Package: BASiCS
Type: Package
Title: Bayesian Analysis of Single-Cell Sequencing data
Version: 1.4.7
Date: : 2019-03-22
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("Alan", "O'Callaghan", role = c("ctb")),
        person("Sylvia", "Richardson", role = c("ctb")), 
        person("John", "Marioni", role=c("ctb"))) 
Maintainer: Catalina A. Vallejos <cnvallej@uc.cl>, Nils Eling <eling@ebi.ac.uk>
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 can be quantified via a biological over-dispersion 
 parameter that measures the excess of variability that is observed with respect 
 to Poisson sampling noise, after normalisation and technical noise removal. 
 Due to the strong mean/over-dispersion confounding that is typically observed 
 for scRNA-seq datasets, BASiCS also tests for changes in residual 
 over-dispersion, defined by residual values with respect to a global 
 mean/over-dispersion trend. 
License: GPL (>= 2)
Depends: R (>= 3.5), SingleCellExperiment, KernSmooth, ggplot2
Imports: BiocGenerics, methods, coda, data.table, graphics, grDevices,
        MASS, matrixStats, Rcpp (>= 0.11.3), S4Vectors, scran, stats,
        SummarizedExperiment, testthat, utils
Suggests: BiocStyle, knitr, rmarkdown
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software,
        GeneExpression, Transcriptomics, SingleCell,
        DifferentialExpression, Bayesian, CellBiology
SystemRequirements: C++11
NeedsCompilation: yes
URL: https://github.com/catavallejos/BASiCS
BugReports: https://github.com/catavallejos/BASiCS/issues
RoxygenNote: 6.1.1
git_url: https://git.bioconductor.org/packages/BASiCS
git_branch: RELEASE_3_8
git_last_commit: 56a7c64
git_last_commit_date: 2019-03-22
Date/Publication: 2019-03-22
Packaged: 2019-03-23 03:33:18 UTC; biocbuild
Author: Catalina Vallejos [aut, cre],
  Nils Eling [aut],
  Alan O'Callaghan [ctb],
  Sylvia Richardson [ctb],
  John Marioni [ctb]
Built: R 3.5.3; i386-w64-mingw32; 2019-03-23 10:18:44 UTC; windows
Archs: i386, x64
