README.md

In hk1785/OMiSA: Optimal Microbiome-based Survival Analysis (OMiSA)

R package: OMiSA

Version: 1.5

Date: 2019-6-11

Title: Optimal Microbiome-based Survival Analysis (OMiSA)

Author: Hyunwook Koh, Alexandra E. Livanos, Martin J. Blaser, Huilin Li

Maintainer: Hyunwook Koh [e-mail]

Description: This software package provides facilities for the non-parametric methods 1) Optimal Microbiome-based Survival Analysis (OMiSA), 2) Optimal Microbiome-based Survival Analysis using Linear and Non-linear bases of OTUs (OMiSALN) and 3) Optimal Microbiome Regression-based Kernel Association Test for Survival traits (OMiRKAT-S). OMiSA, OMiSALN and OMiRKAT-S test the association between a microbial group (e.g., community, taxon) composition and a survival (time-to-event) response on human health/disease with or without covariate adjustments (e.g., age, sex).

NeedsCompilation: No

Depends: R(>= 3.2.3)

Imports: BiasedUrn, CompQuadForm, dirmult, ecodist, GUniFrac, phyloseq, robCompositions, robustbase, survival

License: GPL-2

Reference

• Koh H, Livanos AE, Blaser MJ, Li H. (2018) A highly adaptive microbiome-based association test for survival traits. BMC Genomics 19:210.

• DOI: https://doi.org/10.1186/s12864-018-4599-8

Troubleshooting Tips

If you have any problems for using this R package, please report in Issues (https://github.com/hk1785/OMiSA/issues) or email Hyunwook Koh ([e-mail]).

• Tip 1. Depending on your pre-installed R libraries, this R package can require you to install additional R packages such as "gh", "usethis", "cli", etc using the command: install.packages("package_name").
• Tip 2. Please make sure if you have the most recent package version.

Installation

library(devtools)
install_github("hk1785/OMiSA", force=T)

Data format

library(phyloseq)

• URL: https://joey711.github.io/phyloseq/

Prerequites

BiasedUrn

install.packages("BiasedUrn")

CompQuadForm

install.packages("CompQuadForm")

devtools

install.packages("devtools")

dirmult

install.packages("dirmult")

ecodist

install.packages("ecodist")

GUniFrac

install.packages("GUniFrac")

phyloseq

source("https://bioconductor.org/biocLite.R")
biocLite("phyloseq")

robCompositions

install.packages("robCompositions")

robustbase

install.packages("robustbase")

survival

install.packages("survival")

Installation

library(devtools)
install_github("hk1785/OMiSA", force=T)

Data format

library(phyloseq)
URL: https://joey711.github.io/phyloseq/

Manual

This R package includes two core functions, OMiSA, OMiSALN and OMiRKAT. Please find the details below.

:mag: OMiSA

Description

OMiSA is a non-parametric method which tests the association between a microbial group (e.g., community, taxon) composition and a survival (time-to-event) response on human health or disease with or without covariate adjustments (e.g., age, sex).

Usage

OMiSA(obstime, delta, X, total.reads = NULL, tree, cov = NULL, pow = c(1/4,1/3,1/2,1), g.unif.alpha = c(0.5), n.perm = 5000)

Arguments

• obstime - A numeric vector for the observed times.
• delta - A numeric vector for the event/censoring indicators (1: event, 0: censoring).
• X - A matrix for the OTU table (1. Elements are counts. 2. Rows are samples and columns are OTUs. 3. Monotone/singletone OTUs must be removed.).
• total.reads - A numeric vector for the total reads per sample in the entire community. If you survey the entire community, you do not need to specify this. If you test a microbial taxon, you need to specify this (see the examples below). Default is NULL for the entire community.
• tree - A rooted phylogenetic tree.
• cov - A data.frame (or vector) for covariate adjustment(s) (Rows are samples and columns are covariate variables).
• pow - A set of the candidate gamma values. Default is c(1/4, 1/3, 1/2, 1).
• g.unif.alpha - A set of the candidate alpha parameter value(s) for the generalized UniFrac distance (e.g., c(0.25, 0.5)). Default is c(0.5).
• n.perm - The number of permutations. Default is 5000.

Values

$pvs.misaln - The estimated p-values for individual MiSALN tests

$pvs.mirkats - The estimated p-values for individual MiRKAT-S tests

$p.omisaln - The estimated p-value for OMiSALN

$p.omirkats - The estimated p-value for OMiRKAT-S

$p.omisa - The estimated p-value for OMiSA

References

• Koh H, Livanos AE, Blaser MJ, Li H. (2018) A highly adaptive microbiome-based association test for survival traits. BMC Genomics 19, 210
• Plantinga A, Zhan X, Zhao N, Chen J, Jenq RR, Wu MC. (2017) MiRKAT-S: a community-level test of association between the microbiota and survival times. Microbiome 5, 17

