R/MSPrep_package.R

In KechrisLab/MSPrep: Package for Summarizing, Filtering, Imputing, and Normalizing Metabolomics Data

#' Package for summarizing, filtering, imputing, and normalizing metabolomics 
#' data.
#' 
#' Package performs summarization of replicates, filtering by frequency, several
#' different options for imputing missing data, and a variety of options for 
#' transforming, batch correcting, and normalizing data
#'
#' @author Max McGrath
#' @author Matt Mulvahill
#' @author Grant Hughes
#' @author Sean Jacobson
#' @author Harrison Pielke-Lombardo
#' @author Katerina Kechris
#' @docType package
#' @name MSPrep 
#' @details
#' Package for pre-analytic processing of mass spectrometry quantification data.
#' Four functions are provided and are intended to be used in sequence (as a 
#' pipeline) to produce processed and normalized data. These are  
#' msSummarize(), msFilter(), msImpute(), and msNormalize(). 
#' The function msPrepare() is also provided as a wrapper function combining 
#' the four previously mentioned functions.
#' 
#' @references
#' Bolstad, B.M.et al.(2003) A comparison of normalization methods for high
#' density oligonucleotide array data based on variance and bias.
#' Bioinformatics, 19, 185-193
#' 
#' DeLivera, A.M.et al.(2012) Normalizing and Integrating Metabolomic Data.
#' Anal. Chem, 84, 10768-10776.
#' 
#' Gagnon-Bartsh, J.A.et al.(2012) Using control genes to correct for unwanted
#' variation in microarray data. Biostatistics, 13, 539-552.
#' 
#' Hughes G, Cruickshank-Quinn C, Reisdorph R, Lutz S, Petrache I, Reisdorph N, 
#' Bowler R, Kechris K. MSPrep--Summarization, normalization and diagnostics for
#'  processing of mass spectrometry-based metabolomic data. Bioinformatics. 
#'  2014;30(1):133-4. Epub 2013/11/01. doi: 10.1093/bioinformatics/btt589. 
#'  PubMed PMID: 24174567; PMCID: PMC3866554.
#' 
#' Johnson, W.E.et al.(2007) Adjusting batch effects in microarray expression
#' data using Empirical Bayes methods. Biostatistics, 8, 118-127.
#' 
#' Leek, J.T.et al.(2007) Capturing Heterogeneity in Gene Expression Studies by
#' Surrogate Variable Analysis. PLoS Genetics, 3(9), e161.
#' 
#' Oba, S.et al.(2003) A Bayesian missing value estimation for gene expression
#' profile data. Bioinformatics, 19, 2088-2096
#' 
#' Redestig, H.et al.(2009) Compensation for Systematic Cross-Contribution
#' Improves Normalization of Mass Spectrometry Based Metabolomics Data. Anal.
#' Chem., 81, 7974-7980.
#' 
#' Stacklies, W.et al.(2007) pcaMethods: A bioconductor package providing PCA
#' methods for incomplete data. Bioinformatics, 23, 1164-1167.
#' 
#' Wang, W.et al.(2003) Quantification of Proteins and Metabolites by Mass
#' Spectrometry without Isotopic Labeling or Spiked Standards. Anal. Chem., 75,
#' 4818-4826.
#' 
#' @examples
#' # Load example data
#' data(msquant)
#' 
#' # Call function to tidy, summarize, filter, impute, and normalize data
#' preparedDF <- msPrepare(msquant,
#'                         minPropPresent = 1/3,
#'                         missingValue = 1,
#'                         filterPercent = 0.8,
#'                         imputeMethod = "knn",
#'                         normalizeMethod = "quantile + ComBat",
#'                         transform = "log10",
#'                         covariatesOfInterest = c("spike"),
#'                         compVars = c("mz", "rt"),
#'                         sampleVars = c("spike", "batch", "replicate", 
#'                                        "subject_id"),
#'                         colExtraText = "Neutral_Operator_Dif_Pos_",
#'                         separator = "_")
#' 
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