BASiCS_Filter

Matrix of dimensions <code>q</code> times <code>n</code> whose elements 
corresponds to the simulated expression counts.
First <code>q.bio</code> rows correspond to biological genes. Last <code>q-q.bio</code> 
rows correspond to technical spike-in genes.

Counts

Logical vector of length <code>q</code>. If <code>Tech = FALSE</code> 
the gene is biological; otherwise the gene is spike-in.

Tech

Vector of length <code>q-q.bio</code> whose elements indicate 
the simulated input concentrations for the spike-in genes.

SpikeInput

Vector of length <code>n</code> whose elements indicate batch 
information. Not required if a single batch is present on the data. 
Default: <code>BatchInfo = NULL</code>.

BatchInfo

Minimum value of total expression counts 
required per cell (biological and technical). 
Default: <code>MinTotalCountsPerCell = 2</code>.

MinTotalCountsPerCell

Minimum value of total expression counts 
required per transcript (biological and technical). 
Default: <code>MinTotalCountsPerGene = 2</code>.

MinTotalCountsPerGene

Minimum number of cells where expression 
must be detected (positive count). Criteria applied to each transcript. 
Default: <code>MinCellsWithExpression = 2</code>.

MinCellsWithExpression

Minimum average number of 
counts per cells where expression is detected. Criteria applied to 
each transcript. Default value: <code>MinAvCountsPerCellsWithExpression = 2</code>.

MinAvCountsPerCellsWithExpression

<code>BASiCS_Filter</code> indicates which transcripts and 
cells pass a pre-defined inclusion criteria. The output of this 
function can be combined with <code>newBASiCS_Data</code> to generate a 
the <code><a href="/link/?package=BASiCS&version=1.1.0" singlecellexperiment'="" data-mini-rdoc="BASiCS::"></a>'>SingleCellExperiment</code> object required to run BASiCS.

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_Filter function

<code>BASiCS_Filter</code> indicates which transcripts and 
cells pass a pre-defined inclusion criteria. The output of this 
function can be combined with <code>newBASiCS_Data</code> to generate a 
the <code><a href='<a href='SingleCellExperiment'></a>'>SingleCellExperiment</a></code> object required to run BASiCS.

BASiCS_Filter: Filter for input datasets

Description

Usage

Arguments

Value

References

Examples