| sparseDCEstimate {splatter} | R Documentation |
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 libray 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