SurvPcaClass {MicrobiomeSurv}

Survival PCA and Classification for microbiome data

Description

The function performs principal component analysis (PCA) on microbiome matrix and fit Cox proportional hazard model with covariates using also the first PCA as covariates.

Usage

SurvPcaClass(
  Survival,
  Micro.mat,
  Censor,
  Reduce = TRUE,
  Select = 5,
  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 microbiome and number of columns should be equal to number of patients.

Censor

A vector of censoring indicator

Reduce

A boolean paramier 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 microbiome (default is 15) 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 paramier 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 can handle single and multiple microbiome. For larger microbiome matrix, this function will reduce largermicrobiome matrix to smaller version using supervised pca approach and this is by default done and can be control by using the argument Reduce. Other prognostic factors can be included to the model.

Value

A object of class SurvPca 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 microbiome matrix or the full matrix

KMplot

The Kaplan-Meier survival plot of the riskgroup

SurvBPlot

The distribution of the survival in the riskgroup

Riskpca

The plot of Risk scores vs first PCA

Author(s)

Thi Huyen Nguyen, [e-mail]

Olajumoke Evangelina Owokotomo, [e-mail]

Ziv Shkedy

See Also

coxph, EstimateHR, princomp, SurvPlsClass

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the function
SPCA_fam_shan_w3 = SurvPcaClass(Survival = surv_fam_shan_w3$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
SPCA_fam_shan_w3$SurvFit

# Getting the riskscores
SPCA_fam_shan_w3$Riskscores

# Getting the riskgroup
SPCA_fam_shan_w3$Riskgroup

# Obtaining the first principal component scores
SPCA_fam_shan_w3$pc1