#' 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 = "_")
#'
NULL
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.