msPrepare {MSPrep} | R Documentation

## Summarize, filter, impute, transform and normalize metabolomics dataset

### Description

Wrapper function for the entire MSPrep pre-analytics pipeline. Calls msSummarize(), msFilter, msImpute(), and msNormalize().

### Usage

    msPrepare(
      data,
      cvMax = 0.5,
      minPropPresent = 1/3,
      filterPercent = 0.8,
      imputeMethod = c("halfmin", "bpca", "knn", "rf", "none"),
      kKnn = 5,
      nPcs = 3,
      maxIterRf = 10,
      nTreeRf = 100,
      compoundsAsNeighbors = FALSE,
      normalizeMethod = c("ComBat", "quantile", "quantile + ComBat", "median",
        "median + ComBat", "CRMN", "RUV", "SVA", "none"),
      nControl = 10,
      controls = NULL,
      nComp = 2,
      kRUV = 3,
      covariatesOfInterest = NULL,
      batch = NULL,
      transform = c("log10", "log2", "none"),
      replicate = "replicate",
      compVars = c("mz", "rt"),
      sampleVars = c("subject_id"),
      colExtraText = NULL,
      separator = NULL,
      missingValue = NA,
      returnSummaryDetails = FALSE,
      returnToSE = FALSE,
      returnToDF = FALSE
    )

### Arguments

`data` | Data set as either a data frame or 'SummarizedExperiement'.
`cvMax` | Decimal value from 0 to 1 representing the acceptable level of coefficient of variation between replicates.
`minPropPresent` | Decimal value from 0 to 1 representing the minimum proportion present to summarize with median or mean. Below this the compound will be set to 0.
`filterPercent` | Decimal value indicating filtration threshold. Compounds which are present in fewer samples than the specified proportion will be removed.
`imputeMethod` | String specifying imputation method. Options are "halfmin" (half the minimum value), "bpca" (Bayesian PCA), and "knn" (k-nearest neighbors), or "none" to skip imputation.
`kKnn` | Number of clusters for 'knn' method.
`nPcs` | Number of principle components used for re-estimation for 'bpca' method.
`maxIterRf` | Maximum number of iterations to be performed given the stopping criterion is not met beforehand for 'rf' method.
`nTreeRf` | Number of trees to grow in each forest for 'rf' method.
`compoundsAsNeighbors` | For KNN imputation. If TRUE, compounds will be used as neighbors rather than samples. Note that using compounds as neighbors is significantly slower than using samples.
`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), or "none" to skip normalization.
`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.
`covariatesOfInterest` | Sample variables used as covariates in normalization algorithms (required for ComBat, CRMN, and SVA).
`batch` | Name of the sample variable identifying batch.
`transform` | Select transformation to apply to data prior to normalization. Options are "log10", "log2", and "none".
`replicate` | Name of sample variable specifying replicate. Must match an element in 'sampleVars' or a column in the column data of a 'SummarizedExperiment'.
`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'.
`missingValue` | Specifies the abundance value which indicates missing data. May be a numeric or 'NA'.
`returnSummaryDetails` | Logical value specifying whether to return details of replicate summarization.
`returnToSE` | Logical value specifying whether to return as 'SummarizedExperiment'
`returnToDF` | Logical value specifying whether to return as data frame.

### Value

A data frame or 'SummarizedExperiment' with summarized technical replicates (if present), filtered compounds, missing values imputed, and transformed and normalized abundances. Default return type is set to match the data input but may be altered with the 'returnToSE' or 'returnToDF' arguments.

### Examples

    # Load example data
    data(msquant)

    # Call function to tidy, summarize, filter, impute, and normalize data
    peparedData <- msPrepare(msquant, cvMax = 0.50, minPropPresent = 1/3,
                             filterPercent = 0.8, imputeMethod = "halfmin",
                             normalizeMethod = "quantile",
                             compVars = c("mz", "rt"),
                             sampleVars = c("spike", "batch", "replicate",
                                            "subject_id"),
                             colExtraText = "Neutral_Operator_Dif_Pos_",
                             separator = "_", missingValue = 1,
                             returnToSE = FALSE)

[Package MSPrep version 1.4.0 Index]