#' 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 not 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{[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, or mean parameter if a
#' normal distribution is used.}
#' \item{\code{lib.scale}}{Scale (sdlog) parameter for the library
#' size log-normal distribution, or sd parameter if a normal
#' distribution is used.}
#' \item{\code{lib.norm}}{Logical. Whether to use a normal
#' distribution for library sizes instead of a log-normal.}
#' }
#' }
#' \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.downProb]}}{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, "group" uses the same
#' parameters for every cell in each groups 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.nSteps]}}{Vector giving the number of steps to
#' simulate along each path. If a single value is given it will be
#' applied to all paths. This parameter was previously called
#' \code{path.length}.}
#' \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(nBatches = "numeric",
batchCells = "numeric",
batch.facLoc = "numeric",
batch.facScale = "numeric",
mean.shape = "numeric",
mean.rate = "numeric",
lib.loc = "numeric",
lib.scale = "numeric",
lib.norm = "logical",
out.prob = "numeric",
out.facLoc = "numeric",
out.facScale = "numeric",
nGroups = "numeric",
group.prob = "numeric",
de.prob = "numeric",
de.downProb = "numeric",
de.facLoc = "numeric",
de.facScale = "numeric",
bcv.common = "numeric",
bcv.df = "numeric",
dropout.type = "character",
dropout.mid = "numeric",
dropout.shape = "numeric",
path.from = "numeric",
path.nSteps = "numeric",
path.skew = "numeric",
path.nonlinearProb = "numeric",
path.sigmaFac = "numeric"),
prototype = prototype(nBatches = 1,
batchCells = 100,
batch.facLoc = 0.1,
batch.facScale = 0.1,
mean.rate = 0.3,
mean.shape = 0.6,
lib.loc = 11,
lib.scale = 0.2,
lib.norm = FALSE,
out.prob = 0.05,
out.facLoc = 4,
out.facScale = 0.5,
nGroups = 1,
group.prob = 1,
de.prob = 0.1,
de.downProb = 0.5,
de.facLoc = 0.1,
de.facScale = 0.4,
bcv.common = 0.1,
bcv.df = 60,
dropout.type = "none",
dropout.mid = 0,
dropout.shape = -1,
path.from = 0,
path.nSteps = 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 Lun2 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 columns: \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 columns: \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 vector 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[SingleCellExperiment]{SingleCellExperiment}} 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 about 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 =
SingleCellExperiment::SingleCellExperiment(),
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"))
#' The BASiCSParams class
#'
#' S4 class that holds parameters for the BASiCS simulation.
#'
#' @section Parameters:
#'
#' The BASiCS 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{Batch parameters}}{
#' \describe{
#' \item{\code{nBatches}}{Number of batches to simulate.}
#' \item{\code{batchCells}}{Number of cells in each batch.}
#' }
#' }
#' \item{\emph{Gene parameters}}{
#' \describe{
#' \item{\code{gene.params}}{A \code{data.frame} containing gene
#' parameters with two columns: \code{Mean} (mean expression for
#' each biological gene) and \code{Delta} (cell-to-cell
#' heterogeneity for each biological gene).}
#' }
#' }
#' \item{\emph{Spike-in parameters}}{
#' \describe{
#' \item{\code{nSpikes}}{The number of spike-ins to simulate.}
#' \item{\code{spike.means}}{Input molecules for each spike-in.}
#' }
#' }
#' \item{\emph{Cell parameters}}{
#' \describe{
#' \item{\code{cell.params}}{A \code{data.frame} containing gene
#' parameters with two columns: \code{Phi} (mRNA content factor for
#' each cell, scaled to sum to the number of cells in each batch)
#' and \code{S} (capture efficient for each cell).}
#' }
#' }
#' \item{\emph{Variability parameters}}{
#' \describe{
#' \item{\code{theta}}{Technical variability parameter for each
#' batch.}
#' }
#' }
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{BASiCSEstimate}}. For details of the BASiCS simulation see
#' \code{\link{BASiCSSimulate}}.
#'
#' @name BASiCSParams
#' @rdname BASiCSParams
#' @aliases BASiCSParams-class
#' @exportClass BASiCSParams
setClass("BASiCSParams",
contains = "Params",
slots = c(nBatches = "numeric",
batchCells = "numeric",
gene.params = "data.frame",
nSpikes = "numeric",
spike.means = "numeric",
cell.params = "data.frame",
theta = "numeric"),
prototype = prototype(nBatches = 1,
batchCells = 100,
gene.params =
data.frame(
Mean = c(8.36, 10.65, 4.88, 6.29, 21.72,
12.93, 30.19),
Delta = c(1.29, 0.88, 1.51, 1.49, 0.54,
0.40, 0.85)
),
nSpikes = 5,
spike.means = c(12.93, 30.19, 1010.72, 7.90,
31.59),
cell.params =
data.frame(
Phi = c(1.00, 1.06, 1.09, 1.05, 0.80),
S = c(0.38, 0.40, 0.38, 0.39, 0.34)
),
theta = 0.39)
)
#' The MFAParams class
#'
#' S4 class that holds parameters for the mfa simulation.
