getRTNormalizedMatrix {NormalyzerDE} | R Documentation

## Perform RT-segmented normalization by performing the supplied normalization over retention-time sliced data

### Description

The function orders the retention times and steps through them using the supplied step size (in minutes). If smaller than a fixed lower boundary the window is expanded to ensure a minimum amount of data in each normalization step. An offset can be specified which can be used to perform multiple RT-segmentations with partial overlapping windows.

### Usage

    getRTNormalizedMatrix(rawMatrix, retentionTimes, normMethod,
      stepSizeMinutes = 1, windowMinCount = 100, offset = 0,
      noLogTransform = FALSE)

### Arguments

`rawMatrix` | Target matrix to be normalized
`retentionTimes` | Vector of retention times corresponding to rawMatrix
`normMethod` | The normalization method to apply to the time windows
`stepSizeMinutes` | Size of windows to be normalized
`windowMinCount` | Minimum number of values for window to not be expanded.
`offset` | Whether time window should shifted half step size
`noLogTransform` | Don't log-transform the data

### Value

Normalized matrix

### Examples

    data(example_data_small)
    data(example_design_small)
    data(example_data_only_values)
    dataMat <- example_data_only_values
    retentionTimes <- as.numeric(example_data[, "Average.RT"])
    performCyclicLoessNormalization <- function(rawMatrix) {
        log2Matrix <- log2(rawMatrix)
        normMatrix <- limma::normalizeCyclicLoess(log2Matrix, method="fast")
        colnames(normMatrix) <- colnames(rawMatrix)
        normMatrix
    }
    rtNormMat <- getRTNormalizedMatrix(dataMat, retentionTimes,
    performCyclicLoessNormalization, stepSizeMinutes=1, windowMinCount=100)

[Package NormalyzerDE version 1.2.0 Index]