CVSimet: Cross validation for sequentially increases metabolites

In MetabolicSurv: A Biomarker Validation Approach for Classification and Predicting Survival Using Metabolomics Signature

Description

This function does cross validation for the metabolite by metabolite analysis while sequentially increasing the number of metabolites as specified.

Usage

CVSimet(Object, Top = seq(5, 100, by = 5), Survival, Censor, Prognostic = NULL)

Arguments

Object	An object of class cvmm
Top	The Top k number of metabolites to be used
Survival	A vector of survival time with length equals to number of subjects
Censor	A vector of censoring indicator
Prognostic	A dataframe containing possible prognostic(s) factor and/or treatment effect to be used in the model.

Details

This function firstly processes the cross validation for the metabolite by metabolite analysis results, and then sequentially considers top k metabolites. The function recompute first PCA or PLS on train data and estimate risk scores on both test and train data only on the metabolite matrix with top k metabolites. Patients are then classified as having low or high risk based on the test data where the cutoff used is median of the risk score. The process is repeated for each top K metabolite sets.

Value

A object of class cvsim is returned with the following values

HRpca	A 3-way array in which first, second, and third dimensions correspond to number of metabolites, Hazard ratio infromation(Estimated HR, LowerCI and UpperCI), and number of cross validation respectively. This contains the estimated HR on test data and dimension reduction method is PCA.
HRpls	A 3-way array in which first, second, and third dimensions correspond to number of metabolites, Hazard ratio infromation(Estimated HR, LowerCI and UpperCI), and number of cross validation respectively. This contains the estimated HR on test data and dimension reduction method is PLS.
Nmets	The number of metabolites in the reduced matrix
Ncv	The number of cross validation done
Top	A sequence of top k metabolites considered. Default is Top=seq(5,100,by=5)

Author(s)

Olajumoke Evangelina Owokotomo, [e-mail]

Ziv Shkedy

See Also

MSpecificCoxPh

Examples

## FIRSTLY SIMULATING A METABOLIC SURVIVAL DATA
Data = MSData(nPatients = 100, nMet = 150, Prop = 0.5)

## GETTING THE cvmm OBJECT
Result = CVMetSpecificCoxPh(Fold=3,Survival=Data$Survival,
Mdata=t(Data$Mdata),Censor= Data$Censor,Reduce=TRUE,Select=150,
Prognostic=Data$Prognostic,Quantile = 0.5,Ncv=3)

## USING THE FUNCTION
 Result2 = CVSimet(Result, Top = seq(5, 100, by = 5), Data$Survival,
 Data$Censor,Prognostic = Data$Prognostic)

## GET THE CLASS OF THE OBJECT
class(Result2)     # An "cvsim" Class

MetabolicSurv documentation built on June 11, 2021, 9:06 a.m.