sparseDCEstimate: Estimate SparseDC simulation parameters
In Granoia/splatter-mod: Simple Simulation of Single-cell RNA Sequencing Data

Description Usage Arguments Details Value Examples

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

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())

`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.

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.

SparseParams object containing the estimated parameters.

# 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