plot-BASiCS_Summary-method

plot,BASiCS_Summary-method,

plot,BASiCS_Summary,ANY-method

A <code><a href="/link/?package=BASiCS&version=1.1.0" basics_summary'="" data-mini-rdoc="BASiCS::"></a>'>BASiCS_Summary</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

Name of the second slot to be used for the plot. 
Possible values: <code>'mu'</code>, <code>'delta'</code>, 
<code>'phi'</code>, <code>'s'</code> and <code>'nu'</code> 
(combinations between gene-specific and 
cell-specific parameters are not admitted)

Param2

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

Genes

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

Cells

Specifies which batches are requested. 
Required only if <code>Param = 'theta'</code>

Batches

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>.

xlab

ylab

xlim

ylim

Logical parameter. If <code>TRUE</code>, 
transparency will be added to the color of the dots.

SmoothPlot

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_summary'="" data-mini-rdoc="BASiCS::"></a>'>BASiCS_Summary</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_Summary-method function

A <code><a href='<a href='BASiCS_Summary'></a>'>BASiCS_Summary</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_Summary'></a>'>BASiCS_Summary</a></code> objects

plot-BASiCS_Summary-method: 'plot' method for BASiCS_Summary objects

Description

Usage

Arguments

Value

References

Examples