#' The Params virtual class
#'
#' Virtual S4 class that all other Params classes inherit from.
#'
#' @section Parameters:
#'
#' The Params class defines 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.}
#' }
#'
#' The parameters shown in brackets can be estimated from real data.
#'
#' @name Params
#' @rdname Params
#' @aliases Params-class
setClass("Params",
contains = "VIRTUAL",
slots = c(nGenes = "numeric",
nCells = "numeric",
seed = "numeric"),
prototype = prototype(nGenes = 10000, nCells = 100,
seed = sample(1:1e6, 1)))
#' The SimpleParams class
#'
#' S4 class that holds parameters for the simple simulation.
#'
#' @section Parameters:
#'
#' The simple simulation uses 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{\code{mean.shape}}{The shape parameter for the mean gamma
#' distribution.}
#' \item{\code{mean.rate}}{The rate parameter for the mean gamma
#' distribution.}
#' \item{\code{[count.disp]}}{The dispersion parameter for the counts
#' negative binomial distribution.}
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{simpleEstimate}}. For details of the simple simulation
#' see \code{\link{simpleSimulate}}.
#'
#' @name SimpleParams
#' @rdname SimpleParams
#' @aliases SimpleParams-class
#' @exportClass SimpleParams
setClass("SimpleParams",
contains = "Params",
slots = c(mean.shape = "numeric",
mean.rate = "numeric",
count.disp = "numeric"),
prototype = prototype(mean.shape = 0.4, mean.rate = 0.3,
count.disp = 0.1))
#' The SplatParams class
#'
#' 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{[nGroups]}}{The number of groups or paths to simulate.}
#' \item{\code{[groupCells]}}{Vector giving the number of cells in each
#' simulation group/path.}
#' \item{\code{[seed]}}{Seed to use for generating random numbers.}
#' \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{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.present}}{Logical. Whether to simulate
#' dropout.}
#' \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}}.
#'
#' @name SplatParams
#' @rdname SplatParams
#' @aliases SplatParams-class
#' @exportClass SplatParams
setClass("SplatParams",
contains = "Params",
slots = c(nGroups = "numeric",
groupCells = "numeric",
mean.shape = "numeric",
mean.rate = "numeric",
lib.loc = "numeric",
lib.scale = "numeric",
out.prob = "numeric",
out.facLoc = "numeric",
out.facScale = "numeric",
de.prob = "numeric",
de.downProb = "numeric",
de.facLoc = "numeric",
de.facScale = "numeric",
bcv.common = "numeric",
bcv.df = "numeric",
dropout.present = "logical",
dropout.mid = "numeric",
dropout.shape = "numeric",
path.from = "numeric",
path.length = "numeric",
path.skew = "numeric",
path.nonlinearProb = "numeric",
path.sigmaFac = "numeric"),
prototype = prototype(nGroups = 1,
groupCells = 100,
mean.rate = 0.3,
mean.shape = 0.6,
lib.loc = 11,
lib.scale = 0.2,
out.prob = 0.05,
out.facLoc = 4,
out.facScale = 0.5,
de.prob = 0.1,
de.downProb = 0.5,
de.facLoc = 4,
de.facScale = 1,
bcv.common = 0.1,
bcv.df = 60,
dropout.present = FALSE,
dropout.mid = 0,
dropout.shape = -1,
path.from = 0,
path.length = 100,
path.skew = 0.5,
path.nonlinearProb = 0.1,
path.sigmaFac = 0.8))
#' The LunParams class
#'
#' S4 class that holds parameters for the Lun simulation.
#'
#' @section Parameters:
#'
#' The Lun simulation uses the following parameters:
#'
#' \describe{
#' \item{\code{nGenes}}{The number of genes to simulate.}
#' \item{\code{nCells}}{The number of cells to simulate.}
#' \item{\code{[nGroups]}}{The number of groups to simulate.}
#' \item{\code{[groupCells]}}{Vector giving the number of cells in each
#' simulation group/path.}
#' \item{\code{[seed]}}{Seed to use for generating random numbers.}
#' \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{Counts parameters}}{
#' \describe{
#' \item{\code{[count.disp]}}{The dispersion parameter for the
#' counts negative binomial distribution.}
#' }
#' }
#' \item{\emph{Differential expression parameters}}{
#' \describe{
#' \item{\code{[de.nGenes]}}{The number of genes that are
#' differentially expressed in each group}
#' \item{\code{[de.upProp]}}{The proportion of differentially
#' expressed genes that are up-regulated in each group}
#' \item{\code{[de.upFC]}}{The fold change for up-regulated genes}
#' \item{\code{[de.downFC]}}{The fold change for down-regulated
#' genes}
#' }
#' }
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{lunEstimate}}. For details of the Lun simulation see
#' \code{\link{lunSimulate}}.
#'
#' @name LunParams
#' @rdname LunParams
#' @aliases LunParams-class
#' @exportClass LunParams
setClass("LunParams",
contains = "SimpleParams",
slots = c(nGroups = "numeric",
groupCells = "numeric",
de.nGenes = "numeric",
de.upProp = "numeric",
de.upFC = "numeric",
de.downFC = "numeric"),
prototype = prototype(nGroups = 1, groupCells = 100, mean.shape = 2,
mean.rate = 2, de.nGenes = 1000, de.upProp = 0.5,
de.upFC = 5, de.downFC = 0))
#' The Lun2Params class
#'
#' S4 class that holds parameters for the Lun simulation.
