Commit c19ee385 authored by daniel.eilertz's avatar daniel.eilertz
Browse files

Added automRm.R with roxygen tags as documentation root (links dont wokr yet...)

parent 00203324
#' automRm: Taking LC-QQQ peak picking to the next level
#'
#' automRm processes LC-QQQ raw data in the open .mzML format to obtain a user-friendly output of signal intensities for every analyte and every sample in .xlsx format. \cr \cr
#' In addition to the .mzML files a matching list of target metabolites must be available. automRm then uses 2 random forest models to 1st. decide which chromatographic peak is the most likely to represent an analyte and 2nd to decide if the data is of sufficient quality to be given to a person with little metabolomics experience.
#' Both random forest models can easily be trained to meet one's LC-MS methods and quality standards.
#'
#'
#' @section automRm functions:
#' @param train_model Train first and/or second model for automated peak picking.
#' @param initialize_prm Load and assign global default varariables. Default variables can be overwritten by the parameters argument when calling process_batch or using edited update_prm.tsv files in working folder).
#' @param process_batch Automated analysis of provided .mzML files.
#'
#' @docType package
#' @name automRm
NULL
#> NULL
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/automRm.R
\docType{package}
\name{automRm}
\alias{automRm}
\title{automRm: Taking LC-QQQ peak picking to the next level}
\arguments{
\item{train_model}{Train first and/or second random forest model for automated peak picking.}
\item{initialize_prm}{Load and assign global default varariables. Default variables can be overwritten by the parameters argument when calling process_batch or using edited update_prm.tsv files in working folder).}
\item{process_batch}{Automated analysis of provided .mzML files.}
}
\description{
automRm processes LC-QQQ raw data in the open .mzML format to obtain a user-friendly output of signal intensities for every analyte and every sample in .xlsx format. \cr \cr
In addition to the .mzML files a matching list of target metabolites must be available. automRm then uses 2 random forest models to 1st. decide which chromatographic peak is the most likely to represent an analyte and 2nd to decide if the data is of sufficient quality to be given to a person with little metabolomics experience.
Both random forest models can easily be trained to meet one's LC-MS methods and quality standards.
\code{\link{automRm::train_model()}}.Tralalla
}
\section{automRm functions}{
}
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