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  • % Generated by roxygen2: do not edit by hand
    % Please edit documentation in R/splat-estimate.R
    \name{splatEstDropout}
    \alias{splatEstDropout}
    \title{Estimate Splat dropout parameters}
    \usage{
    splatEstDropout(norm.counts, params)
    }
    \arguments{
    \item{norm.counts}{library size normalised counts matrix.}
    
    \item{params}{SplatParams object to store estimated values in.}
    }
    \value{
    SplatParams object with estimated values.
    }
    \description{
    Estimate the midpoint and shape parameters for the logistic function used
    when simulating dropout. Also estimates whether dropout is likely to be
    present in the dataset.
    }
    \details{
    Logistic function parameters are estimated by fitting a logistic function
    to the relationship between log2 mean gene expression and the proportion of
    zeros in each gene. See \code{\link[stats]{nls}} for details of fitting. The
    presence of dropout is determined by comparing the observed number of zeros
    in each gene to the expected number of zeros from a negative binomial
    distribution with the gene mean and a dispersion of 0.1. If the maximum
    difference between the observed number of zeros and the expected number is
    greater than 10 percent of the number of cells
    (\code{max(obs.zeros - exp.zeros) > 0.1 * ncol(norm.counts)}) then dropout is
    considered to be present in the dataset. This is a somewhat crude measure
    but should give a reasonable indication. A more accurate approach is to look
    at a plot of log2 mean expression vs the difference between observed and
    expected number of zeros across all genes.
    }