wrapper.normalizeD2 {DAPAR}R Documentation

Normalisation

Description

Provides several methods to normalize quantitative data from a MSnSet object. They are organized in four main families : Strong Rescaling, Median Centering, Mean Centering, Mean CenteringScaling. For the first family, two sub-categories are available : the sum by columns and the quantiles method. For the three other families, two categories are available : "Overall" which means that the value for each protein (ie line in the expression data tab) is computed over all the samples ; "within conditions" which means that the value for each protein (ie line in the exprs() data tab) is computed condition by condition.

Usage

wrapper.normalizeD2(obj, method, type, scaling = FALSE, quantile = 0.15)

Arguments

obj

An object of class MSnSet.

method

One of the following : Global Alignment (for normalizations of important magnitude), Quantile Centering, Mean Centering.

type

For the method "Global Alignment", the parameters are: "sum by columns": operates on the original scale (not the log2 one) and propose to normalize each abundance by the total abundance of the sample (so as to focus on the analyte proportions among each sample). "Alignment on all quantiles": proposes to align the quantiles of all the replicates; practically it amounts to replace abundances by order statistics. For the two other methods, the parameters are "overall" (shift all the sample distributions at once) or "within conditions" (shift the sample distributions within each condition at a time).

scaling

A boolean that indicates if the variance of the data have to be forced to unit (variance reduction) or not.

quantile

A float that corresponds to the quantile used to align the data.

Value

An instance of class MSnSet where the quantitative data in the exprs() tab has been normalized.

Author(s)

Samuel Wieczorek

Examples

require(DAPARdata)
data(Exp1_R25_pept)
wrapper.normalizeD2(Exp1_R25_pept[1:1000], "Quantile Centering", "within conditions")

[Package DAPAR version 1.10.4 Index]