Type: Package
Title: Biomarker Validation for Microbiome-Based Survival Classification and Prediction
Version: 0.1.0
Maintainer: Thi Huyen Nguyen <thihuyen.nguyen@uhasselt.be>
Description: 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.
URL: https://github.com/N-T-Huyen/MicrobiomeSurv
BugReports: https://github.com/N-T-Huyen/MicrobiomeSurv/issues/new
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Imports: graphics, stats, ggplot2, survival, survminer, glmnet, methods, superpc, lmtest, gplots, tidyr, dplyr, microbiome, pls, grDevices
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Depends: R (≥ 2.10)
NeedsCompilation: no
Packaged: 2023-10-11 19:10:24 UTC; lucp11243
Author: Thi Huyen Nguyen [aut, cre], Olajumoke Evangelina Owokotomo [aut], Ziv Shkedy [aut]
Repository: CRAN
Date/Publication: 2023-10-12 06:20:02 UTC

Cross Validations for Lasso Elastic Net Survival predictive models and Classification

Description

The function does cross validation for Lasso, Elastic net and Ridge regressions models before the survial analysis and classification. The survival analysis is based on the selected taxa in the presence or absence of prognostic factors.

Usage

CVLasoelascox(
  Survival,
  Censor,
  Micro.mat,
  Prognostic,
  Standardize = TRUE,
  Alpha = 1,
  Fold = 4,
  Ncv = 10,
  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 profile 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.

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.

Ncv

Number of validations to be carried out. The default is 10.

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

Details

The function performs the cross validations for Lasso, Elastic net and Ridge regressions models for Cox proportional hazard model. Taxa are selected at each iteration and then use for the classifier. Which implies that predictive taxa is varied from one cross validation to the other depending on selection. The underline idea is to investigate the Hazard Ratio for the train and test data based on the optimal lambda selected for the non-zero shrinkage coefficients, the nonzero selected taxa will thus be used in the survival analysis and in calculation of the risk scores for each sets of data.

Value

A object of class cvle is returned with the following values

Coef.mat

A matrix of coefficients with rows equals to number of cross validations and columns equals to number of taxa.

lambda

A vector of estimated optimum lambda for each iterations.

n

A vector of the number of selected taxa.

HRTrain

A matrix of survival information for the training dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

HRTest

A matrix of survival information for the test dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

pld

A vector of partial likelihood deviance at each cross validations.

Mi.mat

A matrix with 0 and 1. Number of rows equals to number of iterations and number of columns equals to number of 1 taxon indicates that the particular taxon was selected or had nonzero coefficient and otherwise it is zero.

Micro.mat

The Microbiome data matrix that was used for the analysis either same as Mdata or a reduced version.

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

coxph, EstimateHR, glmnet, Lasoelascox

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
CV_lasso_fam_shan_w3 = CVLasoelascox(Survival = surv_fam_shan_w3$Survival,
                                     Censor = surv_fam_shan_w3$Censor,
                                     Micro.mat = fam_shan_trim_w3,
                                     Prognostic = prog_fam_shan_w3,
                                     Standardize = TRUE,
                                     Alpha = 1,
                                     Fold = 4,
                                     Ncv = 10,
                                     nlambda = 100)

# Number of selected taxa per CV
CV_lasso_fam_shan_w3@n

# Get the matrix of coefficients
CV_lasso_fam_shan_w3@Coef.mat

# Survival information of the train dataset
CV_lasso_fam_shan_w3@HRTrain

# Survival information of the test dataset
CV_lasso_fam_shan_w3@HRTest

Cross validation for the Taxon specific analysis

Description

The function performs cross validation for each taxon depending the number of fold which guides the division into the train and testing dataset. The classifier is then obtained on the training dataset to be validated on the test dataset.

Usage

CVMSpecificCoxPh(
  Fold = 3,
  Survival,
  Micro.mat,
  Censor,
  Reduce = TRUE,
  Select = 5,
  Prognostic = NULL,
  Mean = TRUE,
  Quantile = 0.5,
  Ncv = 100
)

Arguments

Fold

Number of times in which the dataset is divided. Default is 3 which implies dataset will be divided into three groups and 2/3 of the dataset will be the train datset and 1/3 will be to test the results.

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 th argument Reduce=TRUE.

Prognostic

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

Mean

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

Quantile

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

Ncv

The Number of cross validation loop. Default is 100.

Details

This function performs the cross validation for taxon by taxon analysis. The data will firstly be divided into data train dataset and test datset. Furthermore, a taxon-specific model is fitted on train data and a classifier is built. In addition, the classifier is then evaluated on test dataset for each particular taxon. The Process is repeated for all the full or reduced taxa to obtaind the HR statistics of the low risk group. The following steps depends on the number of cross validation specified.

Value

A object of class cvmm is returned with the following values.

HRTrain

The Train dataset HR statistics for each taxon by the number of CV.

HRTest

The Test dataset HR statistics for each taxon by the number of CV.

train

The selected subjects for each CV in the train dataset.

test

The selected subjects for each CV in the test dataset.

n.mi

The number of taxa used in the analysis.

Ncv

The number of cross validation performed.

Rdata

The Microbiome data matrix that was used for the analysis either same as Micro.mat or a reduced version.

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

coxph, EstimateHR, MSpecificCoxPh,

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the function
CVCox_taxon_fam_shan_w3 = CVMSpecificCoxPh(Fold=3,
                                           Survival = surv_fam_shan_w3$Survival,
                                           Micro.mat = fam_shan_trim_w3,
                                           Censor = surv_fam_shan_w3$Censor,
                                           Reduce=TRUE,
                                           Select=5,
                                           Prognostic=prog_fam_shan_w3,
                                           Mean = TRUE,
                                           Ncv=10)

# Get the class of the object
class(CVCox_taxon_fam_shan_w3)     # An "cvmm" Class

# Method that can be used for the result
show(CVCox_taxon_fam_shan_w3)
summary(CVCox_taxon_fam_shan_w3)
plot(CVCox_taxon_fam_shan_w3)

Cross validation for majority votes

Description

This function does cross validation for the Majority votes based classification which is a cross validated approach to Majorityvotes.

Usage

CVMajorityvotes(
  Survival,
  Censor,
  Prognostic = NULL,
  Micro.mat,
  Reduce = TRUE,
  Select = 5,
  Fold = 3,
  Ncv = 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.

Prognostic

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

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.

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.

Select

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

Fold

Number of times in which the dataset is divided. Default is 3 which implies dataset will be divided into three groups and 2/3 of the dataset will be the train datset and 1/3 will be to train the results.

Ncv

The Number of cross validation loop. Default is 100.

