SurvPlsClass {MicrobiomeSurv}

Survival PLS and Classification for microbiome data

Description

The function performs partial least squares (PLS) and principal component regression on microbiome matrix and fit Cox proportional hazard model with covariates using the first PLS scores as covariates.

Usage

SurvPlsClass(
  Survival,
  Micro.mat,
  Censor,
  Reduce = TRUE,
  Select = 150,
  Prognostic = NULL,
  Plots = FALSE,
  Mean = TRUE,
  Quantile = 0.5
)

Arguments

Survival

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

Micro.mat

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

Censor

A vector of censoring indicator

Reduce

A boolean parameter indicating if the microbiome profile matrix should be reduced, default is TRUE and larger microbiome profile matrix is reduced by supervised pca approach and first pca is extracted from the reduced matrix to be used in the classifier.

Select

Number of taxa (default is 5) to be selected from supervised PCA. This is valid only if the argument Reduce=TRUE

Prognostic

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

Plots

A boolean parameter indicating if the plots should be shown. Default is FALSE

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 function reduces larger microbiome matrix to smaller version using supervised pca approach. The function performs the PLS on the reduced microbiome 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 mean of the risk scores as default. However, user can choose any quantile. This function can handle single and multiple taxa. Prognostic factors can also be included to enhance classification.

Value

A object is returned with the following values

Survfit

The cox proportional regression result using the first PCA

Riskscores

A vector of risk scores which is equal to the number of patents.

Riskgroup

The classification of the subjects based on the PCA into low or high risk group

pc1

The First PCA scores based on either the reduced Metabolite matrix or the full matrix

KMplot

The Kaplan-Meier survival plot of the riskgroup

SurvBPlot

The distribution of the survival in the riskgroup

Riskpls

The plot of Risk scores vs first PLS

Author(s)

Thi Huyen Nguyen, [e-mail]

Olajumoke Evangelina Owokotomo, [e-mail]

Ziv Shkedy

See Also

coxph, EstimateHR, plsr, SurvPcaClass

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the function
SPLS_fam_shan_w3 = SurvPlsClass(Survival = surv_fam_shan_w3$Survival,
                                Micro.mat = fam_shan_trim_w3,
                                Censor = surv_fam_shan_w3$Censor,
                                Reduce=TRUE,
                                Select=5,
                                Prognostic = prog_fam_shan_w3,
                                Plots = TRUE,
                                Mean = TRUE)

# Getting the survival regression output
SPLS_fam_shan_w3$SurvFit

# Getting the riskscores
SPLS_fam_shan_w3$Riskscores

# Getting the riskgroup
SPLS_fam_shan_w3$Riskgroup

# Obtaining the first principal component scores
SPLS_fam_shan_w3$pc1