Cross Validations for Lasso Elastic Net Survival predictive models and Classification

Description

The function does cross validation for Lasso, Elastic net and Ridge regressions models before the survial analysis and classification. The survival analysis is based on the selected metabolites in the presence or absene of prognostic factors.

Usage

CVLasoelacox(
  Survival,
  Censor,
  Mdata,
  Prognostic,
  Quantile = 0.5,
  Metlist = NULL,
  Standardize = TRUE,
  Reduce = TRUE,
  Select = 15,
  Alpha = 1,
  Fold = 4,
  Ncv = 10,
  nlambda = 100
)

Arguments

Details

The function performs the cross validations for Lasso, Elastic net and Ridge regressions models for Cox proportional hazard model. Metabolites are selected at each iteration and then use for the classifier. This implies that predictive metabolites signature is varied from one cross validation to the other depending on selection. The underline idea is to investigate the Hazard Ratio for the train and test data based on the optimal lambda selected for the non-zero shrinkage coefficients, the nonzero selected metabolites will thus be used in the survival analysis and in calculation of the risk scores for each sets of data.

Value

A object of class cvle is returned with the following values

• Coef.mat :A matrix of coefficients with rows equals to number of cross validations and columns equals to number of metabolites.

• RuntimeA vector of runtime for each iteration measured in seconds.

• lambdaA vector of estimated optimum lambda for each iterations.

• nA vector of the number of selected metabolites

• HRTrainA matrix of survival information for the training dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

• HRTestA matrix of survival information for the test dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

• pldA vector of partial likelihood deviance at each cross validations.

• Met.matA matrix with 0 and 1. Number of rows equals to number of iterations and number of columns equals to number of metabolites. 1 indicates that the particular metabolite was selected or had nonzero coefficient and otherwise it is zero.

• MdataThe Metabolite data matrix that was used for the analysis either same as Mdata or a reduced version.

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

coxph, EstimateHR, glmnet, Lasoelacox

Examples

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

## USING THE FUNCTION
Results = CVLasoelacox(Survival = Data$Survival,Censor = Data$Censor,
Mdata = t(Data$Mdata),Prognostic = Data$Prognostic, Quantile = 0.5,
Metlist = NULL,Standardize = TRUE, Reduce=FALSE, Select=15,
Alpha = 1,Fold = 4,Ncv = 10,nlambda = 100)

## NUMBER OF SELECTED METABOLITES PER CV
Results@n

## GET THE MATRIX OF COEFFICIENTS
Results@Coef.mat

## SURVIVAL INFORMATION OF THE TRAIN DATASET
Results@HRTrain

## SURVIVAL INFORMATION OF THE TEST DATASET
Results@HRTest

Cross validation for majority votes

Description

This function does cross validation for the Majority votes based classification.

Usage

CVMajorityvotes(
  Survival,
  Censor,
  Prognostic = NULL,
  Mdata,
  Reduce = TRUE,
  Select = 15,
  Fold = 3,
  Ncv = 100
)

Arguments

Details

This function does cross validation for the Majority votes based classification which is a cross validated approach to Majorityvotes.

Value

A object of class cvmv is returned with the following values

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

Majorityvotes

Examples

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

## USING THE FUNCTION
Result = CVMajorityvotes(Survival=Data$Survival,Censor=Data$Censor,
Prognostic=Data$Prognostic, Mdata=t(Data$Mdata), Reduce=FALSE,
Select=15, Fold=3, Ncv=10)

## GET THE CLASS OF THE OBJECT
class(Result)     # An "cvmv" Class

##  METHOD THAT CAN BE USED FOR THE RESULT
show(Result)
summary(Result)

Cross validation for the Metabolite specific analysis

Description

The function performs cross validation for each metabolite depending the number of fold which guides the division into the train and testing dataset. The classifier is then obtained on the training dataset to be validated on the test dataset

Usage

CVMetSpecificCoxPh(
  Fold = 3,
  Survival,
  Mdata,
  Censor,
  Reduce = TRUE,
  Select = 150,
  Prognostic = NULL,
  Quantile = 0.5,
  Ncv = 3
)

Arguments

Details

This function performs the cross validation for metabolite by metabolite analysis. The data will firstly be divided into data train dataset and test datset. Furthermore, a metabolite-specific model is fitted on train data and a classifier is built. In addition, the classifier is then evaluated on test dataset for each particular metabolite. The Process is repeated for all the full or reduced metabolites to obtaind the HR statistics of the low risk group. The following steps depends on the number of cross validation specified.

Value

A object of class cvmm is returned with the following values

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

coxph, EstimateHR, MSpecificCoxPh,

Examples

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

## USING THE FUNCTION
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)

## GET THE CLASS OF THE OBJECT
class(Result)     # An "cvmm" Class

##  METHOD THAT CAN BE USED FOR THE RESULT
show(Result)
summary(Result)
plot(Result)

Cross Validations for PCA and PLS based methods

Description

This function does cross validation for the analysis performs by SurvPcaClass and SurvPlsClass functions where the dimension reduction methods can either be PCA and PLS.