Mean

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

Quantile

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

Value

A object of class cvmv is returned with the following values

HRTrain

A matrix of survival information for the training dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

HRTest

A matrix of survival information for the test dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

Ncv

The number of cross validation used.

Micro.mat

The microbiome data matrix that was used for the analysis either same as Micro.mat or a reduced version.

Progfact

The names of prognostic factors used.

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

Majorityvotes

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the function
CVMajority_fam_shan_w3 = CVMajorityvotes(Survival = surv_fam_shan_w3$Survival,
                                         Micro.mat = fam_shan_trim_w3,
                                         Censor = surv_fam_shan_w3$Censor,
                                         Reduce=TRUE,
                                         Select=5,
                                         Mean = TRUE,
                                         Prognostic = prog_fam_shan_w3,
                                         Fold=3,
                                         Ncv=10)

# Get the class of the object
class(CVMajority_fam_shan_w3)     # An "cvmv" Class

# Method that can be used for the result
show(CVMajority_fam_shan_w3)
summary(CVMajority_fam_shan_w3)
plot(CVMajority_fam_shan_w3)

Cross Validations for PCA and PLS based methods

Description

This function does cross validation for the analysis performs by SurvPcaClass and SurvPlsClass functions where the dimension reduction methods can either be PCA and PLS.

Usage

CVPcaPls(
  Fold = 3,
  Survival,
  Micro.mat,
  Censor,
  Reduce = TRUE,
  Select = 15,
  Prognostic = NULL,
  Ncv = 5,
  DR = "PCA"
)

Arguments

Fold

Number of times in which the dataset is divided. Default is 3 which implies dataset will be divided into three groups and 2/3 of the dataset will be the train datset and 1/3 will be to test the results.

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.

Ncv

The Number of cross validation loop. Default is 100.

DR

The dimension reduction method. It can be either "PCA" for Principle components analysis or "PLS" for Partial least squares.

Details

This function does cross validation for the analysis using two reduction method. The reduction method can be PCA or PLS. If it is PCA then the SurvPcaClass is internally used for the cross validation and SurvPlsClass otherwise.

Value

A object of class cvpp is returned with the following values

Result

A dataframe containg the estimated Hazard ratio of the test dataset and the training dataset.

Ncv

The number of cross validation performed.

Method

The dimesion reduction method used.

CVtrain

The training dataset indices matrix used for the cross validation.

CVtest

The test dataset indices matrix used for the cross validation.

Select

The number of taxa used for the dimesion reduction method used.

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

SurvPlsClass, 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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the function
CVPls_fam_shan_w3 = CVPcaPls(Fold = 3,
                            Survival = surv_fam_shan_w3$Survival,
                            Micro.mat = fam_shan_trim_w3,
                            Censor = surv_fam_shan_w3$Censor,
                            Reduce=TRUE,
                            Select=5,
                            Prognostic = prog_fam_shan_w3,
                            Ncv=10,
                            DR = "PLS")

# Get the class of the object
class(CVPls_fam_shan_w3)     # An "cvpp" Class

# Method that can be used for the result
show(CVPls_fam_shan_w3)
summary(CVPls_fam_shan_w3)
plot(CVPls_fam_shan_w3)

Cross validation for sequentially increases taxa

Description

This function does cross validation for the taxon by taxon analysis while sequentially increasing the number of taxa as specified.

Usage

CVSITaxa(
  Object,
  Top = seq(5, 100, by = 5),
  Survival,
  Censor,
  Prognostic = NULL
)

Arguments

Object

An object of class cvmm.

Top

The Top k number of taxa to be used.

Survival

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

Censor

A vector of censoring indicator.

Prognostic

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

Details

The function is a cross validation version of the function SITaxa. This function firstly processes the cross validation for the taxon by taxon analysis results, and then sequentially considers top k taxa. The function recompute first PCA or PLS on train data and estimate risk scores on both test and train data only on the microbiome matrix with top k taxa. Patients are then classified as having low or high risk based on the test data where the cutoff used is mean of the risk score. The process is repeated for each top K taxa sets.

Value

A object of class cvsit is returned with the following values

HRpca

A 3-way array in which first, second, and third dimensions correspond to number of taxa, Hazard ratio infromation(Estimated HR, LowerCI and UpperCI), and number of cross validation respectively. This contains the estimated HR on test data and dimension reduction method is PCA.

HRpls

A 3-way array in which first, second, and third dimensions correspond to number of taxa, Hazard ratio infromation(Estimated HR, LowerCI and UpperCI), and number of cross validation respectively. This contains the estimated HR on test data and dimension reduction method is PLS.

Ntaxa

The number of taxa in the reduced matrix.

Ncv

The number of cross validation done.

Top

A sequence of top k taxa considered. Default is Top = seq(5, 100, by=5)

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

MSpecificCoxPh, SITaxa

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Getting the cvmm object
CVCox_taxon_fam_shan_w3 = CVMSpecificCoxPh(Fold=3,
                                           Survival = surv_fam_shan_w3$Survival,
                                           Micro.mat = fam_shan_trim_w3,
                                           Censor = surv_fam_shan_w3$Censor,
                                           Reduce=TRUE,
                                           Select=5,
                                           Prognostic=prog_fam_shan_w3,
                                           Mean = TRUE,
                                           Ncv=10)

# Using the function
 CVSITaxa_fam_shan_w3 = CVSITaxa(Object = CVCox_taxon_fam_shan_w3,
                                 Top=seq(1, 6, by=2),
                                 Survival = surv_fam_shan_w3$Survival,
                                 Censor = surv_fam_shan_w3$Censor,
                                 Prognostic=prog_fam_shan_w3)

# Get the class of the object
class(CVSITaxa_fam_shan_w3)     # An "cvsit" Class

This function will fit the full and reduced models and calculate LRT raw p-value and adjusted p-value based on BH Method

Description

This function will fit the full and reduced models and calculate LRT raw p-value and adjusted p-value based on BH Method

Usage

CoxPHUni(Survival, Censor, Prognostic, Micro.mat, Method = "BH")

Arguments

Survival

The time to event outcome.

Censor

An indicator variable indicate the subject is censored or not.

Prognostic

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

Micro.mat

a microbiome matrix, can be at otu, family or any level of the ecosystem. Rows are taxa, columns are subjectsc.

Method

A multiplicity adjustment Method that user can choose. The default is BH Method.

