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MicrobiomeSurv

R package : A biomarker validation approach for predicting time to event using microbiome data, this package develope biomarker detection for microbiome data. It contains a set of functions and cross validation methods to validate and select biomarkers when the outcome of interest is time to event. The package can handle prognostic factors and mainly microbiome matrix as input, the package can served as biomarker validation tool.

Installation

You can install the development version of MicrobiomeSurv from GitHub with:

# install.packages("devtools")
devtools::install_github("N-T-Huyen/MicrobiomeSurv")

Why use the package

• It can be used with any form of high dimensional/omics data such as: Microbiome data, Metabolic data, Gene expression matrix.
• It develops any level of the ecosystem from the high dimensional data to be used for other purpose.
• It also employs data reduction techniques such as PCA, PLS, Lasso and elastic net, and majority votes.
• It classifies subjects into low and high risk group based on the microbiome scoro.
• It incorporates the use of subject prognostic information for the to enhance the biomarker for classification.
• It gives information about the time to develop a desease of subjects depending on the classification.

A quick Demostration to solve a problem

"Problem of interest"

"Given a set of subjects with known risks cores and prognostic features how can we use this information to obtain their risk of develop T1D and what group does each respective subject belongs to?"

##  Loading the package
library("MicrobiomeSurv")

## Dataset
Dataset and the full codes example is published on github 

##  This function does classification, survival estimation and visualization
est_HR_fam_shan_w3 = EstimateHR(Risk.Scores = lasso_fam_shan_w3$Risk.Scores,
                                Data.Survival = lasso_fam_shan_w3$Data.Survival,
                                Prognostic = prog_fam_shan_w3, Plots = TRUE,
                                Mean = TRUE)

## Survival information
est_HR_fam_shan_w3$SurvResult


## Group information
est_HR_fam_shan_w3$Riskgroup

Functions in the package

Category	Functions	Description
Basic	FirstFilter	Remove taxa with all zeros count.
	ZerosPerGroup	Obtain number and proportion of zeros per group.
	SecondFilter	Filter taxa using a pre-specified threshold.
	GetRA	Convert OTU count to relative abundance.
	SummaryData	Obtain microbiome measurements at higher level such as family, order, etc.
	CoxPHUni	Summary statistics for all taxa using univariate CoxPh model. BH method is used for multiplicity adjustment.
	Top1Uni	Do analysis for the taxon that has the smallest adjusted p-value from the CoxPh model.
	MSpecificCoxPh	Feature specific analysis.
	SurvPcaClass	Classifier based on first PCA.
	SurvPlsClass	Classifier based on first PLS.
	Majorityvotes	Classifiction for Majority Votes.
	Lasoelascox	Wrapper function for glmnet.
	MSData	Generate Artificial Microbiome Survival Data.
	EstimateHR	Classify subjects into low or high risk groups. Visualize survival fit along with HR estimates.
Advance	CVLasoelascox	Cross-Validations for Lasso Elastic Net predictive models and Classification.
	CVSITaxa	Cross-Validation for Top $K_{1}, \ldots, K_{n}$ taxa.
	CVPcaPls	Cross-Validations for PCA and PLS based methods.
	CVMajorityvotes	Cross-Validation for majority votes.
	MiFreq	Frequency of selected taxa from Cross-Validation.
	QuantileAnalysis	Sensitivity of the quantile used for classification.
	DistHR	Null distribution of the estimated HR.
	SITaxa	Sequentially increase the number of top $K$ taxa.