Normalization: Normalization of features

In IntClust: Integration of Multiple Data Sets with Clustering Techniques

Description

If data of different scales are being employed by the user, it is recommended to perform a normalization to make the data structures comparable.

Usage

Normalization(Data, method = c("Quantile", "Fisher-Yates", "Standardize",
  "Range", "Q", "q", "F", "f", "S", "s", "R", "r"))

Arguments

Data	A data matrix. It is assumed the rows are corresponding with the objects.
method	A method of normalization. Should be one of "Quantile","Fisher-Yates","standardize","Range" or any of the first letters of these names.

Details

The method "Quantile" refers to the Quantile-Normalization widely used in omics data. The "Fisher-Yates" normalization has a similar approach as the Quantile- Normalization but does not rely on the data, just on the number of rows present in the data matrix. The "Standardize" method refers to the stdize function of the pls package and centers and scales the data matrix. The method "Range" computes the maximum and minimum value of the matrix and determines the range. Every value is then reduced by the minimum and divided by the range of the data matrix. The latter normalization will result in values between 0 and 1.

Value

The returned value is a normalized matrix.

Examples

x=matrix(rnorm(100),ncol=10,nrow=10)
Norm_x=Normalization(x,method="R")

IntClust documentation built on May 2, 2019, 5:51 a.m.