plot,BASiCS_Summary-method

plot-BASiCS_Summary-method

A <code>BASiCS_Summary</code> object.

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

Param

Name of the second slot to be used for the plot. Possible values: <code>mu, delta, phi, s, 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=0.3.1&to=graphics" data-mini-rdoc="graphics::par">par</a></code>.

xlab

ylab

xlim

ylim

Other graphical parameters (see <code><a rd-options="graphics" href="/link/par?package=BASiCS&version=0.3.1&to=graphics" data-mini-rdoc="graphics::par">par</a></code>).

'plot' method for BASiCS_Summary 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 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.

Catalina A Vallejos

BASiCS

Bayesian Analysis of Single-Cell Sequencing data

plot-BASiCS_Summary-method function

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

Description

Usage

Arguments

References

Examples