SurvPlsClass: Survival PLS and Classification for metabolic data
In MetabolicSurv: A Biomarker Validation Approach for Classification and Predicting Survival Using Metabolomics Signature

Description Usage Arguments Details Value Author(s) References See Also Examples

The function performs partial least squares (PLS) and principal component regression on Metabolomics matrix and fit Cox proportional hazard model with covariates using the first PLS scores as covariates.

SurvPlsClass(
  Survival,
  Mdata,
  Censor,
  Reduce = TRUE,
  Select = 150,
  Prognostic = NULL,
  Plots = FALSE,
  Quantile = 0.5
)

`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 plots should be shown. Default is FALSE
`Quantile`	The cut off value for the classifier, default is the median cutoff

This function reduces larger metabolomics matrix to smaller version using supervised pca approach. The function performs the PLS on the reduced metabolomics matrix and fit Cox proportional hazard model with first PLS scores as a covariate afterwards. And classifier is then built based on the first PLS scores multiplied by its estimated regression coefficient. Patients are classified using median of the risk scores. The function can also perform grid analysis where the grid will be several quantiles but the default is median. This function can handle single and multiple metabolites. Prognostic factors can also be included to enhance classification.

A object is returned with the following values

`Survfit`	The cox proportional regression result using the first PCA
`Riskscores`	A vector of risk scores which is equal to the number of patents.
`Riskgroup`	The classification of the subjects based on the PCA into low or high risk group
`pc1`	The First PCA scores based on either the reduced Metabolite matrix or the full matrix
`KMplot`	The Kaplan-Meier survival plot of the riskgroup
`SurvBPlot`	The distribution of the survival in the riskgroup
`Riskpca`	The plot of Risk scores vs first PCA

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

\insertRef

ye1MetabolicSurv

coxph, EstimateHR, plsr, SurvPcaClass

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

## USING THE FUNCTION
Result = SurvPlsClass(Survival=Data$Survival, Mdata=t(Data$Mdata),
Censor=Data$Censor, Reduce = FALSE, Select = 150,
Prognostic = Data$Prognostic, Plots = FALSE, Quantile = 0.5)

## GETTING THE SURVIVAL REGRESSION OUTPUT
Result$SurvFit

## GETTING THE RISKSCORES
Result$Riskscores

### GETTING THE RISKGROUP
Result$Riskgroup

### OBTAINING THE FIRST PRINCIPAL COMPONENT SCORES
Result$pc1