BASiCS
This package is for version 3.7 of Bioconductor; for the stable, up-to-date release version, see BASiCS.
Bayesian Analysis of Single-Cell Sequencing data
Bioconductor version: 3.7
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.
Author: Catalina Vallejos [aut, cre], Nils Eling [aut], Sylvia Richardson [ctb], John Marioni [ctb]
Maintainer: Catalina A. Vallejos <cnvallej at uc.cl>, Nils Eling <eling at ebi.ac.uk>
citation("BASiCS")
):
Installation
To install this package, start R (version "3.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("BASiCS")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("BASiCS")
Introduction to BASiCS | HTML | R Script |
Reference Manual | ||
NEWS | Text |
Details
biocViews | Bayesian, CellBiology, DifferentialExpression, GeneExpression, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics |
Version | 1.2.1 |
In Bioconductor since | BioC 3.6 (R-3.4) (6.5 years) |
License | GPL (>= 2) |
Depends | R (>= 3.5), SingleCellExperiment |
Imports | BiocGenerics, methods, coda, data.table, ggplot2, graphics, grDevices, KernSmooth, MASS, matrixStats, Rcpp (>= 0.11.3), S4Vectors, scran, stats, SummarizedExperiment, testthat, utils |
System Requirements | |
URL | https://github.com/catavallejos/BASiCS |
Bug Reports | https://github.com/catavallejos/BASiCS/issues |
See More
Suggests | BiocStyle, knitr, rmarkdown |
Linking To | Rcpp, RcppArmadillo |
Enhances | |
Depends On Me | |
Imports Me | |
Suggests Me | splatter |
Links To Me | |
Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
Source Package | BASiCS_1.2.1.tar.gz |
Windows Binary | BASiCS_1.2.1.zip (32- & 64-bit) |
Mac OS X 10.11 (El Capitan) | BASiCS_1.2.1.tgz |
Source Repository | git clone https://git.bioconductor.org/packages/BASiCS |
Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/BASiCS |
Package Short Url | https://bioconductor.org/packages/BASiCS/ |
Package Downloads Report | Download Stats |
Old Source Packages for BioC 3.7 | Source Archive |