BASiCS_VarThresholdSearchHVG

BASiCS_VarThresholdSearchHVG_LVG

BASiCS_VarThresholdSearchLVG

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

Grid of values for the variance contribution 
threshold (they must be contained in (0,1))

VarThresholdsGrid

Target for expected false discovery rate related to 
HVG/LVG detection. Default: <code>EFDR = 0.10</code>.

EFDR

If <code>Progress = TRUE</code>, partial output is 
printed in the console. Default: <code>Progress = TRUE</code>.

Progress

Detection method for highly and lowly variable genes 
using a grid of variance contribution thresholds

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_VarThresholdSearchHVG function

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

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BASiCS_VarThresholdSearchHVG: Detection method for highly and lowly variable genes using a grid of variance contribution thresholds

Description

Usage

Arguments

Value

Details

References

See Also

Examples