sparseDCEstimate.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sparseDC-estimate.R
\name{sparseDCEstimate}
\alias{sparseDCEstimate}
\alias{sparseDCEstimate.SingleCellExperiment}
\alias{sparseDCEstimate.matrix}
\title{Estimate SparseDC simulation parameters}
\usage{
sparseDCEstimate(counts, conditions, nclusters, norm = TRUE,
  params = newSparseDCParams())

\method{sparseDCEstimate}{SingleCellExperiment}(counts, conditions,
  nclusters, norm = TRUE, params = newSparseDCParams())

\method{sparseDCEstimate}{matrix}(counts, conditions, nclusters,
  norm = TRUE, params = newSparseDCParams())
}
\arguments{
\item{counts}{either a counts matrix or an SingleCellExperiment object
containing count data to estimate parameters from.}

\item{conditions}{numeric vector giving the condition each cell belongs to.}

\item{nclusters}{number of cluster present in the dataset.}

\item{norm}{logical, whether to libray size normalise counts before
estimation. Set this to FALSE if counts is already normalised.}

\item{params}{PhenoParams object to store estimated values in.}
}
\value{
SparseParams object containing the estimated parameters.
}
\description{
Estimate simulation parameters for the SparseDC simulation from a real
dataset.
}
\details{
The \code{nGenes} and \code{nCells} parameters are taken from the size of the
input data. The counts are preprocessed using
\code{\link[SparseDC]{pre_proc_data}} and then parameters are estimated using
\code{\link[SparseDC]{sparsedc_cluster}} using lambda values calculated using
\code{\link[SparseDC]{lambda1_calculator}} and
\code{\link[SparseDC]{lambda2_calculator}}.

See \code{\link{SparseDCParams}} for more details on the 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
}