SpectronauttoMSstatsFormat.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/converters_SpectronauttoMSstatsFormat.R
\name{SpectronauttoMSstatsFormat}
\alias{SpectronauttoMSstatsFormat}
\title{Import Spectronaut files}
\usage{
SpectronauttoMSstatsFormat(
  input,
  annotation = NULL,
  intensity = c("PeakArea", "NormalizedPeakArea", "MS1Quantity"),
  excludedFromQuantificationFilter = TRUE,
  filter_with_Qvalue = FALSE,
  qvalue_cutoff = 0.01,
  useUniquePeptide = TRUE,
  removeFewMeasurements = TRUE,
  removeProtein_with1Feature = FALSE,
  summaryforMultipleRows = max,
  calculateAnomalyScores = FALSE,
  anomalyModelFeatures = c(),
  anomalyModelFeatureTemporal = c(),
  removeMissingFeatures = 0.5,
  anomalyModelFeatureCount = 100,
  runOrder = NULL,
  n_trees = 100,
  max_depth = "auto",
  numberOfCores = 1,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)
}
\arguments{
\item{input}{name of Spectronaut output, which is long-format. ProteinName, PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge, IsotopeLabelType, Condition, BioReplicate, Run, Intensity, F.ExcludedFromQuantification are required. Rows with F.ExcludedFromQuantification=True will be removed.}

\item{annotation}{name of 'annotation.txt' data which includes Condition, BioReplicate, Run. If annotation is already complete in Spectronaut, use annotation=NULL (default). It will use the annotation information from input.}

\item{intensity}{'PeakArea'(default) uses not normalized MS2 peak area. 'NormalizedPeakArea' uses MS2 peak area normalized by Spectronaut.
'MS1Quantity' uses MS1 level quantification, which should be used if MS2 is unreliable.}

\item{excludedFromQuantificationFilter}{Remove rows with F.ExcludedFromQuantification=TRUE Default is TRUE.}

\item{filter_with_Qvalue}{FALSE(default) will not perform any filtering. TRUE will filter out the intensities that have greater than qvalue_cutoff in EG.Qvalue column. Those intensities will be replaced with zero and will be considered as censored missing values for imputation purpose.}

\item{qvalue_cutoff}{Cutoff for EG.Qvalue. default is 0.01.}

\item{useUniquePeptide}{TRUE (default) removes peptides that are assigned for more than one proteins.
We assume to use unique peptide for each protein.}

\item{removeFewMeasurements}{TRUE (default) will remove the features that have 1 or 2 measurements across runs.}

\item{removeProtein_with1Feature}{TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default.}

\item{summaryforMultipleRows}{max(default) or sum - when there are multiple measurements for certain feature and certain run, use highest or sum of multiple intensities.}

\item{calculateAnomalyScores}{Default is FALSE. If TRUE, will run anomaly detection model and calculate anomaly scores for each feature. Used downstream to weigh measurements in differential analysis.}

\item{anomalyModelFeatures}{character vector of quality metric column names to be used as features in the anomaly detection model. List must not be empty if calculateAnomalyScores=TRUE.}

\item{anomalyModelFeatureTemporal}{character vector of temporal direction corresponding to columns passed to anomalyModelFeatures. Values must be one of: \code{mean_decrease}, \code{mean_increase}, \code{dispersion_increase}, or NULL (to perform no temporal feature engineering). Default is empty vector. If calculateAnomalyScores=TRUE, vector must have as many values as anomalyModelFeatures (even if all NULL).}

\item{removeMissingFeatures}{Remove features with missing values in more than this fraction of runs. Default is 0.5. Only used if calculateAnomalyScores=TRUE.}

\item{anomalyModelFeatureCount}{Feature selection for anomaly model. Anomaly detection works on the precursor-level and can be much slower if all features used. We will by default filter to the top-100 highest intensity features. This can be adjusted as necessary. To turn feature-selection off, set this value to a high number (e.g. 10000). Only used if calculateAnomalyScores=TRUE.}

\item{runOrder}{Temporal order of MS runs. Should be a two column data.table with columns \code{Run} and \code{Order}, where \code{Run} matches the run name output by Spectronaut and \code{Order} is an integer. Used to engineer the temporal features defined in anomalyModelFeatureTemporal.}

\item{n_trees}{Number of trees to use in isolation forest when calculateAnomalyScores=TRUE. Default is 100.}

\item{max_depth}{Max tree depth to use in isolation forest when calculateAnomalyScores=TRUE. Default is "auto" which calculates depth as log2(N) where N is the number of runs. Otherwise must be an integer.}

\item{numberOfCores}{Number of cores for parallel processing anomaly detection model. When > 1, a logfile named 'MSstats_anomaly_model_progress.log' is created to track progress. Only works for Linux & Mac OS. Default is 1.}

\item{use_log_file}{logical. If TRUE, information about data processing
will be saved to a file.}

\item{append}{logical. If TRUE, information about data processing will be added
to an existing log file.}

\item{verbose}{logical. If TRUE, information about data processing wil be printed
to the console.}

\item{log_file_path}{character. Path to a file to which information about
data processing will be saved.
If not provided, such a file will be created automatically.
If \code{append = TRUE}, has to be a valid path to a file.}

\item{...}{additional parameters to \code{data.table::fread}.}
}
\value{
data.frame in the MSstats required format.
}
\description{
Import Spectronaut files
}
\examples{
spectronaut_raw = system.file("tinytest/raw_data/Spectronaut/spectronaut_input.csv",
                              package = "MSstatsConvert")
spectronaut_raw = data.table::fread(spectronaut_raw)
spectronaut_imported = SpectronauttoMSstatsFormat(spectronaut_raw, use_log_file = FALSE)
head(spectronaut_imported)

}
\author{
Meena Choi, Olga Vitek
}