Biomarker Validation for Microbiome-Based Survival Classification and Prediction
This function will fit the full and reduced models and calculate LRT r...
Cross Validations for Lasso Elastic Net Survival predictive models and...
The cvle Class.
Cross validation for majority votes
The cvmm Class.
Cross validation for the Taxon specific analysis
The cvmv Class.
Cross Validations for PCA and PLS based methods
The cvpp Class.
The cvsit Class.
Cross validation for sequentially increases taxa
Null Distribution of the Estimated HR
Classification, Survival Estimation and Visualization
This function is used for the first step of filtering which removes OT...
This function convert OTU matrix to RA matrix.
Wapper function for glmnet
Classifiction for Majority Votes
Frequency of Selected Taxa from the LASSO, Elastic-net Cross-Validatio...
The ms Class.
Taxon by taxon Cox proportional analysis
The perm Class.
Quantile sensitivity analysis
This function is used for the second step of filtering which removes O...
Sequential Increase in Taxa for the PCA or PLS classifier
This function gives indices such as Observed richness, Shannon index, ...
Survival PCA and Classification for microbiome data
Survival PLS and Classification for microbiome data
This function finds out the taxon has the smallest p-value, then calcu...
This function returns a matrix with rows are Micros and 9 columns cont...
An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups. Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. Several methods were implemented to estimate the microbiome risk score such as the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, Elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, supervised principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9>, and supervised partial least squares analysis by Inge S. Helland <https://www.jstor.org/stable/4616159>. 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 microbiome 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.
Useful links