Usage

CVPcaPls(
  Fold = 3,
  Survival,
  Mdata,
  Censor,
  Reduce = TRUE,
  Select = 15,
  Prognostic = NULL,
  Ncv = 5,
  DR = "PCA"
)

Arguments

Details

This function does cross validation for the analysis using two reduction method. The reduction method can be PCA or PLS. If it is PCA then the SurvPcaClass is internally used for the cross validation and SurvPlsClass otherwise.

Value

A object of class cvpp is returned with the following values

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

References

Bair E, Hastie T, Debashis P, Tibshirani R (2006). “Prediction by supervised principal components.” American Statistics Association,, 101(473), 119–137.

See Also

SurvPlsClass, SurvPcaClass

Examples

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

## USING THE FUNCTION
Result = CVPcaPls(Fold = 4, Survival = Data$Survival,
Mdata = t(Data$Mdata), Censor = Data$Censor, Reduce=TRUE,
Select=19, Prognostic= Data$Prognostic,Ncv=55,DR ="PLS")

## GET THE CLASS OF THE OBJECT
class(Result)     # An "cvpp" Class

##  METHOD THAT CAN BE USED FOR THE RESULT
show(Result)
summary(Result)
plot(Result)

Cross validation for sequentially increases metabolites

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

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

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

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

Survival and Prognostic Data .

Description

A dataset containing the riskscore, survival parameters (Overall survival and censoring indicator) and other pronostic factors of 149 subjects.

Usage

data(DataHR)

Format

A data frame with 149 rows and 5 variables:

Riskscore

Riskscores of the subjects

Survival

Overall survival of the subjects

Censor

Censoring indicator for all the patients; 1= Dead and 0 = Alive

Gender

The first prognostic factor which is the gender of all the patients; 1=Male and 0 = Female

Stage

The second prognostic factor which is the cancer stage of all the patients; 1= Early stage and 0= Advanced stage

...

Source

https://bmccancer.biomedcentral.com/articles/10.1186/s12885-018-4755-1

Examples

data(DataHR)
summary(DataHR[,1:2])

Null Distribution of the Estimated HR

Description

This function generates the null distribution of the HR by permutation approach. Several ways of permutation setting can be implemented. That is, function can be used to generate null distributions for four different validation schemes, PLS based, PCA based, Majority votes based and Lasso based.

Usage

DistHR(
  Survival,
  Censor,
  Mdata,
  Prognostic = NULL,
  Quantile = 0.5,
  Reduce = FALSE,
  Select = 15,
  nperm = 100,
  case = 2,
  Validation = c("PLSbased", "PCAbased", "L1based", "MVbased")
)

Arguments

Details

This function generates the null distribution of the HR by permutation approach either using a large metabolite matrix or a reduced version by supervised pca approach. Several ways of permutation setting can be implemented. That is, the function can be used to generate null distributions for four different validation schemes which are PLS based, PCA based, Majority votes based and Lasso based. Note this function internally calls function SurvPcaClass, SurvPlsClass, Majorityvotes, and Lasoelacox.

Value

A object of class perm is returned with the following values

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

coxph, EstimateHR, SurvPcaClass, SurvPlsClass, Majorityvotes, Lasoelacox, EstimateHR, Lasoelacox

Examples

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

## USING THE FUNCTION
Example <- DistHR(Survival = Data$Survival,Mdata = t(Data$Mdata),
Censor = Data$Censor,Reduce=FALSE,Select=15,Prognostic=Data$Prognostic,
Quantile = 0.5, nperm=10, case=2, Validation=c("L1based"))

Classification, Survival Estimation and Visualization

Description

The Function classifies subjects into low and high risk group using the risk scores based on the cut-off percentile specified. It also visualize survival fit along with HR estimates.

Usage

EstimateHR(
  Risk.Scores,
  Data.Survival,
  Prognostic = NULL,
  Plots = FALSE,
  Quantile = 0.5
)

Arguments

Details

The risk scores obtained using the signature is used to generate the risk group by dividing subjects into low and high risk group. A Cox model is then fitted with the risk group as covariate in the presence or absence of prognostic factors and or treatment effect. The extent of survival in the risk groups is known.

Value

An object of is returned, which is a list with the results of the cox regression and some informative plot concerning survival of the risk group.

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

coxph

Examples

### Classification and estimating with prognostic factors
data(DataHR)
Result = EstimateHR(Risk.Scores=DataHR[,1],Data.Survival=DataHR[,2:3]
,Prognostic=DataHR[,4:5],Plots=FALSE,Quantile=0.50)

### Classification and estimating without prognostic factors
data(DataHR)
Result = EstimateHR(Risk.Scores=DataHR[,1],Data.Survival=DataHR[,2:3]
,Prognostic=NULL,Plots=FALSE,Quantile=0.50)

Inner and Outer Cross Validations for Lasso Elastic Net Survival predictive models and Classification

Description

The function does cross validation for Lasso, Elastic net and Ridge regressions models based on fixed or top selected metabolites from CVLasoelacox with classifier validated on a independent sample for the survial analysis and classification. The survival analysis is based on the selected metabolites in the presence or absene of prognostic factors.

