A Biomarker Validation Approach for Classification and Predicting Survival Using Metabolomics Signature
Cross Validations for Lasso Elastic Net Survival predictive models and...
The cvle Class.
Constructor for the cvle class
Cross validation for majority votes
Cross validation for the Metabolite specific analysis
The cvmm Class.
Constructor for the cvmm class
The cvmv Class.
Constructor for the cvmv class
Cross Validations for PCA and PLS based methods
The cvpp Class.
Constructor for the cvpp class
The cvsim Class.
Constructor for the cvsim class
Cross validation for sequentially increases metabolites
Null Distribution of the Estimated HR
Classification, Survival Estimation and Visualization
The fcv Class.
Constructor for the fcv class
Inner and Outer Cross Validations for Lasso Elastic Net Survival predi...
Wapper function for glmnet
Classifiction for Majority Votes
MetabolicSurv: A biomarker validation approach for predicting survival...
Frequency of Selected Metabolites from the LASSO, Elastic-net Cross-Va...
The ms class
Generate Artificial Metabolic Survival Data
Metabolite by metabolite Cox proportional analysis
The perm Class.
Constructor for the perm class
Plot method for cvle class
Plot method for cvmm class
Plot method for cvmv class
Plot method for cvpp class
Plot method for cvsim class
Plot method for fcv class
Plot method for ms class
Plot method for perm class
Show method for cvle class
Show method for cvmm class
Show method for cvmv class
Show method for cvpp class
Show method for cvsim class
Show method for fcv class
Show method for ms class
Show method for perm class
Sequential Increase in Metabolites for the PCA or PLS classifier
Summary method for cvle class
Summary method for cvmm class
Summary method for cvmv class
Summary method for cvpp class
Summary method for cvsim class
Summary method for fcv class
Summary method for ms class
Summary method for perm class
Survival PCA and Classification for metabolic data
Survival PLS and Classification for metabolic data
An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. Several methods were implemented to reduce the metabolomics matrix such as the principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9> , the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, the elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>. Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.
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