Value

A relative abundance matrix of OTUs

coef

coefficient of one microbiome (OTU or family, ...)

exp.coef

exponential of the coefficient

p.value.LRT

raw LRT p-value

p.value

adjusted p-value based on chosen Method

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

CoxPHUni

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the funtion
summary_fam_shan_w3 = CoxPHUni(Survival = surv_fam_shan_w3$Survival,
                               Censor = surv_fam_shan_w3$Censor,
                               Prognostic = prog_fam_shan_w3,
                               Micro.mat = fam_shan_trim_w3,
                               Method = "BH")

Null Distribution of the Estimated HR

Description

This function generates the null distribution of the HR by permutation approach either using a large microbiome matrix or a reduced version by supervised pca approach. Several ways of permutation setting can be implemented. That is, the function can be used to generate null distributions for four different validation schemes which are PLS based, PCA based, Majority votes based and Lasso based. Note this function internally calls function SurvPcaClass, SurvPlsClass, Majorityvotes, and Lasoelascox.

Usage

DistHR(
  Survival,
  Censor,
  Micro.mat,
  Prognostic = NULL,
  Mean = TRUE,
  Quantile = 0.5,
  Reduce = FALSE,
  Select = 5,
  nperm = 100,
  case = 2,
  Method = "BH",
  Validation = c("PLSbased", "PCAbased", "L1based", "MVbased")
)

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 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.

Prognostic

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

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.

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.

nperm

Number of permutations to be used and default 100.

case

There are seven different ways on how to call this argument:

  1. Permute survival only.

  2. Permute survival and rows of data frame of the prognostic factors.

  3. Permute survival, rows of data frame of the prognostic factors, columns of microbiome matrix independently.

  4. Permute microbiome matrix only.

Method

A multiplicity adjustment Method that user can choose. The default is BH Method.

Validation

There are four different validation schemes where the null distribution can be estimated. That is c("PLSbased","PCAbased","L1based","MVbased").

Value

A object of class perm is returned with the following values

HRobs

Estimated HR for low risk group on the original data.

HRperm

Estimated HR for low risk group on the permuted data.

nperm

Number of permutations carried out.

Validation

The validation scheme that was used.

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

coxph, EstimateHR, SurvPcaClass, SurvPlsClass, Majorityvotes, Lasoelascox

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the function
DistHR_fam_shan_w3 = DistHR(Survival = surv_fam_shan_w3$Survival,
                            Micro.mat = fam_shan_trim_w3,
                            Censor = surv_fam_shan_w3$Censor,
                            Prognostic=prog_fam_shan_w3,
                            Mean = TRUE,
                            Quantile=0.5,
                            Reduce= FALSE,
                            Select = 5,
                            nperm=100,
                            case=4,
                            Method = "BH",
                            Validation="PCAbased")

# Method that can be used for the result
show(DistHR_fam_shan_w3)
summary(DistHR_fam_shan_w3)
plot(DistHR_fam_shan_w3)

Classification, Survival Estimation and Visualization

Description

The function classifies subjects into Low and High risk groups using the risk scores based on the cut-off point which is the mean of the risk score. Also visualize survival fit along with HR estimates.

Usage

EstimateHR(
  Risk.Scores,
  Data.Survival,
  Prognostic = NULL,
  Plots = FALSE,
  Mean = TRUE,
  Quantile = 0.5
)

Arguments

Risk.Scores

A vector of risk scores with size equals to number of subjects obtained from (Lasoelascox).

Data.Survival

A dataframe in which the first column is the Survival and the second column is the Censoring indicator for each subject.

Prognostic

A dataframe containing possible prognostic(s) factor and/or treatment effect

Plots

A boolean parameter indicating if 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

The risk scores obtained using the taxa is then used to generate the risk group by dividing subjects into low and high risk groups. A Cox model is then fitted with the risk group as covariate in the presence or absence of prognostic factors and or treatment effect. The extent of survival in the risk groups is known

Value

An object of is returned, which is a list with the results of the cox regression and some informative plot concerning survival of the risk group.

SurvResult

The cox proportional regression result

Riskgroup

The riskgroup based on the riskscore and the cut off value and length is equal to number of subjects

KMplot

The Kaplan-Meier survival plot of the riskgroup

SurvBPlot

The distribution of the survival in the riskgroup

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

coxph, Lasoelascox

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Obtaning the risk score and data survival
lasso_fam_shan_w3 = Lasoelascox(Survival = surv_fam_shan_w3$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)

# Using the function
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)

This function is used for the first step of filtering which removes OTUs having all zeros (inactive OTUs). The input is an OTU matrix with rows are OTUs and columns are subjects.

Description

This function is used for the first step of filtering which removes OTUs having all zeros (inactive OTUs). The input is an OTU matrix with rows are OTUs and columns are subjects.

Usage

FirstFilter(Micro.mat)

Arguments

Micro.mat

A large or small microbiome 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.

Value

A smaller microbiome matrix.

Micro.mat.trim

The OTU matrix after removing all inactive OTUs

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

FirstFilter

Examples

# Preparing data for analysis at OTU level
data(Week3_otu)
Week3_otu = data.frame(Week3_otu)
otu_mat_w3 = t(data.matrix(Week3_otu[ , 1:2720]))
colnames(otu_mat_w3) = Week3_otu$SampleID
# Filtering first step
otu_w3 = FirstFilter(Micro.mat = otu_mat_w3)

This function convert OTU matrix to RA matrix.

Description

This function convert OTU matrix to RA matrix.

Usage

GetRA(Micro.mat)

Arguments

Micro.mat

an OTU matrix with OTUs in rows and subjects in columns.

Value

A relative abundance matrix of OTUs

ra

Relative abundance matrixs

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

GetRA

Examples

# Read dataset
data(Week3_otu)
Week3_otu = data.frame(Week3_otu)
otu_mat_w3 = t(data.matrix(Week3_otu[ , 1:2720]))

# Convert absolute abundance to relative abundance
ra_otu_trim_w3 = GetRA(Micro.mat = otu_mat_w3)

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, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

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

Taxon by taxon Cox proportional analysis

Description

The Function fits cox proportional hazard model and does classification for each taxon separately

Usage

MSpecificCoxPh(
  Survival,
  Micro.mat,
  Censor,
  Reduce = FALSE,
  Select = 5,
  Prognostic = NULL,
  Mean = TRUE,
  Quantile = 0.5,
  Method = "BH"
)

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 subjects.

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.

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.

Mean

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

Quantile

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

Method

Multiplicity adjustment methods.

Details

This function fits taxon by taxon Cox proportional hazard model and perform the classification based on a microbiome risk score which has been estimated using a single taxon. Function is useful for majority vote classification method and taxon by taxon analysis and also for top K taxa.

