, cre],
  Nils Eling [aut],
  Sylvia Richardson [ctb],
  John Marioni [ctb]
Built: R 3.4.3; i386-w64-mingw32; 2018-03-19 02:25:36 UTC; windows
Archs: i386, x64
