BASiCS_VarianceDecomp

an object of class <code><a href="/link/?package=BASiCS&version=1.1.0" basics_chain'="" data-mini-rdoc="BASiCS::"></a>'>BASiCS_Chain</code>

Chain

Ordering variable for output. 
Possible values: <code>'GeneName'</code>, <code>'BioVarGlobal'</code>,
 <code>'TechVarGlobal'</code> and <code>'ShotNoiseGlobal'</code>. 
Default: <code>OrderVariable = "BioVarGlobal"</code>.

OrderVariable

If <code>TRUE</code>, a barplot of the variance decomposition 
(global and by batches, if any) is generated. Default: <code>Plot = TRUE</code>.

Plot

Other arguments to be passed to <code><a rd-options="graphics" href="/link/barplot?package=BASiCS&version=1.1.0&to=graphics" data-mini-rdoc="graphics::barplot">barplot</a></code>

Function to decompose total variability of gene 
expression into biological and technical components.

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.

Catalina Vallejos

BASiCS

Bayesian Analysis of Single-Cell Sequencing data

Nils Eling

Sylvia Richardson

John Marioni

BASiCS_VarianceDecomp function

an object of class <code><a href='<a href='BASiCS_Chain'></a>'>BASiCS_Chain</a></code>

Other arguments to be passed to <code><a rd-options='graphics' href='barplot'>barplot</a></code>

BASiCS_VarianceDecomp: Decomposition of gene expression variability according to BASiCS

Description

Usage

Arguments

Value

Details

References

See Also

Examples