Value

A object of class ms is returned with the following values

Result

The cox proportional regression result for each taxon

HRRG

The hazard ratio statistics (Hazard-ratio, Lower confidence interval and upper confidence interval) of the riskgroup based on the riskscore and the cut off value for each taxon

Group

The classification of the subjects based on each taxon analysis

Mi.names

The names of the taxa for the analysis

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

coxph, EstimateHR

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the function
Cox_taxon_fam_shan_w3 = MSpecificCoxPh(Survival = surv_fam_shan_w3$Survival,
                                      Micro.mat = fam_shan_trim_w3,
                                      Censor = surv_fam_shan_w3$Censor,
                                      Reduce=FALSE,
                                      Select=5,
                                      Prognostic = prog_fam_shan_w3,
                                      Mean = TRUE,
                                      Method = "BH")

# Results
show(Cox_taxon_fam_shan_w3)
summary(Cox_taxon_fam_shan_w3)

Classifiction for Majority Votes

Description

The Function fits cox proportional hazard model and does classification based on the majority votes.

Usage

Majorityvotes(Result, Prognostic, Survival, Censor, J = 1)

Arguments

Result

An object obtained from the taxon specific analysis (MSpecificCoxPh) which is of class "ms"

Prognostic

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

Survival

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

Censor

A vector of censoring indicator

J

The jth set of subjects required for the visualization. The default is J=1 which is the first set of subjects. For visualization, J should be less than the number of subjects divided by 25

Details

The Function fits cox proportional hazard model and does classification based on the majority votes while estimating the Hazard ratio of the low risk group. The function firstly count the number of low risk classification for each subject based on the taxon specific analysis which determines the majority votes. In addition, function visualizes the taxon specific calssification for the subjects. 25 subjects is taken for visualization purpose.

Value

A list is returned with the following values

Model.result

The cox proportional regression result based on the majority vote classification

N

The majority vote for each subject

Classif

The majority vote classification for each subjects

Group

The classification of the subjects based on each taxon analysis

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

MSpecificCoxPh, coxph, EstimateHR

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Running the taxon specific function
Cox_taxon_fam_shan_w3 = MSpecificCoxPh(Survival = surv_fam_shan_w3$Survival,
                                       Micro.mat = fam_shan_trim_w3,
                                       Censor = surv_fam_shan_w3$Censor,
                                       Reduce=FALSE,
                                       Select=5,
                                       Prognostic = prog_fam_shan_w3,
                                       Mean = TRUE,
                                       Method = "BH")

# Using the function
Majority_fam_shan_w3 = Majorityvotes(Result = Cox_taxon_fam_shan_w3,
                                     Prognostic = prog_fam_shan_w3,
                                     Survival = surv_fam_shan_w3$Survival,
                                     Censor = surv_fam_shan_w3$Censor,
                                     J=1)

# The survival analysis for majority vote result
Majority_fam_shan_w3$Model.result

# The majority vote for each subject
Majority_fam_shan_w3$N

# The majority vote classification for each subject
Majority_fam_shan_w3$Classif

# The group for each subject based on the taxon specific analysis
Majority_fam_shan_w3$Group

Frequency of Selected Taxa from the LASSO, Elastic-net Cross-Validation

Description

The function selects the frequency of selection from the shrinkage method (LASSO, Elastic-net) based on cross validation, that is the number of times each taxon occur during the cross-validation process. This function outputs the mostly selected taxa during the LASSO and Elastic-net cross validation. Selected top taxa are ranked based on frequency of selection and also a particular frequency can be selected. In addition, it visualizes the selected top taxa based on the minimum frequency specified.

Usage

MiFreq(Object, TopK = 20, N = 3)

Arguments

Object

An object of class cvle returned from the function CVLasoelascox.

TopK

The number of Top K taxa (5 by default) to be displayed in the frequency of selection graph.

N

The taxa with the specified frequency should be displayed in the frequency of selection graph.

Value

A vector of taxa and their frequency of selection. Also, a graphical representation is displayed.

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

cvmm, coxph, EstimateHR, CVLasoelascox

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Cross-Validation for LASSO and ELASTIC-NET
CV_lasso_fam_shan_w3 = CVLasoelascox(Survival = surv_fam_shan_w3$Survival,
                                     Censor = surv_fam_shan_w3$Censor,
                                     Micro.mat = fam_shan_trim_w3,
                                     Prognostic = prog_fam_shan_w3,
                                     Standardize = TRUE,
                                     Alpha = 1,
                                     Fold = 4,
                                     Ncv = 10,
                                     nlambda = 100)


# Using the function
MiFreq_fam_shan_w3 = MiFreq(Object = CV_lasso_fam_shan_w3, TopK=5, N=3)

Quantile sensitivity analysis

Description

The function performs sensitivity of the cut off quantile for obtaining the risk group obtained under SurvPlsClass, SurvPcaClass or Lasoelascox requires for the survival analysis and classification.

Usage

QuantileAnalysis(
  Survival,
  Micro.mat,
  Censor,
  Reduce = TRUE,
  Select = 5,
  Prognostic = NULL,
  Plots = FALSE,
  DM = c("PLS", "PCA", "SM"),
  Alpha = 1
)

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 graphical represenataion of the analysis should be shown. Default is FALSE and it is only valid for the PCA or PLS dimension method.

DM

The dimension method to be used. PCA implies using the SurvPcaClass, PLS uses SurvPcaClass while SM uses the Lasoelascox which ruses the shrinkage method techniques such as lasso and elastic net.

Alpha

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

Details

This function investigates how each analysis differs from the general median cutoff of 0.5, therefore to see the sensitive nature of the survival result different quantiles ranging from 10th percentile to 90th percentiles were used. The sensitive nature of the quantile is investigated under SurvPlsClass, SurvPcaClass or Lasoelascox while relate to the 3 different Dimension method to select from.

Value

A Dataframe is returned depending on weather a data reduction method should be used or not. The dataframe contains the HR of the low risk group for each percentile.

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

coxph,EstimateHR, SurvPcaClass, SurvPlsClass,Lasoelascox

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the PCA method
QuantileAnalysis_PCA_fam_shan_w3 = QuantileAnalysis(Survival = surv_fam_shan_w3$Survival,
                                                    Micro.mat = fam_shan_trim_w3,
                                                    Censor = surv_fam_shan_w3$Censor,
                                                    Reduce=TRUE,
                                                    Select= 5,
                                                    Prognostic=prog_fam_shan_w3,
                                                    Plots = TRUE,
                                                    DM="PCA",
                                                    Alpha =1)

Sequential Increase in Taxa for the PCA or PLS classifier

Description

The Function fits cox proportional hazard model and does classification by sequentially increasing the taxa using either PCA or PLS based on the topK taxa specified.

