The SplatParams class

Description

S4 class that holds parameters for the Splatter simulation.

Parameters

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.

Batch parameters

[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 parameters

mean.shape

Shape parameter for the mean gamma distribution.

mean.rate

Rate parameter for the mean gamma distribution.

Library size parameters

lib.loc

Location (meanlog) parameter for the library size log-normal distribution.

lib.scale

Scale (sdlog) parameter for the library size log-normal distribution.

Expression outlier parameters

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.

Group parameters

[nGroups]

The number of groups or paths to simulate.

[group.prob]

Probability that a cell comes from a group.

Differential expression parameters

[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.

Biological Coefficient of Variation parameters

bcv.common

Underlying common dispersion across all genes.

bcv.df

Degrees of Freedom for the BCV inverse chi-squared distribution.

Dropout parameters

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.

Differentiation path parameters

[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.