Usage

Icvlasoel(
  Survival,
  Censor,
  Prognostic = NULL,
  Mdata,
  Fold = 3,
  Ncv = 50,
  Nicv = 100,
  Alpha = 0.1,
  TopK,
  Weights = FALSE
)

Arguments

Details

The function does cross validation for Lasso, Elastic net and Ridge regressions models based on fixed or top selected metabolites from CVLasoelacox with classifier validated on a independent sample for the survial analysis and classification. The survival analysis is based on the selected metabolites in the presence or absene of prognostic factors. The classifier is built on the weights obtain from the inner cross validations results and it is tested on out-of-bag data. These weights can be fixed or can be updated at each outer iteration. If weights are not fixed then patients are classified using majority votes. Otherwise, weights obtained from the inner cross validations are summarized by mean weights and used in the classifier. Inner cross validations are performed by calling to function CVLasoelacox. Hazard ratio for low risk group is estimated using out-of-bag data.

Value

A object of class fcv is returned with the following values

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

CVLasoelacox, EstimateHR, glmnet, Lasoelacox

Examples

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

## USING THE FUNCTION
Results = Icvlasoel(Data$Survival, Data$Censor, Data$Prognostic,
t(Data$Mdata), Fold = 3,Ncv = 5, Nicv = 7, Alpha = 1,
TopK = colnames(Data$Mdata[,80:100]), Weights = FALSE)

## NUMBER OF Outer CV
Results@Ncv
## NUMBER OF Inner CV
Results@Nicv

## HR of low risk group for the Inner CV
Results@HRInner

## HR of low risk group for the out of bag dataset
Results@HRTest

## The weight for the analysis
Results@Weight

Wapper function for glmnet

Description

The function uses the glmnet function to firstly do the variable selection either with Lasso, Elastic net or ridge regressions before the survial analysis. The survival analysis is based on the selected metabolites in the presence or absence of prognostic factors.

Usage

Lasoelacox(
  Survival,
  Censor,
  Mdata,
  Prognostic,
  Quantile = 0.5,
  Metlist = NULL,
  Plots = FALSE,
  Standardize = TRUE,
  Alpha = 1,
  Fold = 4,
  nlambda = 100
)

Arguments

Details

This is a wrapper function for glmnet and it fits models using either Lasso, Elastic net and Ridge regressions. This is done in the presence or absence of prognostic factors. The prognostic factor when avaialable will always be forced to be in the model so no penalty for it. Optimum lambda will be used to select the non-zero shrinkage coefficients, the nonzero selected metabolites will thus be used in the survival analysis and in calculation of the risk scores.

Value

A object is returned with the following values

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

coxph, EstimateHR, glmnet,

Examples

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

## USING THE FUNCTION
Results = Lasoelacox(Survival=Data$Survival, Censor=Data$Censor,
Mdata=t(Data$Mdata), Prognostic = Data$Prognostic, Quantile = 0.5,
Metlist = NULL, Plots = FALSE, Standardize = TRUE, Alpha = 1)

## VIEW THE SELECTED METABOLITES
Results$Selected.mets
## NUMBER OF SELECTED METABOLITES
Results$n

## VIEW THE CLASSIFICATION GROUP OF EACH SUBJECT
Results$Risk.Group

## VIEW THE SURVIVAL ANALYSIS RESULT
Results$SurvFit

## TO CHECK IF THERE WAS ANY SELECTION
Results$Select

Generate Artificial Metabolic Survival Data

Description

The Function generates metabolic profile and survival dataset of any number of patients and also their survival information.

Usage

MSData(nPatients = 100, nMet = 150, Prop = 0.5)

Arguments

Details

The function generates the metabolic profile where small set of metabolites (30) are informative and rest of them are set as noisy metabolites. Also, the survival time and Censoring information are generated based on first right singular vectors of svd of the metabolic profile matrix. It also generates other prognostic factors such as Age, Stage and Gender which are slightly correlated with survival time.

Value

An object of class list is returned with the following items .

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

coxph

Examples

#GENERATE SOME METABOLIC SURVIVAL DATA WITH PROGNOSTIC FACTORS

Data<-MSData(nPatients=100,nMet=150,Prop=0.5)

SurvTime<-Data$Survival
Censor<-Data$Censor
ProgFact<-Data$Prognostic
MetData<-Data$Mdata
Metnames<-Data$Met.names

Metabolite by metabolite Cox proportional analysis

Description

The Function fits cox proportional hazard model and does classification for each metabolite

Usage

MSpecificCoxPh(
  Survival,
  Mdata,
  Censor,
  Reduce = FALSE,
  Select = 15,
  Prognostic = NULL,
  Quantile = 0.5
)

Arguments

Details

This function fits metabolite by metabolite Cox proportional hazard model and perform the classification based on a specified quantile. Risk score will be been estimated using a single metabolite. Function is useful for majority vote classification method and metabolite by metabolite analysis and also for top K metabolites.

