BASiCS_Sim

Gene-specific mean expression parameters \(\mu_i\) for all 
biological genes (vector of length <code>q.bio</code>, 
all elements must be positive numbers)

\(\mu_i\) for all technical genes defined as true 
input molecules (vector of length <code>q-q.bio</code>, 
all elements must be positive numbers)

Mu_spikes

Gene-specific biological over-dispersion parameters 
\(\delta_i\), biological genes only
(vector of length <code>q.bio</code>, all elements must be positive numbers)

Delta

Cell-specific mRNA content normalising parameters \(\phi_j\)
(vector of length <code>n</code>, all elements must be positive numbers and 
the sum of its elements must be equal to <code>n</code>)

Cell-specific technical normalising parameters \(s_j\)
(vector of length <code>n</code>, all elements must be positive numbers)

Technical variability parameter \(\theta\) (must be positive)

Theta

<code>BASiCS_Sim</code> creates a simulated dataset from the model 
implemented in 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.

BASiCS_Sim: Generates synthetic data according to the model implemented in BASiCS

Description

Usage

Arguments

Value

References

Examples