BASiCS_VarianceDecomp

an object of class <code><a rd-options="BASiCS" href="/link/BASiCS_Data-class?package=BASiCS&version=0.7.30&to=BASiCS" data-mini-rdoc="BASiCS::BASiCS_Data-class">BASiCS_Data-class</a></code>

Data

an object of class <code><a rd-options="BASiCS" href="/link/BASiCS_Chain-class?package=BASiCS&version=0.7.30&to=BASiCS" data-mini-rdoc="BASiCS::BASiCS_Chain-class">BASiCS_Chain-class</a></code>

object

Ordering variable for output. Must take values in <code>c("GeneNames", "BioVarGlobal", "TechVarGlobal", "ShotNoiseGlobal")</code>.

OrderVariable

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

Plot

Other arguments to be passed to <code><a rd-options="graphics" href="/link/barplot?package=BASiCS&version=0.7.30&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 unexplained 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 where: (ii)
cell-specific normalization constants are estimated as part of the model
parameters, (ii) technical variability is quantified based on spike-in
genes that are artificially introduced to each analysed cells lysate and
(iii) the total variability of the expression counts is decomposed into
technical and biological components. BASiCS also provides an intuitive
detection criterion for highly (or lowly) variable genes within the
population of cells under study. This is formalized by means of tail
posterior probabilities associated to high (or low) biological cell-to-cell
variance contributions, quantities that can be easily interpreted by
applied users. Additionally, BASiCS has now been extended to compare gene
expression patterns between pre-specified populations 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 the removal of technical variation.

Catalina Vallejos

BASiCS

Bayesian Analysis of Single-Cell Sequencing data

BASiCS_VarianceDecomp function

an object of class <code><a rd-options='BASiCS' href='BASiCS_Data-class'>BASiCS_Data-class</a></code>

an object of class <code><a rd-options='BASiCS' href='BASiCS_Chain-class'>BASiCS_Chain-class</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