Value

A object of class ms is returned with the following values

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

coxph, EstimateHR

Examples

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

## USING THE FUNCTION
Example1 = MSpecificCoxPh(Survival = Data$Survival,
Mdata = t(Data$Mdata), Censor = Data$Censor, Reduce = FALSE,
Select = 15,Prognostic = Data$Prognostic, Quantile = 0.5)

## KNOWLING THE CLASS OF THE OUTPUT
class(Example1)

## EXTRACTING THE COMPONENT OF THE FUNCTION
### HAZARD RATIO INFORMATION FOR EACH METABOLITES
Example1@HRRG

### COX MODEL RESULT FOR EACH METABOLITES
Example1@Result

### CLASSIFICATION FOR EACH METABOLITES
Example1@Group

Classifiction for Majority Votes

Description

The Function fits cox proportional hazard model and does classification based on the majority votes.

Usage

Majorityvotes(Result, Prognostic, Survival, Censor, J = 1)

Arguments

Details

The Function fits cox proportional hazard model and does classification based on the majority votes while estimating the Hazard ratio of the low risk group. The function firstly count the number of low risk classification for each subject based on the metabolite specific analysis which determines the majority votes. In addition, It visualizes the metabolic specific calssification for the subjects. 25 subjects is taken for visualization purpose.

Value

A list is returned with the following values

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

References

Hastie T, Tibshirani R, Friedman J (2001). The elements of statistical learning: data mining, inference, and prediction: with 200 full-color illustrations. New York: Springer-Verlag.

See Also

MSpecificCoxPh, coxph, EstimateHR

Examples

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

## RUNNING THE METABOLITE SPECIFIC FUNCTION
Example1 = MSpecificCoxPh(Survival = Data$Survival,
Mdata = t(Data$Mdata), Censor = Data$Censor, Reduce = FALSE,
Select = 15,Prognostic = Data$Prognostic, Quantile = 0.5)

## USING THE FUNCTION
Result2 = Majorityvotes(Example1,Data$Prognostic, Data$Survival,Data$Censor,J=2)

## THE SURVIVAL ANALYSIS FOR MAJORITY VOTE RESULT
 Result2$Model.result

### THE MAJORITY VOTE FOR EACH SUBJECT
Result2$N

### THE MAJORITY VOTE CLASSIFICATION FOR EACH SUBJECT
Result2$Classif

### THE GROUP FOR EACH SUBJECT BASED ON THE METABOLITE SPPECIFIC ANALYSIS
Result2$Group

Frequency of Selected Metabolites from the LASSO, Elastic-net Cross-Validation

Description

The function selects the frquency of selection from the shrinkage method (LASSO, Elastic-net) based on cross validation, that is the number of times each metabolite occur during the cross-validation process. In case of large metabolomic matrix then the N argument can be used to select metabolites occurence at a particular frequency.

Usage

MetFreq(Object, TopK = NULL, N = 3)

Arguments

Details

This function outputs the mostly selected metabolites during the LASSO and Elastic-net cross validation. Selected top metabolites are ranked based on frequency of selection and also a particular frequency cqn be selected. In addition, it visualizes the selected top metabolites based on the minimum frequency specified.

Value

A vector of metabolites and their frequency of selection. Also, a graphical representation is displayed.

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

cvmm, coxph, EstimateHR,CVLasoelacox

Examples

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

## CROSS-VALIDATION FOR LASSO AND ELASTIC-NET
Result = CVLasoelacox(Survival = Data$Survival,
Censor = Data$Censor, Mdata = t(Data$Mdata),
Prognostic = Data$Prognostic, Quantile = 0.5,
Metlist = NULL,Standardize = TRUE, Reduce=FALSE, Select=15,
Alpha = 1,Fold = 4,Ncv = 10,nlambda = 100)

## CONFIRMING THE CLASS
class(Result)

## USING THE FUNCTION
MetFreq(Result,TopK = 5, N=5)

MetabolicSurv: A biomarker validation approach for predicting survival using metabolic signature.

Description

This package develope biomarker signature for metabolic data. It contains a set of functions and cross validation methods to validate and select biomarkers when the outcome of interest is survival. The package can handle prognostic factors and mainly metabolite matrix as input, the package can served as biomarker validation tool.

MetabolicSurv functions

1. It can be used with any form of high dimensional/omics data such as: Metabolic data, Gene expression matrix, incase you dont have a data it can simulate hypothetical scinerio of a high dimensional data based on the desired biological parameters

2. It developed any form of signature from the high dimensional data to be used for other purpose

3. It also employs data reduction techniques such as PCA, PLS and Lasso

4. It classifies subjects based on the signatures into Low and high risk group

5. It incorporate the use of subject prognostic information for the to enhance the biomarker for classification

6. It gives information about the survival rate of subjects depending on the classification

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

Sequential Increase in Metabolites for the PCA or PLS classifier

Description

The Function fits cox proportional hazard model and does classification by sequentially increasing the metabolites using either PCA or PLS based on the topK metabolites specified.