Usage

SITaxa(
  TopK = 15,
  Survival,
  Micro.mat,
  Censor,
  Reduce = TRUE,
  Select = 5,
  Prognostic = NULL,
  Plot = FALSE,
  DM = c("PLS", "PCA"),
  ...
)

Arguments

TopK

Top K taxa (5 by default) to be used in the sequential analysis.

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 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.

Plot

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

DM

Dimension reduction method which can either be PLS or PCA.

...

Additinal arguments for plotting and only valid if Plot=TRUE

Details

This function sequentially increase the number of top K taxa to be used in the PCA or PLS methods in order to obtain the risk score. This function internally calls MSpecificCoxPh to rank the taxa based on HR for each taxon. Therefore taxa can be ordered based on increasing order of the HR for low risk group. Thereafter, the function takes few top K (5 is the default) to be used in the sequential analysis.

Value

A list containing a data frame with estimated HR along with 95% CI at each TopK value for the sequential analysis.

Result

The hazard ratio statistics (HR, Lower confidence interval and upper confidence interval) of the lower riskgroup based for each sequential metabolite analysis

TopKplot

A graphical representation of the Result containing the hazard ratio statistics

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

coxph, EstimateHR, MSpecificCoxPh, SurvPcaClass, 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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Using the function
SITaxa_fam_shan_w3 = SITaxa(TopK=5,
                            Survival = surv_fam_shan_w3$Survival,
                            Micro.mat = fam_shan_trim_w3,
                            Censor = surv_fam_shan_w3$Censor,
                            Reduce=TRUE,
                            Select=5,
                            Prognostic=prog_fam_shan_w3,
                            Plot = TRUE,
                            DM="PLS")

# For the HR statistics
SITaxa_fam_shan_w3$Result

# For the graphical output
SITaxa_fam_shan_w3$TopKplot

This function is used for the second step of filtering which removes OTUs based on a threshold.

Description

This function is used for the second step of filtering which removes OTUs based on a threshold.

Usage

SecondFilter(zero.per.group, Micro.mat, threshold = 0.7, week = 0)

Arguments

zero.per.group

a n x 9 matrix. Columns are number of zero in control groups, proportion of zeros in control group, number of subject in control group, number of zero in treated groups, proportion of zeros in treated group, number of subject in treated group, total number of zeros, proportion of zeros in total, number of subject

Micro.mat

OTU matrix (rows are otus, columns are subjects)

threshold

user can choose. For instance, if threshold is 0.7, the function will remove OTUs having at least 70% of zeros in one of two groups

week

A specific time point. To use when having different time points in the dataset.

Value

A smaller microbiome matrix.

Micro.mat.new

an smaller OTU matrix with less OTUs

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

SecondFilter

SecondFilter

Examples

# Read dataset
data(Week3_otu)
Week3_otu = data.frame(Week3_otu)
otu_mat_w3 = t(data.matrix(Week3_otu[ , 1:2720]))

# Import dataset from the result of zero_per_group
data(data_zero_per_group_otu_w3)

# Using the function
otu_trim_w3 = SecondFilter(zero.per.group = data_zero_per_group_otu_w3,
                           Micro.mat = otu_mat_w3, threshold = 0.7, week = 3)

This function gives indices such as Observed richness, Shannon index, Inverse Simpson, ... of higher level such as levelily, order, phylum, ...

Description

This function gives indices such as Observed richness, Shannon index, Inverse Simpson, ... of higher level such as levelily, order, phylum, ...

Usage

SummaryData(Micro.mat, info, measure = "observed")

Arguments

Micro.mat

an OTU matrix with OTUs in rows and subjects in columns.

info

A n x 2 matrix containing a column of OTU's names and a column of the corresponding information of the chosen level.

measure

The indices at chosen level that user wishes to use. It can be observed richness, Shannon index, inverse Simpson, ...

Value

A matrix of the selected measurement of the chosen level.

level.measure

A matrix of measurements at levelily level of patients

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

SummaryData

Examples

# Read dataset
data(Week3_otu)
Week3_otu = data.frame(Week3_otu)
otu_mat_w3 = t(data.matrix(Week3_otu[ , 1:2720]))
data(fam_info_w3)

# USing the function
fam_shan_w3 = SummaryData(Micro.mat = otu_mat_w3, info = fam_info_w3, measure = "shannon")

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, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

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

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, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

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

This function finds out the taxon has the smallest p-value, then calculate risk score of patients based on that taxon. Categorized subjects into high or low risk groups based on the mean of the risk score as a cutoff point Checking whether the two groups are significant difference in the probability to be survival.

Description

This function finds out the taxon has the smallest p-value, then calculate risk score of patients based on that taxon. Categorized subjects into high or low risk groups based on the mean of the risk score as a cutoff point Checking whether the two groups are significant difference in the probability to be survival.

Usage

Top1Uni(Result, Micro.mat, Survival, Censor, Plots = FALSE)

Arguments

Result

A Result statistic of all taxon.

Micro.mat

A large or small microbiome 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.

Survival

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

Censor

A vector of censoring indicator

Plots

A boolean parameter indicating if plots should be shown. Default is FALSE. If TRUE, the first plot is plot of the observed Kaplan-Meier curves per group while the second is boxplot of the two groups.

Value

A list is returned with the following values

name.top1

Taxon having the smallest p-value in the univariate coxPH model

sum.top1

Result statistic of the taxon containing coefficient, exponential of coefficient, raw p.value using LRT, and p.value after using BH adjustment

KMplot.top1

Kaplan-Meier plot

log.rank.top1

Log-rank test

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy Top1Uni

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$Treatment_new))
colnames(prog_fam_shan_w3) = c("Treatment")
data(fam_shan_trim_w3)
names_fam_shan_trim_w3 =
c("Unknown", "Lachnospiraceae", "S24.7", "Lactobacillaceae", "Enterobacteriaceae", "Rikenellaceae")
fam_shan_trim_w3 = data.matrix(fam_shan_trim_w3[ ,2:82])
rownames(fam_shan_trim_w3) = names_fam_shan_trim_w3
# Obtain summary statistics for families
summary_fam_shan_w3 = CoxPHUni(Survival = surv_fam_shan_w3$Survival,
                               Censor = surv_fam_shan_w3$Censor,
                               Prognostic = prog_fam_shan_w3,
                               Micro.mat = fam_shan_trim_w3,
                               Method = "BH")

# Analysis of the taxon having smallest p-value (in the result of using CoxPHUni function)
top1_fam_shan_w3 = Top1Uni(Result = summary_fam_shan_w3,
                           Micro.mat = fam_shan_trim_w3,
                           Survival = surv_fam_shan_w3$Survival,
                           Censor = surv_fam_shan_w3$Censor,
                           Plots = TRUE)

OTU table at week 3.