#'
#' @section Parameters:
#'
#' The Lun2 simulation uses 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{Gene parameters}}{
#' \describe{
#' \item{\code{gene.params}}{A \code{data.frame} containing gene
#' parameters with two coloumns: \code{Mean} (mean expression for
#' each gene) and \code{Disp} (dispersion for each gene).}
#' \item{\code{zi.params}}{A \code{data.frame} containing
#' zero-inflated gene parameters with three coloumns: \code{Mean}
#' (mean expression for each gene), \code{Disp} (dispersion for
#' each, gene), and \code{Prop} (zero proportion for each gene).}
#' }
#' }
#' \item{\code{[nPlates]}}{The number of plates to simulate.}
#' \item{\emph{Plate parameters}}{
#' \describe{
#' \item{\code{plate.ingroup}}{Character vecotor giving the plates
#' considered to be part of the "ingroup".}
#' \item{\code{plate.mod}}{Plate effect modifier factor. The plate
#' effect variance is divided by this value.}
#' \item{\code{plate.var}}{Plate effect variance.}
#' }
#' }
#' \item{\emph{Cell parameters}}{
#' \describe{
#' \item{\code{cell.plates}}{Factor giving the plate that each cell
#' comes from.}
#' \item{\code{cell.libSizes}}{Library size for each cell.}
#' \item{\code{cell.libMod}}{Modifier factor for library sizes.
#' The library sizes are multiplied by this value.}
#' }
#' }
#' \item{\emph{Differential expression parameters}}{
#' \describe{
#' \item{\code{de.nGenes}}{Number of differentially expressed
#' genes.}
#' \item{\code{de.fc}}{Fold change for differentially expressed
#' genes.}
#' }
#' }
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{lun2Estimate}}. For details of the Lun2 simulation see
#' \code{\link{lun2Simulate}}.
#'
#' @name Lun2Params
#' @rdname Lun2Params
#' @aliases Lun2Params-class
#' @exportClass Lun2Params
setClass("Lun2Params",
contains = "Params",
slots = c(nPlates = "numeric",
plate.ingroup = "character",
plate.mod = "numeric",
plate.var = "numeric",
gene.params = "data.frame",
zi.params = "data.frame",
cell.plates = "numeric",
cell.libSizes = "numeric",
cell.libMod = "numeric",
de.nGenes = "numeric",
de.fc = "numeric"),
prototype = prototype(nPlates = 1,
cell.plates = factor(rep(1, 100)),
plate.ingroup = "1",
plate.mod = 1,
plate.var = 14,
gene.params = data.frame(Mean = rep(3.2, 10000),
Disp = rep(0.03, 10000)
),
zi.params = data.frame(Mean = rep(1.6, 10000),
Disp = rep(0.1, 10000),
Prop = rep(2.3e-6, 10000)
),
cell.libSizes = rep(70000, 100),
cell.libMod = 1,
de.nGenes = 0,
de.fc = 3))
#' The SCDDParams class
#'
#' S4 class that holds parameters for the scDD simulation.
#'
#' @section Parameters:
#'
#' The SCDD simulation uses the following parameters:
#'
#' \describe{
#' \item{\code{[nGenes]}}{The number of genes to simulate (not used).}
#' \item{\code{nCells}}{The number of cells to simulate in each condition.}
#' \item{\code{[seed]}}{Seed to use for generating random numbers.}
#' \item{\code{SCdat}}{
#' \code{\link[SummarizedExperiment]{SummarizedExperiment}} containing real
#' data.}
#' \item{\code{[nDE]}}{Number of DE genes to simulate.}
#' \item{\code{[nDP]}}{Number of DP genes to simulate.}
#' \item{\code{[nDM]}}{Number of DM genes to simulate.}
#' \item{\code{[nDB]}}{Number of DB genes to simulate.}
#' \item{\code{[nEE]}}{Number of EE genes to simulate.}
#' \item{\code{[nEP]}}{Number of EP genes to simulate.}
#' \item{\code{[sd.range]}}{Interval for fold change standard deviations.}
#' \item{\code{[modeFC]}}{Values for DP, DM and DB mode fold changes.}
#' \item{\code{[varInflation]}}{Variance inflation factors for each
#' condition. If all equal to 1 will be set to \code{NULL} (default)}
#' \item{\code{[condition]}}{String giving the column that represents
#' biological group of interest}
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{scDDEstimate}}. See \code{\link[scDD]{simulateSet}} for more
#' details of the parameters. For details of the Splatter implementation of the
#' scDD simulation see \code{\link{scDDSimulate}}.
#'
#' @name SCDDParams
#' @rdname SCDDParams
#' @aliases SCDDParams-class
#' @exportClass SCDDParams
setClass("SCDDParams",
contains = "Params",
slots = c(SCdat = "SummarizedExperiment",
nDE = "numeric",
nDP = "numeric",
nDM = "numeric",
nDB = "numeric",
nEE = "numeric",
nEP = "numeric",
sd.range = "numeric",
modeFC = "numeric",
varInflation = "numeric",
condition = "character"),
prototype = prototype(SCdat =
SummarizedExperiment::SummarizedExperiment(),
nCells = 100,
nDE = 250,
nDP = 250,
nDM = 250,
nDB = 250,
nEE = 5000,
nEP = 4000,
sd.range = c(1, 3),
modeFC = c(2, 3, 4),
varInflation = c(1, 1),
condition = "condition"))