Usage

SIMet(
  TopK = 15,
  Survival,
  Mdata,
  Censor,
  Reduce = TRUE,
  Select = 50,
  Prognostic = NULL,
  Plot = FALSE,
  DimMethod = c("PLS", "PCA"),
  ...
)

Arguments

Details

This function sequentially increase the number of top K metabolites to be used in the PCA or PLS methods in order to obtain the risk score. This function internally calls MSpecificCoxPh to rank the metabolites based on HR. Therefore metabolites can be ordered based on increasing order of the HR for low risk group. Thereafter, the function takes few top K (15 is the default) to be used in the sequential analysis.

Value

A list containing a data frame with estimated HR along with 95% CI at each TopK value for the sequential analysis.

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

References

Vinzi VE, Chin WW, Henseler J, Wang H (2010). Handbook of Partial Least Squares: Concepts, Methods and Applications, 1st edition. Springer Publishing Company, Incorporated.

Bair E, Hastie T, Debashis P, Tibshirani R (2006). “Prediction by supervised principal components.” American Statistics Association,, 101(473), 119–137.

See Also

coxph, EstimateHR, MSpecificCoxPh, SurvPcaClass, SurvPcaClass

Examples

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

## USING THE FUNCTION
Example1 = SIMet(TopK = 10, Survival=Data$Survival,
Mdata=t(Data$Mdata), Censor=Data$Censor, Reduce = TRUE,
Select = 50,Prognostic = Data$Prognostic, Plot = TRUE, DimMethod ="PLS")

## FOR THE HR STATISTICS
Example1$Result

## FOR THE GRAPHICAL OUTPUT
Example1$TopKplot

Survival PCA and Classification for metabolic data

Description

The function performs principal component analysis (PCA) on Metabolomics matrix and fit Cox proportional hazard model with covariates using also the first PCA as covariates.

Usage

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

Arguments

Details

This function can also be used to perform the grid analysis where the grid will be several quantile values and default is 0.5 which is the median cut-off. This function can handle single and multiple metabolites. For larger Metabolomics matrix, this function will reduce larger Metabolomics matrix to smaller version using supervised pca approach and this is by default done and can be control by using the argument Reduce. Other prognostic factors can be included to the model.

Value

A object of class SurvPca is returned with the following values

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

References

Bair E, Hastie T, Debashis P, Tibshirani R (2006). “Prediction by supervised principal components.” American Statistics Association,, 101(473), 119–137.

See Also

coxph, EstimateHR, princomp, SurvPlsClass

Examples

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

## USING THE FUNCTION
Result = SurvPcaClass(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

Survival PLS and Classification for metabolic data

Description

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.

Usage

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

Arguments

Details

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.

Value

A object is returned with the following values

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

References

Bair E, Hastie T, Debashis P, Tibshirani R (2006). “Prediction by supervised principal components.” American Statistics Association,, 101(473), 119–137.

See Also

coxph, EstimateHR, plsr, SurvPcaClass

Examples

## 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

Constructor for the cvle class

Description

Constructor for the cvle class

Usage

cvle(Coef.mat, Runtime, lambda, n, Met.mat, HRTrain, HRTest, pld, Mdata)

Arguments

Value

object of class cvle

The cvle Class.

Description

The cvle Class.

Slots

Coef.mat

A matrix of coefficients with rows equals to number of cross validations and columns equals to number of metabolites.

Runtime

A vector of runtime for each iteration measured in seconds.

lambda

A vector of estimated optimum lambda for each iterations.

n

A vector of the number of selected metabolites

Met.mat

A matrix with 0 and 1. Number of rows equals to number of iterations and number of columns equals to number of metabolites. 1 indicates that the particular metabolite was selected or had nonzero coefficient and otherwise it is zero.

HRTrain

A matrix of survival information for the training dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

HRTest

A matrix of survival information for the test dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

pld

A vector of partial likelihood deviance at each cross validations.

Mdata

The metabolite matrix that was used for the analysis which can either be the full the full data or a reduced supervised PCA version.

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

EstimateHR, glmnet, Lasoelacox

Examples

## GENERATE SOME METABOLIC SURVIVAL DATA WITH PROGNOSTIC FACTORS
Data<-MSData(nPatients=100,nMet=150,Prop=0.5)

## USE THE FUNCTION
Eg = CVLasoelacox(Survival = Data$Survival,Censor = Data$Censor,
Mdata = t(Data$Mdata),Prognostic = Data$Prognostic, Quantile = 0.5,
Metlist = NULL,Standardize = TRUE, Reduce=FALSE, Select=15,
Alpha = 1,Fold = 4,Ncv = 10,nlambda = 100)

## GET THE CLASS OF THE OBJECT
class(Eg)     # An "cvle" Class

##  METHOD THAT CAN BE USED FOR THIS CLASS
show(Eg)
summary(Eg)
plot(Eg, type =3)

Constructor for the cvmm class

Description

Constructor for the cvmm class

Usage

cvmm(HRTrain, HRTest, train, test, n.mets, Ncv, Rdata)

Arguments

Value

object of class cvmm

The cvmm Class.

