normalization | R Documentation |

Collection of Different Normalization Methods.

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

`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 " |

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

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.

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. |

Stefano Cacciatore and Leonardo Tenori

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

`scaling`

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$x,pch=21,bg=class,xlab=cc$txt[1],ylab=cc$txt[2])

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