#'
#' @section Parameters:
#'
#' The mfa 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{[trans.prop]}}{Proportion of genes that show transient
#' expression. These genes are briefly up or down-regulated before returning
#' to their initial state}
#' \item{\code{[zero.neg]}}{Logical. Whether to set negative expression
#' values to zero. This will zero-inflate the data.}
#' \item{\code{[dropout.present]}}{Logical. Whether to simulate dropout.}
#' \item{\code{dropout.lambda}}{Lambda parameter for the exponential
#' dropout function.}
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{mfaEstimate}}. See \code{\link[mfa]{create_synthetic}} for more
#' details about the parameters. For details of the Splatter implementation of
#' the mfa simulation see \code{\link{mfaSimulate}}.
#'
#' @name MFAParams
#' @rdname MFAParams
#' @aliases MFAParams-class
#' @exportClass MFAParams
setClass("MFAParams",
contains = "Params",
slots = c(trans.prop = "numeric",
zero.neg = "logical",
dropout.present = "logical",
dropout.lambda = "numeric"),
prototype = prototype(trans.prop = 0, zero.neg = TRUE,
dropout.present = FALSE, dropout.lambda = 1))
#' The PhenoParams class
#'
#' S4 class that holds parameters for the PhenoPath simulation.
#'
#' @section Parameters:
#'
#' The PhenoPath 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{[n.de]}}{Number of genes to simulate from the differential
#' expression regime}
#' \item{\code{[n.pst]}}{Number of genes to simulate from the pseudotime
#' regime}
#' \item{\code{[n.pst.beta]}}{Number of genes to simulate from the
#' pseudotime + beta interactions regime}
#' \item{\code{[n.de.pst.beta]}}{Number of genes to simulate from the
#' differential expression + pseudotime + interactions regime}
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{phenoEstimate}}. For details of the PhenoPath simulation
#' see \code{\link{phenoSimulate}}.
#'
#' @name PhenoParams
#' @rdname PhenoParams
#' @aliases PhenoParams-class
#' @exportClass PhenoParams
setClass("PhenoParams",
contains = "Params",
slots = c(n.de = "numeric",
n.pst = "numeric",
n.pst.beta = "numeric",
n.de.pst.beta = "numeric"),
prototype = prototype(n.de = 2500, n.pst = 2500, n.pst.beta = 2500,
n.de.pst.beta = 2500))
#' The ZINBParams class
#'
#' S4 class that holds parameters for the ZINB-WaVE simulation.
#'
#' @section Parameters:
#'
#' The ZINB-WaVE 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{model}}{Object describing a ZINB model.}
#' }
#'
#' The majority of the parameters for this simulation are stored in a
#' \code{\link[zinbwave]{ZinbModel}} object. Please refer to the documentation
#' for this class and its constructor(\code{\link[zinbwave]{zinbModel}}) for
#' details about all the parameters.
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{zinbEstimate}}. For details of the ZINB-WaVE simulation
#' see \code{\link{zinbSimulate}}.
#'
#' @name ZINBParams
#' @rdname ZINBParams
#' @aliases ZINBParams-class
#' @exportClass ZINBParams
setClass("ZINBParams",
contains = "Params",
slots = c(model = "ANY"),
prototype = prototype(nGenes = 100, nCells = 50))
#' The SparseDCParams class
#'
#' S4 class that holds parameters for the SparseDC simulation.
#'
#' @section Parameters:
#'
#' The SparseDC simulation uses the following parameters:
#'
#' \describe{
#' \item{\code{nGenes}}{The number of genes to simulate in each condition.}
#' \item{\code{nCells}}{The number of cells to simulate.}
#' \item{\code{[seed]}}{Seed to use for generating random numbers.}
#' \item{\code{markers.n}}{Number of marker genes to simulate for each
#' cluster.}
#' \item{\code{markers.shared}}{Number of marker genes for each cluster
#' shared between conditions. Must be less than or equal to
#' \code{markers.n}}.
#' \item{\code{[markers.same]}}{Logical. Whether each cluster should have
#' the same set of marker genes.}
#' \item{\code{clusts.c1}}{Numeric vector of clusters present in
#' condition 1. The number of times a cluster is repeated controls the
#' proportion of cells from that cluster.}
#' \item{\code{clusts.c2}}{Numeric vector of clusters present in
#' condition 2. The number of times a cluster is repeated controls the
#' proportion of cells from that cluster.}
#' \item{\code{[mean.lower]}}{Lower bound for cluster gene means.}
#' \item{\code{[mean.upper]}}{Upper bound for cluster gene means.}
#' }
#'
#' The parameters not shown in brackets can be estimated from real data using
#' \code{\link{sparseDCEstimate}}. For details of the SparseDC simulation
#' see \code{\link{sparseDCSimulate}}.
#'
#' @name SparseDCParams
#' @rdname SparseDCParams
#' @aliases SparseDCParams-class
#' @exportClass SparseDCParams
setClass("SparseDCParams",
contains = "Params",
slots = c(markers.n = "numeric",
markers.shared = "numeric",
markers.same = "logical",
clusts.c1 = "numeric",
clusts.c2 = "numeric",
mean.lower = "numeric",
mean.upper = "numeric"),
prototype = prototype(markers.n = 0,
markers.shared = 0,
markers.same = FALSE,
clusts.c1 = 1,
clusts.c2 = 1,
mean.lower = 1,
mean.upper = 2))