Description

The cvmm Class.

Slots

HRTrain

A 3-way array, The first dimension is the number of metabolites, the second dimension is the HR statistics for the low risk group in the train dataset (HR,1/HR LCI, UCI) while the third dimension is the number of cross validation performed.

HRTest

A 3-way array, The first dimension is the number of metabolites, the second dimension is the HR statistics for the low risk group in the test dataset (HR,1/HR LCI, UCI) while the third dimension is the number of cross validation performed.

train

The selected subjects for each CV in the train dataset

test

The selected subjects for each CV in the test dataset

n.mets

The number of metabolite used in the analysis

Ncv

The number of cross validation performed

Rdata

The Metabolite data matrix that was used for the analysis either same as Mdata or a reduced version

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

CVMetSpecificCoxPh

Examples

## GENERATE SOME METABOLIC SURVIVAL DATA WITH PROGNOSTIC FACTORS
Data<-MSData(nPatients=100,nMet=150,Prop=0.5)

## USING THE FUNCTION
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)

## GET THE CLASS OF THE OBJECT
class(Result)     # An "cvmm" Class

##  METHOD THAT CAN BE USED FOR THIS CLASS
show(Result)
summary(Result)
plot(Result)

Constructor for the cvmv class

Description

Constructor for the cvmv class

Usage

cvmv(HRTrain, HRTest, Ncv, Mdata, Progfact)

Arguments

Value

object of class cvmv

The cvmv Class.

Description

The cvmv Class.

Slots

HRTrain

A matrix of survival information for the training dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

HRTest

A matrix of survival information for the test dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

Ncv

The number of cross validation used

Mdata

The Metabolite data matrix that was used for the analysis either same as Mdata or a reduced version.

Progfact

The names of prognostic factors used

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

Majorityvotes, CVPcaPls, SurvPcaClass, SurvPlsClass

Examples

## GENERATE SOME METABOLIC SURVIVAL DATA WITH PROGNOSTIC FACTORS
Data<-MSData(nPatients=100,nMet=150,Prop=0.5)

## USING THE FUNCTION
Result = CVMajorityvotes(Survival=Data$Survival,Censor=Data$Censor,
Prognostic=Data$Prognostic, Mdata=t(Data$Mdata), Reduce=FALSE,
Select=15, Fold=3, Ncv=10)

## GET THE CLASS OF THE OBJECT
class(Result)     # A "cvmv" Class

##  METHOD THAT CAN BE USED FOR THE RESULT
show(Result)
summary(Result)

Constructor for the cvpp class

Description

Constructor for the cvpp class

Usage

cvpp(Results, Ncv, Method, CVtrain, CVtest, Nmet)

Arguments

Value

object of class cvpp

The cvpp Class.

Description

The cvpp Class.

Slots

Results

A dataframe containg the estimated Hazard ratio of the test dataset and the training dataset

Ncv

The number of cross validation performed

Method

The dimesion reduction method used

CVtrain

The training dataset indices matrix used for the cross validation

CVtest

The test dataset indices matrix used for the cross validation

Nmet

The number of metabolite used for the dimesion reduction method used

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

CVPcaPls, SurvPcaClass, SurvPlsClass

Examples

## GENERATE SOME METABOLIC SURVIVAL DATA WITH PROGNOSTIC FACTORS
Data<-MSData(nPatients=100,nMet=150,Prop=0.5)

## USING THE FUNCTION
Result = CVPcaPls(Fold = 4, Survival = Data$Survival,
Mdata = t(Data$Mdata), Censor = Data$Censor, Reduce=TRUE,
Select=19, Prognostic= Data$Prognostic,Ncv=55,DR ="PLS")

## GET THE CLASS OF THE OBJECT
class(Result)     # A "cvpp" Class

##  METHOD THAT CAN BE USED FOR THE RESULT
show(Result)
summary(Result)
plot(Result)

Constructor for the cvsim class

Description

Constructor for the cvsim class

Usage

cvsim(HRpca, HRpls, Nmets, Ncv, Top)

Arguments

Value

object of class cvsim

The cvsim Class.

Description

The cvsim Class.

