getSmoothedRTNormalizedMatrix: Generate multiple RT time-window normalized matrices where...

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

Description

Uses the function getRTNormalizedMatrix to generate multiple normalized matrices which are shifted respective to each other and finally merged into a single matrix. This could potentially reduce effect of fluctuations within individual windows.

Usage

getSmoothedRTNormalizedMatrix(rawMatrix, retentionTimes, normMethod,
  stepSizeMinutes, windowShifts = 2, windowMinCount = 100,
  mergeMethod = "mean", 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
windowShifts	Number of frame shifts.
windowMinCount	Minimum number of features within window.
mergeMethod	Layer merging approach. Mean or median.
noLogTransform	Don't log transform the input

Value

Normalized matrix

Examples

data(example_data_small)
data(example_data_only_values)
data(example_design_small)
retentionTimes <- as.numeric(example_data[, "Average.RT"])
dataMat <- example_data_only_values
performCyclicLoessNormalization <- function(rawMatrix) {
    log2Matrix <- log2(rawMatrix)
    normMatrix <- limma::normalizeCyclicLoess(log2Matrix, method="fast")
    colnames(normMatrix) <- colnames(rawMatrix)
    normMatrix
}
rtNormMat <- getSmoothedRTNormalizedMatrix(dataMat, retentionTimes, 
    performCyclicLoessNormalization, stepSizeMinutes=1, windowMinCount=100, 
    windowShifts=2, mergeMethod="median")

NormalyzerDE documentation built on Nov. 8, 2020, 8:22 p.m.