plot-BASiCS_Chain-method

plot

plot,BASiCS_Chain-method

plot,BASiCS_Chain,ANY-method

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

Name of the slot to be used for the plot. 
Possible values: <code>'mu'</code>, <code>'delta'</code>, 
<code>'phi'</code>, <code>'s'</code>, <code>'nu'</code> and <code>'theta'</code>

Param

Specifies which gene is requested. 
Required only if <code>Param = 'mu'</code> or <code>'delta'</code>

Gene

Specifies which cell is requested. 
Required only if <code>Param = 'phi', 's'</code> or <code>'nu'</code>

Cell

Specifies which batch is requested. 
Required only if <code>Param = 'theta'</code>

Batch

As in <code><a rd-options="graphics" href="/link/par?package=BASiCS&version=1.1.0&to=graphics" data-mini-rdoc="graphics::par">par</a></code>.

ylab

xlab

Other graphical parameters (see <code><a rd-options="graphics" href="/link/par?package=BASiCS&version=1.1.0&to=graphics" data-mini-rdoc="graphics::par">par</a></code>).

'plot' method for <code><a href="/link/?package=BASiCS&version=1.1.0" basics_chain'="" data-mini-rdoc="BASiCS::"></a>'>BASiCS_Chain</code> objects

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

plot-BASiCS_Chain-method function

A <code><a href='<a href='BASiCS_Chain'></a>'>BASiCS_Chain</a></code> object.

As in <code><a rd-options='graphics' href='par'>par</a></code>.

Other graphical parameters (see <code><a rd-options='graphics' href='par'>par</a></code>).

'plot' method for <code><a href='<a href='BASiCS_Chain'></a>'>BASiCS_Chain</a></code> objects

plot-BASiCS_Chain-method: 'plot' method for BASiCS_Chain objects

Description

Usage

Arguments

Value

References

Examples