Lasoelascox {MicrobiomeSurv}

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 taxa in the presence or absence of prognostic factors.

Usage

Lasoelascox(
  Survival,
  Censor,
  Micro.mat,
  Prognostic,
  Plots = FALSE,
  Standardize = TRUE,
  Alpha = 1,
  Fold = 4,
  nlambda = 100,
  Mean = TRUE,
  Quantile = 0.5
)

Arguments

Survival

A vector of survival time with length equals to number of subjects

Censor

A vector of censoring indicator

Micro.mat

A large or small microbiome matrix. A matrix with microbiome profiles where the number of rows is equal to the number of taxa and number of columns is equal to number of patients.

Prognostic

A dataframe containing possible prognostic(s) factor and/or treatment effect to be used in the model.

Plots

A boolean parameter indicating if plots should be shown. Default is FALSE. If TRUE, the first plot is the partial likelihood deviance against the logarithmn of each lambda while the second is the coefficients versus the lambdas

Standardize

A Logical flag for the standardization of the microbiome matrix, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE.

Alpha

The mixing parameter for glmnet (see glmnet). The range is 0<= Alpha <= 1. The Default is 1

Fold

number of folds to be used for the cross validation. Its value ranges between 3 and the number of subjects in the dataset

nlambda

The number of lambda values - default is 100 as in glmnet.

Mean

The cut off value for the classifier, default is the mean cutoff

Quantile

If user want to use quantile as cutoff point. They need to specify Mean = FALSE and a quantile that they want to use. The default is the median cutoff

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 available 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 selceted taxa will thus be used in the survival analysis and in calculation of the risk scores.

Value

A object is returned with the following values

Coefficients.NonZero

The coefficients of the selected taxa

Selected.Mi

The selected taxa

n

The number of selected taxa

Risk.scores

The risk scores of the subjects

Risk.group

The risk classification of the subjects based on the specified cutoff point

SurvFit

The cox analysis of the riskgroup based on the selected taxa and the prognostic factors

Author(s)

Thi Huyen Nguyen, [e-mail]

Olajumoke Evangelina Owokotomo, [e-mail]

Ziv Shkedy

See Also

coxph

coxph, EstimateHR, glmnet,

Examples

# Prepare data
data(Week3_response)
Week3_response = data.frame(Week3_response)
surv_fam_shan_w3 = data.frame(cbind(as.numeric(Week3_response$T1Dweek),
as.numeric(Week3_response$T1D)))
colnames(surv_fam_shan_w3) = c("Survival", "Censor")
prog_fam_shan_w3 = data.frame(factor(Week3_response$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the function
lasso_fam_shan_w3 = Lasoelascox(Survival = surv_fam_shan_w3$Survival,
                                Censor = surv_fam_shan_w3$Censor,
                                Micro.mat = fam_shan_trim_w3,
                                Prognostic = prog_fam_shan_w3,
                                Plots = TRUE,
                                Standardize = TRUE,
                                Alpha = 1,
                                Fold = 4,
                                nlambda = 100,
                                Mean = TRUE)

# View the selected taxa
lasso_fam_shan_w3$Selected.mi

# Number of selected taxa
lasso_fam_shan_w3$n

# View the classification group of each subject
lasso_fam_shan_w3$Risk.Group

# View the survival analysis result
lasso_fam_shan_w3$SurvFit