msNormalize {MSPrep} | R Documentation

## Function for performing normalization and batch corrections on imputed data.

### Description

Perform normalization and batch corrections on specified imputed dataset. Routines included are quantile, RUV (remove unwanted variation), SVA (surrogate variable analysis), median, CRMN (cross-contribution compensating multiple standard normalization), and ComBat to remove batch effects in raw, quantile, and median normalized data. Generates data driven controls if none exist.

### Usage

    msNormalize(
      data,
      normalizeMethod = c("median", "ComBat", "quantile", "quantile + ComBat",
        "median + ComBat", "CRMN", "RUV", "SVA"),
      nControl = 10,
      controls = NULL,
      nComp = 2,
      kRUV = 3,
      batch = "batch",
      covariatesOfInterest = NULL,
      transform = c("log10", "log2", "ln", "none"),
      compVars = c("mz", "rt"),
      sampleVars = c("subject_id"),
      colExtraText = NULL,
      separator = NULL,
      returnToSE = FALSE,
      returnToDF = FALSE
    )

### Arguments

`data` | Data set as either a data frame or 'SummarizedExperiement'.
`normalizeMethod` | Name of normalization method. "ComBat" (only ComBat batch correction), "quantile" (only quantile normalization), "quantile + ComBat" (quantile with ComBat batch correction), "median" (only median normalization), "median + ComBat" (median with ComBat batch correction), "CRMN" (cross-contribution compensating multiple standard normalization), "RUV" (remove unwanted variation), "SVA" (surrogate variable analysis)
`nControl` | Number of controls to estimate/utilize (for CRMN and RUV).
`controls` | Vector of control identifiers. Leave blank for data driven controls. Vector of column numbers from metafin dataset of that control (for CRMN and RUV).
`nComp` | Number of factors to use in CRMN algorithm.
`kRUV` | Number of factors to use in RUV algorithm.
`batch` | Name of the sample variable identifying batch.
`covariatesOfInterest` | Sample variables used as covariates in normalization algorithms (required for ComBat, CRMN, and SVA).
`transform` | Select transformation to apply to data prior to normalization. Options are "log10", "log2", "ln" and "none".
`compVars` | Vector of the columns which identify compounds. If a 'SummarizedExperiment' is used for 'data', row variables will be used.
`sampleVars` | Vector of the ordered sample variables found in each sample column.
`colExtraText` | Any extra text to ignore at the beginning of the sample columns names. Unused for 'SummarizedExperiments'.
`separator` | Character or text separating each sample variable in sample columns. Unused for 'SummarizedExperiment'.
`returnToSE` | Logical value indicating whether to return as 'SummarizedExperiment'
`returnToDF` | Logical value indicating whether to return as data frame.

### Value

A data frame or 'SummarizedExperiment' with transformed and normalized data. Default return type is set to match the data input but may be altered with the 'returnToSE' or 'returnToDF' arguments.

### 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.

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

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, tidy, summarize, filter, and impute example dataset
    data(msquant)

    summarizedDF <- msSummarize(msquant,
                                compVars = c("mz", "rt"),
                                sampleVars = c("spike", "batch", "replicate",
                                "subject_id"),
                                cvMax = 0.50,
                                minPropPresent = 1/3,
                                colExtraText = "Neutral_Operator_Dif_Pos_",
                                separator = "_",
                                missingValue = 1)

    filteredDF <- msFilter(summarizedDF,
                           filterPercent = 0.8,
                           compVars = c("mz", "rt"),
                           sampleVars = c("spike", "batch", "subject_id"),
                           separator = "_")

    hmImputedDF <- msImpute(filteredDF, imputeMethod = "halfmin",
                            compVars = c("mz", "rt"),
                            sampleVars = c("spike", "batch", "subject_id"),
                            separator = "_",
                            missingValue = 0)

    # Normalize data set
    medianNormalizedDF <- msNormalize(hmImputedDF, normalizeMethod = "median",
                                      compVars = c("mz", "rt"),
                                      sampleVars = c("spike", "batch",
                                      "subject_id"),
                                      separator = "_")

[Package MSPrep version 1.0.0 Index]