SplatParams {splatter} | R Documentation

## 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`.

[Package splatter version 1.2.2 Index]