Example

Import requisite R packages

library(dirmult) 
library(phyloseq) 
library(robustbase)
library(robCompositions) 
library(BiasedUrn)
library(CompQuadForm)
library(GUniFrac) 
library(ecodist) 
library(survival)
library(OMiSA)

Import example microbiome data

data(MiSurv.Data) 

otu.tab <- otu_table(MiSurv.Data)
tax.tab <- tax_table(MiSurv.Data)
tree <- phy_tree(MiSurv.Data)
obstime <- as.numeric(unlist(sample_data(MiSurv.Data)[,1]))
delta <- as.numeric(unlist(sample_data(MiSurv.Data)[,2]))
x1 <- as.numeric(unlist(sample_data(MiSurv.Data)[,3]))
x2 <- as.numeric(unlist(sample_data(MiSurv.Data)[,4]))
covs <- as.data.frame(cbind(x1, x2))
covs[,2] <- as.factor(covs[,2])

Example 1. To test the entire community (e.g., kingdom)

set.seed(100)
OMiSA(obstime, delta, otu.tab, total.reads=NULL, tree, cov=covs)

Example 2. To test the higher-level taxon, p__Firmicutes

total.reads <- rowSums(otu.tab)
ind.Firmicutes <- which(tax.tab[,2] == "p__Firmicutes")
otu.tab.Firmicutes <- otu.tab[,ind.Firmicutes]
tree.Firmicutes <- prune_taxa(colnames(otu.tab.Firmicutes), tree)

set.seed(100)
OMiSA(obstime, delta, otu.tab.Firmicutes, total.reads=total.reads, tree=tree.Firmicutes, cov=covs)

Example 3. To test the higher-level taxon, p__Bacteroidetes

total.reads <- rowSums(otu.tab)
ind.Bacteroidetes <- which(tax.tab[,2] == "p__Bacteroidetes")
otu.tab.Bacteroidetes <- otu.tab[,ind.Bacteroidetes]
tree.Bacteroidetes <- prune_taxa(colnames(otu.tab.Bacteroidetes), tree)

set.seed(100)
OMiSA(obstime, delta, otu.tab.Bacteroidetes, total.reads=total.reads, tree=tree.Bacteroidetes, cov=covs)

:mag: OMiSALN

Description

OMiSALN is a non-parametric method which tests the association between a microbial group (e.g., community, taxon) composition and a survival (time-to-event) response on human health or disease with or without covariate adjustments (e.g., age, sex).

Usage

OMiSALN(obstime, delta, X, total.reads = NULL, cov = NULL, pow = c(1/4, 1/3, 1/2, 1), n.perm = 5000)

Arguments

• obstime - A numeric vector for the observed times.
• delta - A numeric vector for the event/censoring indicators (1: event, 0: censoring).
• X - A matrix for the OTU table (1. Elements are counts. 2. Rows are samples and columns are OTUs. 3. Monotone/singletone OTUs must be removed.).
• total.reads - A numeric vector for the total reads per sample in the entire community. If you survey the entire community, you do not need to specify this. If you test a microbial taxon, you need to specify this (see the examples below). Default is NULL for the entire community.
• tree - A rooted phylogenetic tree.
• cov - A data.frame (or vector) for covariate adjustment(s) (Rows are samples and columns are covariate variables).
• pow - A set of the candidate gamma values. Default is c(1/4, 1/3, 1/2, 1).
• n.perm - The number of permutations. Default is 5000.

Values

$pvs.misaln - The estimated p-values for individual MiSALN tests

$p.omisaln - The estimated p-value for OMiSALN

References

• Koh H, Livanos AE, Blaser MJ, Li H. (2018) A highly adaptive microbiome-based association test for survival traits. BMC Genomics 19, 210

Example

Import requisite R packages

library(dirmult) 
library(phyloseq) 
library(robustbase)
library(robCompositions) 
library(BiasedUrn)
library(CompQuadForm)
library(GUniFrac) 
library(ecodist) 
library(survival)
library(OMiSA)

Import example microbiome data

data(MiSurv.Data) 

otu.tab <- otu_table(MiSurv.Data)
tax.tab <- tax_table(MiSurv.Data)
tree <- phy_tree(MiSurv.Data)
obstime <- as.numeric(unlist(sample_data(MiSurv.Data)[,1]))
delta <- as.numeric(unlist(sample_data(MiSurv.Data)[,2]))
x1 <- as.numeric(unlist(sample_data(MiSurv.Data)[,3]))
x2 <- as.numeric(unlist(sample_data(MiSurv.Data)[,4]))
covs <- as.data.frame(cbind(x1, x2))
covs[,2] <- as.factor(covs[,2])

Example 1. To test the entire community (e.g., kingdom)

set.seed(100)
OMiSALN(obstime, delta, otu.tab, total.reads=NULL, cov=covs)

