% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pqn_norm_method_class.R
\name{pqn_norm}
\alias{pqn_norm}
\title{Probabilistic Quotient Normalisation (PQN)}
\usage{
pqn_norm(
  qc_label = "QC",
  factor_name,
  qc_frac = 0,
  sample_frac = 0,
  ref_method = "mean",
  ref_mean = NULL,
  ...
)
}
\arguments{
\item{qc_label}{(character) The label used to identify QC samples. The default is \code{"QC"}.}

\item{factor_name}{(character) The name of a sample-meta column to use.}

\item{qc_frac}{(numeric) A value between 0 and 1 to indicate the minimum proportion of QC samples a feature must be present in for it to be included when computing the reference. Default qc_frac = 0. . The default is \code{0}.\cr}

\item{sample_frac}{(numeric) A value between 0 and 1 to indicate the minimum proportion of samples a feature must be present in for it to be considered when computing the normalisation coefficients. . The default is \code{0}.\cr}

\item{ref_method}{(character) Reference computation method. Allowed values are limited to the following: \itemize{ \item{\code{"mean"}: The reference is computed as the mean of the samples matching the qc_label input.}\item{\code{"median"}: The reference is computed as the median of the samples matching the qc_label_input.}} The default is \code{"mean"}.}

\item{ref_mean}{(numeric, NULL) A single sample to use as the reference for normalisation. If set to NULL then the reference will be computed based on the other input parameters (ref_mean, qc_label etc). . The default is \code{NULL}.}

\item{...}{Additional slots and values passed to \code{struct_class}.}
}
\value{
A  \code{pqn_norm} object with the following \code{output} slots:
\tabular{ll}{
\code{normalised} \tab          (DatasetExperiment) A DatasetExperiment object containing the normalised data. \cr
\code{coeff} \tab          (data.frame) The normalisation coefficients calculated by PQN. \cr
}
}
\description{
PQN is used to normalise for differences in concentration between samples. It makes use of Quality Control (QC) samples as a reference. PQN scales by the median change relative to the reference in order to be more robust against changes caused by response to perturbation.
}
\details{
This object makes use of functionality from the following packages:\itemize{  \item{\code{pmp}}}
}
\section{Inheritance}{

A \code{pqn_norm} object inherits the following \code{struct} classes: \cr\cr
\verb{[pqn_norm]} >> \verb{[model]} >> \verb{[struct_class]}
}

\examples{
M = pqn_norm(
      qc_label = "QC",
      factor_name = "V1",
      qc_frac = 0,
      sample_frac = 0,
      ref_mean = NULL,
      ref_method = "mean")

D = iris_DatasetExperiment()
M = pqn_norm(factor_name='Species',qc_label='all')
M = model_apply(M,D)

}
\references{
Jankevics A, Lloyd GR, Weber RJM (????). \emph{pmp: Peak Matrix Processing
and signal batch correction for metabolomics datasets}. R package
version 1.15.1.
}