getRTNormalizedMatrix: Perform RT-segmented normalization by performing the supplied...

In ComputationalProteomics/NormalyzerDE: Evaluation of normalization methods and calculation of differential expression analysis statistics

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)

ComputationalProteomics/NormalyzerDE documentation built on Sept. 18, 2023, 9:15 p.m.