MSPrep {MSPrep} | R Documentation

## Package for summarizing, filtering, imputing, and normalizing metabolomics data.

### Description

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

### 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.

### Author(s)

Max McGrath

Matt Mulvahill

Grant Hughes

Sean Jacobson

Harrison Pielke-Lombardo

Katerina Kechris

### 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 = "_")

[Package MSPrep version 1.4.0 Index]