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 libray 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

    # Load example data
    library(scater)
    data("sc_example_counts")

    set.seed(1)
    conditions <- sample(1:2, ncol(sc_example_counts), replace = TRUE)

    params <- sparseDCEstimate(sc_example_counts[1:500, ], conditions,
                               nclusters = 3)
    params

[Package splatter version 1.4.3 Index]