Description

A dataset containing the count of OTUs.

Usage

data(Week3_otu)

Format

A data frame with 81 rows and 2724 variables, we only use 2720 first variables:

X226097bd7a1661a286a3b62d1c1f0e3a

An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

X927b918da0e608c22445cc50ebf92063

An OTU

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An OTU

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An OTU

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An OTU

X42df8b1ef95af065766469117929e018

An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

X4f3f4e7fe9692e43d7c7ce8cf60b14f1

An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

X7ca8fd2a731ddc94e0f53e940957b111

An OTU

X9084a3bb86621ad27e5343ba1c587295

An OTU

X2ae0b98af5d353de3a06ea5a3cc6c7d0

An OTU

X11d7660e8d9775e470fb6025e5e4de70

An OTU

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An OTU

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An OTU

X6ef03ee9d8d8552891f3d9667da82a66

An OTU

X69eea89a1e9b03cc7527cc7df05d9717

An OTU

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An OTU

X40316fcce7d17a283fa1e80b76fa9816

An OTU

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An OTU

X9079e982f890f1684eaa546cbdb500e5

An OTU

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An OTU

X04971809052481b1866deb12bf5aebad

An OTU

X70fb6f6b18601dfde5a057b694a345c8

An OTU

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An OTU

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An OTU

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An OTU

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An OTU

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An OTU

X7bbd8e9a8b7691aa5817479811901239

An OTU

X949f13d2961f9389d656f3754d6ba49d

An OTU

X4ed52398918c3276815f4fb4e9b52c01

An OTU

X6a28cb440f0a6fc9100e38e1ec45e5e3

An OTU

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An OTU

X0f8604c7929451ac44e747f050aa715c

An OTU

X9c1454b23d7b9e86ad391e0b238ee420

An OTU

X7978facd10d73caf9d38eaabc3734d72

An OTU

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An OTU

X2a9d786d5c070cb6588844aea6209709

An OTU

X471ec038f818367e831b0b858510334c

An OTU

X0ab95c6059ac35c603cc4ee440072cca

An OTU

X28577d6d18c544a64dc300c67756df82

An OTU

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An OTU

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An OTU

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An OTU

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An OTU

X461785173d52cea7498a51d11039a6cb

An OTU

X79bfd0f007a0c18bbab60be5a6000335

An OTU

X34ee5b9130ad1e33d8c9b44334984327

An OTU

X52d3527b15fb72c4a3ec6545c41eb64e

An OTU

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An OTU

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An OTU

b4e11c7bc998c25ab94fb5042f061ac1

An OTU

d43ed378a846746647619025dcb17b10

An OTU

dc1f44c145de143aad36156e3c112062

An OTU

X1a37face0a22c54f87c8710233a42784

An OTU

X21af6c12d3a71822b65d7a667ce73643

An OTU

f5d031dfa39b7478776b36cc2623d12d

An OTU

b248c8237cbfc6a17357b926a1c88225

An OTU

X58de20a5c30157a40e4ed693769f9c38

An OTU

X052040f040eed5aabe28e27b4acd3cbf

An OTU

X815e3202678b49e7f0b351a5be11dd85

An OTU

X7b364f5ef3684f92c4f2631cb4ed17d5

An OTU

fdf62551368b413734444e14a3422162

An OTU

X39a1e33291f171b2f41073ac44025f8c

An OTU

X643336dac981889cfd0b469afa912b87

An OTU

X909722ec171a68c45c56a2c8d2e6efa8

An OTU

f919b82fdad02143e98d7c7095ff37a3

An OTU

a75310b050a0188f84c5db17e82e0745

An OTU

X2fc3a470029f7213361373e8b963844a

An OTU

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An OTU

X63935a753e87c1c008ebbf32e2781db7

An OTU

a0a14712e4cf136df6d0b771d8cfe49b

An OTU

ef25f7c91a40da3f4c5620cc51e0d535

An OTU

X7c4ef49f58cf0699b44fbc4ccede08c8

An OTU

X54d9ceb6a24489bd276b726ac824cc55

An OTU

X23c30557799aa71a03c5667425657571

An OTU

X765442afe5f3b10442181438de3ad428

An OTU

X709dbb529c0d8eab107f03624501db96

An OTU

fff75440a8a18a9c0493d7541503f7cd

An OTU

X929d82246fe6e1d3bc25ee0ba753f996

An OTU

a0d3a31c4341a20aa205c57de6611405

An OTU

X9aa3fd974b7442f74bab23ac3a5178a9

An OTU

X12421c48d6a0746f63ad6bc9b8b9d798

An OTU

X9fc5c72a0a49831d25abbab6d52febe1

An OTU

b75aa4110b6d7c21ad0b616c0d2c78cd

An OTU

c12e2d448713410a1ca26451f8f98476

An OTU

d26287185f1151b3c5363a2371b816e7

An OTU

X43f82b9bdf46fbad8c4cea22763f1519

An OTU

ef34e6696ded41823d3f66c0f73e6b46

An OTU

X13ef39327a9a40ce5f44f8b27db52c7c

An OTU

X6898520892f36d2f9597b1c708e294f0

An OTU

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An OTU

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An OTU

X1ff00aa01fb7d4bb5c6756e6bc8e222d

An OTU

X5beb04dd443a70676c7abcb1fa9ed669

An OTU

X5e4f920bf1ca33bd8e4ef68815365008

An OTU

X55684b5fb20eda4d1af5181250880b34

An OTU

X12e578b9763a29387fce1cabd3a23e11

An OTU

X1b5bc0c0f492ec5adee306aa26ba83d6

An OTU

X238bbd12889818a77baebc684a6a04e0

An OTU

X5efc7802cc6b72d959f635a006d482c5

An OTU

X0e4131e2df0be953b6ddbaa6bd96ab54

An OTU

X86527342512661bc5472ee8db6552c0e

An OTU

X19b04f2babf6af613eed71cdf6527fdc

An OTU

d2e99643ec43827ffe30c7f56e06c4d0

An OTU

X470dc35fdbdfad49fb03c09837960eb3

An OTU

X596af7e0c83b8b132d24f48fa1bf2419

An OTU

X89acee9d02c73e5e2b28d8111b0dc901

An OTU

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An OTU

d99949a035ba7530cb5835b1ea1faabe

An OTU

b27135e8d73b632586ff212edbc1e3f3

An OTU

X1ce151cd15ac444204519f4f8260c503

An OTU

X6eb3f747505b027607654373c88891d2

An OTU

X1631aa5ee4035702cdf7eb97497e345c

An OTU

X4435ef9ba4e2670d855c347e06a6b57d

An OTU

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An OTU

a3de2fae9925edf23be9bf5ee6887024

An OTU

X20c0b9d9104aebe21e5d3583d89d0429

An OTU

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An OTU

X1f4d160ff6c0bb9c34d1c20f783d314e

An OTU

X7a88ef8a99cd1665ef2bd6878d17023d

An OTU

a05317bb54e232983273fa650022bffd

An OTU

X4eae78dc4f76a0b76e888032f2430302

An OTU

aec38a93f51d499c121d6f8ac2ce0451

An OTU

X42d179a562749edf5ce0d1fac96140a0

An OTU

X7e44537094e22909f2e40cec0094c25a

An OTU

X4da7382e35b2b29310798825e4ce8cba

An OTU

