SplatParams {splatter} | R Documentation |
S4 class that holds parameters for the Splatter simulation.
The Splatter simulation requires the following parameters:
nGenes
The number of genes to simulate.
nCells
The number of cells to simulate.
[seed]
Seed to use for generating random numbers.
[nBatches]
The number of batches to simulate.
[batchCells]
Vector giving the number of cells in each batch.
[batch.facLoc]
Location (meanlog) parameter for the batch effect factor log-normal distribution. Can be a vector.
[batch.facScale]
Scale (sdlog) parameter for the batch effect factor log-normal distribution. Can be a vector.
mean.shape
Shape parameter for the mean gamma distribution.
mean.rate
Rate parameter for the mean gamma distribution.
lib.loc
Location (meanlog) parameter for the library size log-normal distribution.
lib.scale
Scale (sdlog) parameter for the library size log-normal distribution.
out.prob
Probability that a gene is an expression outlier.
out.facLoc
Location (meanlog) parameter for the expression outlier factor log-normal distribution.
out.facScale
Scale (sdlog) parameter for the expression outlier factor log-normal distribution.
[nGroups]
The number of groups or paths to simulate.
[group.prob]
Probability that a cell comes from a group.
[de.prob]
Probability that a gene is differentially expressed in a group. Can be a vector.
[de.loProb]
Probability that a differentially expressed gene is down-regulated. Can be a vector.
[de.facLoc]
Location (meanlog) parameter for the differential expression factor log-normal distribution. Can be a vector.
[de.facScale]
Scale (sdlog) parameter for the differential expression factor log-normal distribution. Can be a vector.
bcv.common
Underlying common dispersion across all genes.
bcv.df
Degrees of Freedom for the BCV inverse chi-squared distribution.
dropout.present
Logical. Whether to simulate dropout.
dropout.mid
Midpoint parameter for the dropout logistic function.
dropout.shape
Shape parameter for the dropout logistic function.
[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.
[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.
[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.
[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.
[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
splatEstimate
. For details of the Splatter simulation
see splatSimulate
.