QuantileAnalysis: Quantile sensitivity analysis

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

Description

The function performs sensitivity of the cut off quantile for obtaining the risk group obtained under SurvPlsClass, SurvPcaClass or Lasoelacox requires for the survival analysis and classification.

Usage

QuantileAnalysis(Survival, Mdata, Censor, Reduce = TRUE, Select = 150,
  Prognostic = NULL, Plots = FALSE, DimMethod = c("PLS", "PCA",
  "SM"), Alpha = 1)

Arguments

Survival	A vector of survival time with length equals to number of subjects
Mdata	A large or small metabolic profile matrix. A matrix with metabolic profiles where the number of rows should be equal to the number of metabolites and number of columns should be equal to number of patients.
Censor	A vector of censoring indicator
Reduce	A boolean parameter indicating if the metabolic profile matrix should be reduced, default is TRUE and larger metabolic profile matrix is reduced by supervised pca approach and first pca is extracted from the reduced matrix to be used in the classifier.
Select	Number of metabolites (default is 15) to be selected from supervised PCA. This is valid only if th argument Reduce=TRUE
Prognostic	A dataframe containing possible prognostic(s) factor and/or treatment effect to be used in the model.
Plots	A boolean parameter indicating if the graphical represenataion of the analysis should be shown. Default is FALSE and it is only valid for the PCA or PLS dimension method.
DimMethod	The dimension method to be used. PCA implies using the SurvPcaClass, PLS uses SurvPcaClass while SM uses the Lasoelacox which ruses the shrinkage method techniques such as lasso and elastic net.
Alpha	The mixing parameter for glmnet (see glmnet). The range is 0<= Alpha <= 1. The Default is 1

Details

This function investigates how each analysis differs from the general median cutoff of 0.5, therefore to see the sensitive nature of the survival result different quantiles ranging from 10th percentile to 90th percentiles were used. The sensitive nature of the quantile is investigated under SurvPlsClass, SurvPcaClass or Lasoelacox while relate to the 3 different Dimension method to select from.

Value

A Dataframe is returned depending on weather a data reduction method should be used or not. The dataframe contains the HR of the low risk group for each percentile.

Author(s)

Olajumoke Evangelina Owokotomo, [e-mail]

Ziv Shkedy

References

\insertRef

ye1MetabolicSurv

See Also

coxph,EstimateHR, SurvPcaClass, SurvPlsClass,Lasoelacox

Examples

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

## USING THE PCA METHOD
Result = QuantileAnalysis(Data$Survival,t(Data$Mdata),
Data$Censor,Reduce=FALSE, Select=150, Prognostic=Data$Prognostic,
Plots = TRUE,DimMethod="PCA",Alpha=1)

## USING THE PLS METHOD
Result = QuantileAnalysis(Data$Survival,t(Data$Mdata),
Data$Censor,Reduce=FALSE, Select=150, Prognostic=Data$Prognostic,
Plots = TRUE,DimMethod="PLS",Alpha=1)

## USING THE SM METHOD
Result = QuantileAnalysis(Data$Survival,t(Data$Mdata),
Data$Censor,Reduce=FALSE, Select=150, Prognostic=Data$Prognostic,
Plots = FALSE,DimMethod="SM",Alpha=1)

OlajumokeEvangelina/MetabolicSurv documentation built on Aug. 7, 2019, 1:06 p.m.