BASiCS

DOI: 10.18129/B9.bioc.BASiCS    

Bayesian Analysis of Single-Cell Sequencing data

Bioconductor version: Release (3.11)

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], Alan O'Callaghan [aut], Sylvia Richardson [ctb], John Marioni [ctb]

Maintainer: Catalina Vallejos <catalina.vallejos at igmm.ed.ac.uk>

Installation

To install this package, start R (version "4.0") and enter:

if (!requireNamespace("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")

 

HTML	R Script	Introduction to BASiCS
PDF		Reference Manual
Text		NEWS

Details

biocViews	Bayesian, CellBiology, DifferentialExpression, GeneExpression, ImmunoOncology, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics
Version	2.0.0
In Bioconductor since	BioC 3.6 (R-3.4) (3 years)
License	GPL (>= 2)
Depends	R (>= 4.0), SingleCellExperiment
Imports	Biobase, BiocGenerics, coda, cowplot, ggExtra, ggplot2, graphics, grDevices, KernSmooth, MASS, methods, Rcpp (>= 0.11.3), S4Vectors, scran, stats, stats4, SummarizedExperiment, viridis, utils, Matrix, matrixStats, assertthat, reshape2, hexbin
LinkingTo	Rcpp, RcppArmadillo
Suggests	BiocStyle, knitr, rmarkdown, testthat, magick
SystemRequirements	C++11
Enhances
URL	https://github.com/catavallejos/BASiCS
BugReports	https://github.com/catavallejos/BASiCS/issues
Depends On Me
Imports Me
Suggests Me	splatter
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	BASiCS_2.0.0.tar.gz
Windows Binary	BASiCS_2.0.0.zip (32- & 64-bit)
macOS 10.13 (High Sierra)	BASiCS_2.0.0.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 3.11	Source Archive