sparseDCEstimate {splatter} | R Documentation

## Estimate SparseDC simulation parameters

### Description

Estimate simulation parameters for the SparseDC simulation from a real dataset.

### Usage

    sparseDCEstimate(
      counts,
      conditions,
      nclusters,
      norm = TRUE,
      params = newSparseDCParams()
    )

    ## S3 method for class 'SingleCellExperiment'
    sparseDCEstimate(
      counts,
      conditions,
      nclusters,
      norm = TRUE,
      params = newSparseDCParams()
    )

    ## S3 method for class 'matrix'
    sparseDCEstimate(
      counts,
      conditions,
      nclusters,
      norm = TRUE,
      params = newSparseDCParams()
    )

### Arguments

`counts` | either a counts matrix or an SingleCellExperiment object containing count data to estimate parameters from.
`conditions` | numeric vector giving the condition each cell belongs to.
`nclusters` | number of cluster present in the dataset.
`norm` | logical, whether to library size normalise counts before estimation. Set this to FALSE if counts is already normalised.
`params` | PhenoParams object to store estimated values in.

### Details

The `nGenes` and `nCells` parameters are taken from the size of the input data. The counts are preprocessed using `pre_proc_data` and then parameters are estimated using `sparsedc_cluster` using lambda values calculated using `lambda1_calculator` and `lambda2_calculator`.

See `SparseDCParams` for more details on the parameters.

### Value

SparseParams object containing the estimated parameters.

### Examples

    if (requireNamespace("SparseDC", quietly = TRUE)) {
        # Load example data
        library(scater)
        set.seed(1)
        sce <- mockSCE(ncells = 20, ngenes = 100)

        conditions <- sample(1:2, ncol(sce), replace = TRUE)

        params <- sparseDCEstimate(sce, conditions, nclusters = 3)
        params
    }

[Package splatter version 1.11.4 Index]