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

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)