Example 2. To test the higher-level taxon, p__Firmicutes

total.reads <- rowSums(otu.tab)
ind.Firmicutes <- which(tax.tab[,2] == "p__Firmicutes")
otu.tab.Firmicutes <- otu.tab[,ind.Firmicutes]
tree.Firmicutes <- prune_taxa(colnames(otu.tab.Firmicutes), tree)

set.seed(100)
OMiSALN(obstime, delta, otu.tab.Firmicutes, total.reads=total.reads, cov=covs)

Example 3. To test the higher-level taxon, p__Bacteroidetes

total.reads <- rowSums(otu.tab)
ind.Bacteroidetes <- which(tax.tab[,2] == "p__Bacteroidetes")
otu.tab.Bacteroidetes <- otu.tab[,ind.Bacteroidetes]
tree.Bacteroidetes <- prune_taxa(colnames(otu.tab.Bacteroidetes), tree)

set.seed(100)
OMiSALN(obstime, delta, otu.tab.Bacteroidetes, total.reads=total.reads, cov=covs)

:mag: OMiRKATS

Description

OMiRKATS is a non-parametric method which tests the association between a microbial group (e.g., community, taxon) composition and a survival (time-to-event) response on human health or disease with or without covariate adjustments (e.g., age, sex).

Usage

OMiRKATS(obstime, delta, X, total.reads = NULL, tree, cov = NULL, g.unif.alpha = c(0.5), n.perm = 5000)

Arguments

• obstime - A numeric vector for the observed times.
• delta - A numeric vector for the event/censoring indicators (1: event, 0: censoring).
• X - A matrix for the OTU table (1. Elements are counts. 2. Rows are samples and columns are OTUs. 3. Monotone/singletone OTUs must be removed.).
• total.reads - A numeric vector for the total reads per sample in the entire community. If you survey the entire community, you do not need to specify this. If you test a microbial taxon, you need to specify this (see the examples below). Default is NULL for the entire community.
• tree - A rooted phylogenetic tree.
• cov - A data.frame (or vector) for covariate adjustment(s) (Rows are samples and columns are covariate variables).
• g.unif.alpha - A set of the candidate alpha parameter value(s) for the generalized UniFrac distance (e.g., c(0.25, 0.5)). Default is c(0.5).
• n.perm - The number of permutations. Default is 5000.

Values

$pvs.mirkats - The estimated p-values for individual MiRKAT-S tests

$p.omirkats - The estimated p-value for OMiRKAT-S

References

• Koh H, Livanos AE, Blaser MJ, Li H. (2018) A highly adaptive microbiome-based association test for survival traits. BMC Genomics 19, 210
• Plantinga A, Zhan X, Zhao N, Chen J, Jenq RR, Wu MC. (2017) MiRKAT-S: a community-level test of association between the microbiota and survival times. Microbiome 5, 17

Example

Import requisite R packages

library(dirmult) 
library(phyloseq) 
library(robustbase)
library(robCompositions) 
library(BiasedUrn)
library(CompQuadForm)
library(GUniFrac) 
library(ecodist) 
library(survival)
library(OMiSA)

Import example microbiome data

data(MiSurv.Data) 

otu.tab <- otu_table(MiSurv.Data)
tax.tab <- tax_table(MiSurv.Data)
tree <- phy_tree(MiSurv.Data)
obstime <- as.numeric(unlist(sample_data(MiSurv.Data)[,1]))
delta <- as.numeric(unlist(sample_data(MiSurv.Data)[,2]))
x1 <- as.numeric(unlist(sample_data(MiSurv.Data)[,3]))
x2 <- as.numeric(unlist(sample_data(MiSurv.Data)[,4]))
covs <- as.data.frame(cbind(x1, x2))
covs[,2] <- as.factor(covs[,2])

Example 1. To test the entire community (e.g., kingdom)

set.seed(100)
OMiRKATS(obstime, delta, otu.tab, total.reads=NULL, tree=tree, cov=covs)

Example 2. To test the higher-level taxon, p__Firmicutes

total.reads <- rowSums(otu.tab)
ind.Firmicutes <- which(tax.tab[,2] == "p__Firmicutes")
otu.tab.Firmicutes <- otu.tab[,ind.Firmicutes]
tree.Firmicutes <- prune_taxa(colnames(otu.tab.Firmicutes), tree)

set.seed(100)
OMiRKATS(obstime, delta, otu.tab.Firmicutes, total.reads=total.reads, tree=tree.Firmicutes, cov=covs)

Example 3. To test the higher-level taxon, p__Bacteroidetes

total.reads <- rowSums(otu.tab)
ind.Bacteroidetes <- which(tax.tab[,2] == "p__Bacteroidetes")
otu.tab.Bacteroidetes <- otu.tab[,ind.Bacteroidetes]
tree.Bacteroidetes <- prune_taxa(colnames(otu.tab.Bacteroidetes), tree)

set.seed(100)
OMiRKATS(obstime, delta, otu.tab.Bacteroidetes, total.reads=total.reads, tree=tree.Bacteroidetes, cov=covs)

hk1785/OMiSA documentation built on March 1, 2020, 6:38 p.m.