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Luke Zappia authoredLuke Zappia authored
SplatParams.Rd 5.66 KiB
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllClasses.R
\docType{class}
\name{SplatParams}
\alias{SplatParams}
\alias{SplatParams-class}
\title{The SplatParams class}
\description{
S4 class that holds parameters for the Splatter simulation.
}
\section{Parameters}{
The Splatter simulation requires the following parameters:
\describe{
\item{\code{nGenes}}{The number of genes to simulate.}
\item{\code{nCells}}{The number of cells to simulate.}
\item{\code{[seed]}}{Seed to use for generating random numbers.}
\item{\emph{Batch parameters}}{
\describe{
\item{\code{[nBatches]}}{The number of batches to simulate.}
\item{\code{[batchCells]}}{Vector giving the number of cells in
each batch.}
\item{\code{[batch.facLoc]}}{Location (meanlog) parameter for the
batch effect factor log-normal distribution. Can be a vector.}
\item{\code{[batch.facScale]}}{Scale (sdlog) parameter for the
batch effect factor log-normal distribution. Can be a vector.}
}
}
\item{\emph{Mean parameters}}{
\describe{
\item{\code{mean.shape}}{Shape parameter for the mean gamma
distribution.}
\item{\code{mean.rate}}{Rate parameter for the mean gamma
distribution.}
}
}
\item{\emph{Library size parameters}}{
\describe{
\item{\code{lib.loc}}{Location (meanlog) parameter for the
library size log-normal distribution.}
\item{\code{lib.scale}}{Scale (sdlog) parameter for the library
size log-normal distribution.}
}
}
\item{\emph{Expression outlier parameters}}{
\describe{
\item{\code{out.prob}}{Probability that a gene is an expression
outlier.}
\item{\code{out.facLoc}}{Location (meanlog) parameter for the
expression outlier factor log-normal distribution.}
\item{\code{out.facScale}}{Scale (sdlog) parameter for the
expression outlier factor log-normal distribution.}
}
}
\item{\emph{Group parameters}}{
\describe{
\item{\code{[nGroups]}}{The number of groups or paths to
simulate.}
\item{\code{[group.prob]}}{Probability that a cell comes from a
group.}
}
}
\item{\emph{Differential expression parameters}}{
\describe{
\item{\code{[de.prob]}}{Probability that a gene is differentially
expressed in a group. Can be a vector.}
\item{\code{[de.loProb]}}{Probability that a differentially
expressed gene is down-regulated. Can be a vector.}
\item{\code{[de.facLoc]}}{Location (meanlog) parameter for the
differential expression factor log-normal distribution. Can be a
vector.}
\item{\code{[de.facScale]}}{Scale (sdlog) parameter for the
differential expression factor log-normal distribution. Can be a
vector.}
}
}
\item{\emph{Biological Coefficient of Variation parameters}}{
\describe{
\item{\code{bcv.common}}{Underlying common dispersion across all
genes.}
\item{\code{bcv.df}}{Degrees of Freedom for the BCV inverse
chi-squared distribution.}
}
}
\item{\emph{Dropout parameters}}{
\describe{
\item{\code{dropout.type}}{The type of dropout to simulate.
"none" indicates no dropout, "experiment" is global dropout using
the same parameters for every cell, "batch" uses the same
parameters for every cell in each batch and "cell" uses a
different set of parameters for each cell.}
\item{\code{dropout.mid}}{Midpoint parameter for the dropout
logistic function.}
\item{\code{dropout.shape}}{Shape parameter for the dropout
logistic function.}
}
}
\item{\emph{Differentiation path parameters}}{
\describe{
\item{\code{[path.from]}}{Vector giving the originating point of
each path. This allows path structure such as a cell type which
differentiates into an intermediate cell type that then
differentiates into two mature cell types. A path structure of
this form would have a "from" parameter of c(0, 1, 1) (where 0 is
the origin). If no vector is given all paths will start at the
origin.}
\item{\code{[path.length]}}{Vector giving the number of steps to
simulate along each path. If a single value is given it will be
applied to all paths.}
\item{\code{[path.skew]}}{Vector giving the skew of each path.
Values closer to 1 will give more cells towards the starting
population, values closer to 0 will give more cells towards the
final population. If a single value is given it will be applied
to all paths.}
\item{\code{[path.nonlinearProb]}}{Probability that a gene
follows a non-linear path along the differentiation path. This
allows more complex gene patterns such as a gene being equally
expressed at the beginning an end of a path but lowly expressed
in the middle.}
\item{\code{[path.sigmaFac]}}{Sigma factor for non-linear gene
paths. A higher value will result in more extreme non-linear
variations along a path.}
}
}
}
The parameters not shown in brackets can be estimated from real data using
\code{\link{splatEstimate}}. For details of the Splatter simulation
see \code{\link{splatSimulate}}.
}