KODAMA (version 2.4)

normalization: Normalization Methods

Description

Collection of Different Normalization Methods.

Usage

normalization(Xtrain,Xtest=NULL, method = "pqn",ref=NULL)

Value

The function returns a list with 2 items or 4 items (if a test data set is present):

newXtrain

a normalized matrix (training data set).

coeXtrain

a vector of normalization coefficient of the training data set.

newXtest

a normalized matrix (test data set).

coeXtest

a vector of normalization coefficient of the test data set.

Arguments

Xtrain

a matrix of data (training data set).

Xtest

a matrix of data (test data set).(by default = NULL).

method

the normalization method to be used. Choices are "none", "pqn", "sum", "median", "sqrt" (by default = "pqn"). A partial string sufficient to uniquely identify the choice is permitted.

ref

Reference sample for Probabilistic Quotient Normalization. (by default = NULL).

Author

Stefano Cacciatore and Leonardo Tenori

Details

A number of different normalization methods are provided:

• "none": no normalization method is applied.

• "pqn": the Probabilistic Quotient Normalization is computed as described in Dieterle, et al. (2006).

• "sum": samples are normalized to the sum of the absolute value of all variables for a given sample.

• "median": samples are normalized to the median value of all variables for a given sample.

• "sqrt": samples are normalized to the root of the sum of the squared value of all variables for a given sample.

References

Dieterle F,Ross A, Schlotterbeck G, Senn H.
Probabilistic Quotient Normalization as Robust Method to Account for Diluition of Complex Biological Mixtures. Application in 1H NMR Metabolomics.
Anal Chem 2006;78:4281-90.

Cacciatore S, Luchinat C, Tenori L
Knowledge discovery by accuracy maximization.
Proc Natl Acad Sci U S A 2014;111(14):5117-22. doi: 10.1073/pnas.1220873111. Link

Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA
KODAMA: an updated R package for knowledge discovery and data mining.
Bioinformatics 2017;33(4):621-623. doi: 10.1093/bioinformatics/btw705. Link

See Also

scaling

Examples

data(MetRef)
u=MetRef$data;
u=u[,-which(colSums(u)==0)]
u=normalization(u)$newXtrain
u=scaling(u)$newXtrain
class=as.numeric(as.factor(MetRef$gender))
cc=pca(u)
plot(cc$x,pch=21,bg=class)