Slots

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, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

CVPcaPls, SurvPcaClass, SurvPlsClass

Examples

## GENERATE SOME METABOLIC SURVIVAL DATA WITH PROGNOSTIC FACTORS
Data<-MSData(nPatients=100,nMet=150,Prop=0.5)

## FIRST IS THE NETABOLITE BY METABOLITE ANALYSIS
w = 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
Result = CVSimet(w, Top = seq(5, 100, by = 5), Survival=Data$Survival,
 Censor=Data$Censor, Prognostic = Data$Prognostic)

## GET THE CLASS OF THE OBJECT
class(Result)     # A "cvsim" Class

##  METHOD THAT CAN BE USED FOR THE RESULT
show(Result)
summary(Result)
plot(Result, type =2)

Constructor for the fcv class

Description

Constructor for the fcv class

Usage

fcv(Runtime, Fold, Ncv, Nicv, TopK, HRInner, HRTest, Weight)

Arguments

Value

object of class fcv

The fcv Class.

Description

The fcv Class.

Slots

Runtime

A vector of runtime for each iteration measured in seconds.

Fold

Number of folds used.

Ncv

Number of outer cross validations used.

Nicv

Number of inner cross validations used.

TopK

The Top metabolites used

HRInner

A 3-way array in which first, second, and third dimensions correspond to Nicv, 1, and Ncv respectively. This contains estimated HR for low risk group on the out of bag data.

HRTest

A matrix of survival information for the test dataset based on the out of bag data. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

Weight

A matrix with columns equals number of TopK metabolites and rows Ncv. Note that Weights are estimated as colMeans of coefficients matrix return from the inner cross validations.

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

CVLasoelacox, EstimateHR, glmnet, Lasoelacox

Examples

## GENERATE SOME METABOLIC SURVIVAL DATA WITH PROGNOSTIC FACTORS
Data<-MSData(nPatients=100,nMet=150,Prop=0.5)

## USE THE FUNCTION
Eg = Icvlasoel(Data$Survival, Data$Censor, Data$Prognostic,
t(Data$Mdata), Fold = 3,Ncv = 5, Nicv = 7, Alpha = 1,
TopK = colnames(Data$Mdata[,80:100]), Weights = FALSE)

## GET THE CLASS OF THE OBJECT
class(Eg)     # An "fcv" Class

##  METHOD THAT CAN BE USED FOR THIS CLASS
show(Eg)
summary(Eg)
plot(Eg, type =1)

The ms class

Description

The ms class

Constructor for the ms class

Usage

ms(Result, HRRG, Group, Metnames)

ms(Result, HRRG, Group, Metnames)

Arguments

Value

object of class ms

Slots

Result

A list of dataframes of each output object of coxph for the metabolites.

HRRG

A dataframe with estimated metabolite-specific HR for low risk group and 95 percent CI.

Group

A matrix of the classification group a subject belongs to for each of the metabolite analysis. The metabolites are on the rows and the subjects are the columns

Metnames

The names of the metabolites for the analysis

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

MSpecificCoxPh

Examples

## GENERATE SOME METABOLIC SURVIVAL DATA WITH PROGNOSTIC FACTORS
Data<-MSData(nPatients=100,nMet=150,Prop=0.5)

## DO THE METABOLITE BY METABOLITE ANALYSIS
Eg = MSpecificCoxPh(Survival=Data$Survival, Mdata=t(Data$Mdata),
Censor=Data$Censor, Reduce = FALSE, Select = 15,
Prognostic=Data$Prognostic, Quantile = 0.5)

## GET THE CLASS OF THE OBJECT
class(Eg)     # An "ms" Class

##  METHOD THAT CAN BE USED FOR THIS CLASS
show(Eg)
summary(Eg)
plot(Eg)

Constructor for the perm class

Description

Constructor for the perm class

Usage

perm(HRobs, HRperm, nperm, Validation)

Arguments

Value

object of class perm

The perm Class.

Description

The perm Class.

Slots

HRobs

Estimated HR for low risk group on the original data.

HRperm

Estimated HR for low risk group on the permuted data

nperm

Number of permutations carried out.

Validation

The validation scheme that was used.

Note

The first, third and last vertical line on the plot are the lower, median and upper CI of the permuted data estimated HR while the red line is the estimated HR of the original data

Author(s)

Olajumoke Evangelina Owokotomo, olajumoke.owokotomo@uhasselt.be

Ziv Shkedy

See Also

DistHR, EstimateHR, SurvPcaClass, SurvPlsClass, Majorityvotes, Lasoelacox, EstimateHR, Lasoelacox

Examples

## GENERATE SOME METABOLIC SURVIVAL DATA WITH PROGNOSTIC FACTORS
Data<-MSData(nPatients=100,nMet=150,Prop=0.5)

## USING THE FUNCTION
Example <- DistHR(Survival = Data$Survival,Mdata = t(Data$Mdata),
Censor = Data$Censor,Reduce=FALSE,Select=15,Prognostic=Data$Prognostic,
Quantile = 0.5, nperm=10, case=2, Validation=c("L1based"))

## GET THE CLASS OF THE OBJECT
class(Example)     # A "perm" Class

##  METHOD THAT CAN BE USED FOR THIS CLASS
show(Example)
summary(Example)
plot(Example)

Plot method for cvle class

Description

Plot method for cvle class

Usage

## S4 method for signature 'cvle,missing'
plot(x, y, type = 1, ...)

Arguments

Value

Cross Valdiated Results for Lasso and Elastic Net based Predictive Metabolite plots