X74d7fc3b65abcc9669e228d9e7c4c1e8

An OTU

fcecaab3c88d40802d5b14b57271a88f

An OTU

X1f087d6ca500a9eef7b8096b9dbbe12e

An OTU

X8102da3078854a27778e59b659dc313b

An OTU

d5bc23d5f22f5803ff531326cdcc11d2

An OTU

d55c6efcff87c52c7f0a52b59197d2d2

An OTU

X6f8b091f6346823e6839051b854083af

An OTU

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An OTU

X2effb4dd191a848e976bc6bf0ed88346

An OTU

X8f8a67fce2c9dd9f1c1837511819af1b

An OTU

X8e704a0e1277e37ab801823ee1ee9ea7

An OTU

e765d7f34ca916aef3f5fbe67cce0aeb

An OTU

X005d3193f381b0793f0c928bde66dd21

An OTU

SampleID

ID of the subject

Treatment

Treatment variable

T1Dweek

Time to develop T1D in week

T1D

Censored indicator

Source

https://elifesciences.org/articles/37816

Response datase.

Description

A dataset containing the information of subjects.

Usage

data(Week3_response)

Format

A data frame with 81 rows and 30 variables:

SampleID

ID of the subject

Treatment

Treatment variable

T1Dweek

Time to develop T1D in week

T1D

Censored indicator

Treatment_new

Treatment indicator obtained from treatment variable

Source

https://elifesciences.org/articles/37816

This function returns a matrix with rows are Micros and 9 columns containing number and the proportion of zeros per groups of treatments and in total.

Description

This function returns a matrix with rows are Micros and 9 columns containing number and the proportion of zeros per groups of treatments and in total.

Usage

ZerosPerGroup(
  Micro.mat,
  groups,
  week = 0,
  n.obs = n.obs,
  n.control = n.control,
  n.treated = n.treated,
  n.mi = n.mi,
  plot = FALSE
)

Arguments

Micro.mat

Micro matrix (rows are Micros, columns are subjects)

groups

Treatment groups or groups of any binary variables

week

A specific time point. To use when having different time points in the dataset.

n.obs

Number of patients.

n.control

Number of patients in control group or in the first group.

n.treated

Number of patients in treated group or in the second group.

n.mi

Number of taxa.

plot

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

Value

A matrix with information of number and the proportion of zeros per groups.

zero.per.group

A matrix with rows are Micros and 9 columns containing number and the proportion of zeros per groups of treatments and in total.

plot

Plot percentage of zeros per group

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

ZerosPerGroup

Examples


# Preparing data for analysis at OTU level
data(Week3_otu)
data(Week3_response)
Week3_otu = data.frame(Week3_otu)
otu_mat_w3 = t(data.matrix(Week3_otu[ , 1:2720]))
n_obs = dim(otu_mat_w3)[2]
n_control = table(Week3_response$Treatment_new)[1]
n_treated = table(Week3_response$Treatment_new)[2]
n_otu = dim(otu_mat_w3)[1]
# Calculate zeros per groups
zero_per_group_otu_w3 = ZerosPerGroup(Micro.mat = otu_mat_w3,
                                     groups = Week3_response$Treatment_new,
                                     week = 3,
                                     n.obs = n_obs,
                                     n.control = n_control,
                                     n.treated = n_treated,
                                     n.mi = n_otu,
                                     plot = TRUE)

The cvle Class.

Description

Class of object returned by function CVLasoelascox.

Usage

## S4 method for signature 'cvle'
show(object)

## S4 method for signature 'cvle'
summary(object)

## S4 method for signature 'cvle,missing'
plot(x, y, type = 1, ...)

Arguments

object

A cvle class object

x

A cvle class object

y

missing

type

Plot type. 1 distribution of the HR under training and test set. 2 HR vs number selected taxa.

...

The usual extra arguments to generic functions — see plot, plot.default

Slots

Coef.mat

A matrix of coefficients with rows equals to number of cross validations and columns equals to number of taxa,

lambda

A vector of estimated optimum lambda for each iterations.

n

A vector of the number of selected taxa.

mi.mat

A matrix with 0 and 1. Number of rows equals to number of iterations and number of columns equals to number of taxa. 1 indicates that the particular taxon was selected or had nonzero coefficient and otherwise it is zero.

HRTrain

A matrix of survival information for the training dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

HRTest

A matrix of survival information for the test dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

pld

A vector of partial likelihood deviance at each cross validations.

Micro.mat

The microbiome matrix that was used for the analysis which can either be the full the full data or a reduced supervised PCA version.

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

EstimateHR, glmnet, Lasoelascox

The cvmm Class.

Description

Class of object returned by function CVMSpecificCoxPh.

Usage

## S4 method for signature 'cvmm'
show(object)

## S4 method for signature 'cvmm'
summary(object, which = 1)

## S4 method for signature 'cvmm,ANY'
plot(x, y, which = 1, ...)

Arguments

object

A CVMSpecificCoxPh class object

which

This specify which taxon for which estimated HR information need to be visualized. By default results of the first taxon is used.

x

A CVMSpecificCoxPh class object CVMSpecificCoxPh

y

missing

...

The usual extra arguments to generic functions — see plot, plot.default

Details

plot signature(x = "cvmm"): Plots for CVMSpecificCoxPh class analysis results.

Any parameters of plot.default may be passed on to this particular plot method.

Slots

HRTrain

A 3-way array, The first dimension is the number of taxa, the second dimension is the HR statistics for the low risk group in the train dataset (HR,1/HR LCI, UCI) while the third dimension is the number of cross validation performed.

HRTest

A 3-way array, The first dimension is the number of taxa, the second dimension is the HR statistics for the low risk group in the test dataset (HR,1/HR LCI, UCI) while the third dimension is the number of cross validation performed.

train

The selected subjects for each CV in the train dataset.

test

The selected subjects for each CV in the test dataset.

n.mi

The number of taxa used in the analysis.

