runDescend.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/descend.R
\name{runDescend}
\alias{runDescend}
\title{Apply DESCEND to all the genes in the count matrix for one cell population}
\usage{
runDescend(count.matrix, ercc.matrix = NULL, scaling.consts = NULL,
  Z = NULL, Z0 = NULL, n.cores = 1, cl = NULL, type = "FORK",
  do.LRT.test = F, family = c("Poisson", "Negative Binomial"),
  NB.size = 100, show.message = T, verbose = T,
  ercc.trueMol = NULL, control = list())
}
\arguments{
\item{count.matrix}{the observed UMI count matrix. Each row is a gene and each column is a cell. The column sums are used as the input for \code{scaling.consts} when both \code{ercc.matrix} and \code{scaling.consts} are NULL.}

\item{ercc.matrix}{the ERCC spike-ins are used for computing the cell-specific efficiency constants as \code{scaling.consts} when \code{scaling.consts} is NULL. Each row is a spike-in genes and each column is a cell. The number and order of the columns should be the same as the number and order of the columns of \code{count.matrix}.}

\item{scaling.consts}{a vector of cell specific scaling constants, either the cell efficiency or the library size}

\item{Z}{covariates for active intensity. Default is NULL.}

\item{Z0}{covariates for active fraction. Used only when zeroInflate is True. Default is NULL.}

\item{n.cores}{the number of cores used for parallel computing. Default is 1. Used only when parallel computing is done in a single machine. For using multi-machine cores, need to assign \code{cl} explicitly. If \code{verbose} is TRUE, then a separated file is created to store the progress of each slave cores.}

\item{cl}{an object of class "cluster". See more details in \code{\link[parallel]{makeCluster}}}

\item{type}{Default is "FORK" to save memory. Change it to "PSOCK" if you are using Windows and cl is NULL. More details see \code{\link[parallel]{makeCluster}}}

\item{do.LRT.test}{whether do LRT test on the coefficients and active fraction or not. Default is True}

\item{family}{family of the noise distribution, support either "Poisson" or "Negative Binomial" with known tuning parameter}

\item{NB.size}{over-dispersion parameter when the family is Negative Binomial: mu = mu + mu^2/size}

\item{show.message}{whether show messages for the computing progresses. Default is TRUE}

\item{verbose}{verbose the estimation and testing procedures or not. Default is True.}

\item{ercc.trueMol}{the true input number of molecules of the ercc spike-ins when \code{ercc.matrix} is not NULL.}

\item{control}{settings see {\code{\link{DESCEND.control}}}}
}
\value{
a list of DESCEND objects. The length of the list is the same as the number of genes. NA if the gene is too sparse or DESCEND fails to find a solution.
}
\description{
Apply DESCEND to deconvolve the true expression level distribution for every geneand calculate relavant distribution measurements. Parallel computing is allowed. For deconvolution of two or more cell populations, see \code{\link{descendMultiPop}}. For model details, see \code{\link{deconvG}}.
}
\examples{
\dontrun{
## For a Windows machine add the argument: type = "PSOCK"
data(zeisel)
result <- runDescend(zeisel$count.matrix.small, 
                     scaling.consts = zeisel$library.size, n.cores = 3)
hvg <- findHVG(result)
hvg$HVG.genes



result1 <- runDescend(zeisel$count.matrix.small, 
                      zeisel$ercc.matrix, ercc.trueMol = zeisel$trueMol,
                      Z0 = log(zeisel$cell.size),
                      n.cores = 3)

ests <- getEstimates(result1)
ests$CV
}

}