Plot method for cvmm class

Description

Plot method for cvmm class

Usage

## S4 method for signature 'cvmm,ANY'
plot(x, y, which = 1, ...)

Arguments

Value

Cross Valdiated Metabolic Specific CoxPh plots

Plot method for cvmv class

Description

Plot method for cvmv class

Usage

## S4 method for signature 'cvmv,ANY'
plot(x, y, ...)

Arguments

Value

Cross validation for Majority Votes Based Classification Analysis plots

Plot method for cvpp class

Description

Plot method for cvpp class

Usage

## S4 method for signature 'cvpp,missing'
plot(x, y, ...)

Arguments

Value

Cross Validations for PCA and PLS based plots

Plot method for cvsim class

Description

Plot method for cvsim class

Usage

## S4 method for signature 'cvsim,missing'
plot(x, y, type = 1, ...)

Arguments

Value

Cross validation for sequentially increases metabolites plots

Plot method for fcv class

Description

Plot method for fcv class

Usage

## S4 method for signature 'fcv,missing'
plot(x, y, type = 1, ...)

Arguments

Value

Inner and Outer Cross Validations for Lasso Elastic Net Survival predictive models and Classification plots

Plot method for ms class

Description

Plot method for ms class

Usage

## S4 method for signature 'ms,ANY'
plot(x, y, ...)

Arguments

Value

Metabolite by Metabolite CoxPh plots

Plot method for perm class

Description

Plot method for perm class

Usage

## S4 method for signature 'perm,ANY'
plot(x, y, ...)

Arguments

Value

Null Distribution of the Estimated HR plots

Show method for cvle class

Description

Show method for cvle class

Usage

## S4 method for signature 'cvle'
show(object)

Arguments

Value

Cross Valdiated Results for Lasso and Elastic Net based Predictive Metabolite signature.

Show method for cvmm class

Description

Show method for cvmm class

Usage

## S4 method for signature 'cvmm'
show(object)

Arguments

Value

Cross Valdiated Metabolic Specific CoxPh information

Show method for cvmv class

Description

Show method for cvmv class

Usage

## S4 method for signature 'cvmv'
show(object)

Arguments

Value

Cross validation for Majority Votes Based Classification Analysis information

Show method for cvpp class

Description

Show method for cvpp class

Usage

## S4 method for signature 'cvpp'
show(object)

Arguments

Value

CCross Validations for PCA and PLS based information

Show method for cvsim class

Description

Show method for cvsim class

Usage

## S4 method for signature 'cvsim'
show(object)

Arguments

Value

Cross validation for sequentially increases metabolites information

Show method for fcv class

Description

Show method for fcv class

Usage

## S4 method for signature 'fcv'
show(object)

Arguments

Value

Inner and Outer Cross Validations for Lasso Elastic Net Survival predictive models and Classification information

Show method for ms class

Description

Show method for ms class

Usage

## S4 method for signature 'ms'
show(object)

Arguments

Value

Metabolite by Metabolite CoxPh Model and the number of metabolites used.

Show method for perm class

Description

Show method for perm class

Usage

## S4 method for signature 'perm'
show(object)

Arguments

Value

Null Distribution of the Estimated HR information

Summary method for cvle class

Description

Summary method for cvle class

Usage

## S4 method for signature 'cvle'
summary(object)

Arguments

Value

Cross Valdiated Results for Lasso and Elastic Net based Predictive Metabolite signature summary information

Summary method for cvmm class

Description

Summary method for cvmm class

Usage

## S4 method for signature 'cvmm'
summary(object, which = 1)

Arguments

Value

Cross Valdiated Metabolic Specific CoxPh summary

Summary method for cvmv class

Description

Summary method for cvmv class

Usage

## S4 method for signature 'cvmv'
summary(object)

Arguments

Value

Cross validation for Majority Votes Based Classification Analysis summary

Summary method for cvpp class

Description

Summary method for cvpp class

Usage

## S4 method for signature 'cvpp'
summary(object)

Arguments

Value

Cross Validations for PCA and PLS based summary

Summary method for cvsim class

Description

Summary method for cvsim class

Usage

## S4 method for signature 'cvsim'
summary(object)

Arguments

Value

Cross validation for sequentially increases metabolites summary

Summary method for fcv class

Description

Summary method for fcv class

Usage

## S4 method for signature 'fcv'
summary(object)

Arguments

Value

Inner and Outer Cross Validations for Lasso Elastic Net Survival predictive models and Classification summary

Summary method for ms class

Description

Summary method for ms class

Usage

## S4 method for signature 'ms'
summary(object)

Arguments

Value

Metabolite by Metabolite CoxPh summary information

Summary method for perm class

Description

Summary method for perm class

Usage

## S4 method for signature 'perm'
summary(object)

Arguments

Value

Null Distribution of the Estimated HR summary