Ncv

The number of cross validation performed.

Rdata

The microbiome data matrix that was used for the analysis either same as Micro.mat or a reduced version

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

CVMSpecificCoxPh

The cvmv Class.

Description

Class of object returned by function CVMajorityvotes.

Usage

## S4 method for signature 'cvmv'
show(object)

## S4 method for signature 'cvmv'
summary(object)

## S4 method for signature 'cvmv,ANY'
plot(x, y, ...)

Arguments

object

A cvmv class object

x

A cvmv class object

y

missing

...

The usual extra arguments to generic functions — see plot, plot.default

Slots

HRTrain

A matrix of survival information for the training dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

HRTest

A matrix of survival information for the test dataset. It has three columns representing the estimated HR, the 95% lower confidence interval and the 95% upper confidence interval.

Ncv

The number of cross validation used.

Micro.mat

The microbiome data matrix that was used for the analysis either same as Micro.mat or a reduced version.

Progfact

The names of prognostic factors used.

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

Majorityvotes, CVPcaPls, SurvPcaClass, SurvPlsClass

The cvpp Class.

Description

Class of object returned by function CVPcaPls.

Usage

## S4 method for signature 'cvpp'
show(object)

## S4 method for signature 'cvpp'
summary(object)

## S4 method for signature 'cvpp,missing'
plot(x, y, ...)

Arguments

object

A cvpp class object

x

A cvpp class object

y

missing

...

The usual extra arguments to generic functions — see plot, plot.default

Slots

Results

A dataframe containg the estimated Hazard ratio of the test dataset and the training dataset

Ncv

The number of cross validation performed

Method

The dimesion reduction method used

CVtrain

The training dataset indices matrix used for the cross validation

CVtest

The test dataset indices matrix used for the cross validation

Select

The number of taxa used for the dimesion reduction method used

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

CVPcaPls, SurvPcaClass, SurvPlsClass

The cvsit Class.

Description

Class of object returned by function cvsit.

Usage

## S4 method for signature 'cvsit'
show(object)

## S4 method for signature 'cvsit'
summary(object)

## S4 method for signature 'cvsit,missing'
plot(x, y, type = 1, ...)

Arguments

object

A cvsit class object

x

A cvsit class object

y

missing

type

Plot type. 1 distribution of the HR under test For the Top K taxa using PCA. 2 distribution of the HR under test For the Top K taxa using PLS.

...

The usual extra arguments to generic functions — see plot, plot.default

Slots

HRpca

A 3-way array in which first, second, and third dimensions correspond to number of taxa, Hazard ratio information (Estimated HR, LowerCI and UpperCI), and number of cross validation respectively. This contains the estimated HR on test data and dimension reduction method is PCA.

HRpls

A 3-way array in which first, second, and third dimensions correspond to number of taxa, Hazard ratio information (Estimated HR, LowerCI and UpperCI), and number of cross validation respectively. This contains the estimated HR on test data and dimension reduction method is PLS.

Ntaxa

The number of taxa in the reduced matrix.

Ncv

The number of cross validation done.

Top

A sequence of top k taxa considered. Default is Top=seq(5,100,by=5).

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

CVPcaPls, SurvPcaClass, SurvPlsClass

Zero per treatment groups.

Description

A dataset containing the information of zeros per treatment groups at OTU level.

Usage

data(data_zero_per_group_otu_w3)

Format

A data frame with 2720 rows and 10 variables:

OTU

Name of OTUs

zero.ctrl

Number of zeros in control group

propzero.ctrl

Percentage of zeros in the control group

nCtrl

Number of subjects in the control group

zero.Treated

Number of zeros in treated group

propzero.Treated

Percentage of zeros in the treated group

nTreated

Number of subjects in the treated group

zero.total

Number of zeros in total

propzero.total

Percentage of zeros in total

nTotal

Number of subjects in the experiment

Source

https://github.com/N-T-Huyen

Information at family level.

Description

A dataset containing the information at family level.

Usage

data(fam_info_w3)

Format

A data frame with 2720 rows and 2 variables:

OTUID

ID of OTU

Family

Family name

Source

https://github.com/N-T-Huyen

Dataset at family level.

Description

A dataset containing the Shannon index of 6 families after filtering.

Usage

data(fam_shan_trim_w3)

Format

A data frame with 6 rows and 82 variables:

Rows are family names and columns are names of subjects.

Source

https://github.com/N-T-Huyen

Metadata taxonomy.

Description

A dataset containing the information of all levels in the ecosystem: OTU, order, family, kingdom, ...

Usage

data(metadata_taxonomy)

Format

A data frame with 2720 rows and 3 variables:

OTUID,Taxon,Confidence

OTU ID and information at higher levels

...

Source

https://elifesciences.org/articles/37816

The ms Class.

Description

Class of object returned by function MSpecificCoxPh. plot signature(x = "ms"): Plots for ms class analysis results

Usage

## S4 method for signature 'ms'
show(object)

## S4 method for signature 'ms'
summary(object)

## S4 method for signature 'ms,ANY'
plot(x, y, ...)

Arguments

object

A ms class object

x

A ms class object

y

missing

...

The usual extra arguments to generic functions — see plot, plot.default

Details

Any parameters of plot.default may be passed on to this particular plot method.

show(ms-object)

Slots

Result

A list of dataframes of each output object of coxph for the taxa.

HRRG

A dataframe with estimated taxon-specific HR for low risk group and 95 percent CI.

Group

A matrix of the classification group a subject belongs to for each of the taxon analysis. The taxa are on the rows and the subjects are the columns

Mi.names

The names of the taxon for the analysis

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

MSpecificCoxPh

The perm Class.

Description

Class of object returned by function DistHR.

Usage

## S4 method for signature 'perm'
show(object)

## S4 method for signature 'perm'
summary(object)

## S4 method for signature 'perm,ANY'
plot(x, y, ...)

Arguments

object

A perm class object

x

A perm class object

y

missing

...

The usual extra arguments to generic functions — see plot, plot.default

Slots

HRobs

Estimated HR for low risk group on the original data.

HRperm

Estimated HR for low risk group on the permuted data.

nperm

Number of permutations carried out.

Validation

The validation scheme that was used.

Note

The first, third and last vertical line on the plot are the lower, median and upper CI of the permuted data estimated HR while the red line is the estimated HR of the original data

Author(s)

Thi Huyen Nguyen, thihuyen.nguyen@uhasselt.be

Olajumoke Evangelina Owokotomo, olajumoke.x.owokotomo@gsk.com

Ziv Shkedy

See Also

DistHR, EstimateHR, SurvPcaClass, SurvPlsClass, Majorityvotes, Lasoelascox