README.md

In liudoubletian/eBay: An empirical bayes approach to normalization

eBay (An empirical Bayes normalization method for microbiome data)

Introduction

The characteristics of microbiome data are complicated: varied sequencing depth, lots of zeros, over-dispersion, a phylogenetic tree representing the relationship among taxa, and compositionality. In order to deal with these problems, we proposed an empirical Bayes normalization method called eBay. [1] shows the statistical model in detail.

Installation

install.packages("devtools")  
devtools::install_github("liudoubletian/eBay")  
library(eBay)  

Basic Usage

normalization in the absence of a phylogenetic tree

eBay(otu.data=ntree_table, group=group, test.method="t", cutf=0.05,adj.m="BH")

• otu.data : an OTU table with n rows (samples) and m columns (taxa)
• group : a n-vector of group indicators
• test.method : t-test or Wilcoxon rank sum test
• cutf : level of significance
• adj.m : the adjustment methods for p-values

it returns a list of results: final.p : the adjusted p values dif.otus : the set of differentially abundant OTUs

phylogeny-aware empirical Bayes normalization

eBay_tree(otu.data=tree_table,tree=tree,group=group,test.method="t",cutf=0.05,adj.m="BH")

• otu.data : an OTU table with n rows (samples) and m columns (taxa)
• tree : a phylogenetic tree among m taxa
• group : a n-vector of group indicators
• test.method : t-test or Wilcoxon rank sum test
• cutf : level of significance
• adj.m : the adjustment methods for p-values

it returns a list of results: final.p : the adjusted p values dif.otus : the set of differentially abundant OTUs

Example

simulation from DM

The following function shows how to simulate data from a Dirichlet-multinomial distribution.

set.seed(1)  
rand_pi <- runif(20)   
control_pi = case_pi = rand_pi/sum(rand_pi)   
control_theta = case_theta = 0.1  
group <- rep(c(0,1),each =20)  
ntree_table <- simulation_dm(p=20,seed=1, N=20,control_pi, case_pi,control_theta,case_theta)  

Run the eBay function to normalize the data and return a set of differentially abundant taxa.

ebay.res <- eBay(otu.data=ntree_table, group=group, test.method="t", cutf=0.05,adj.m="BH")  
ebay.res  

simulation from DTM

First, generate a tree randomly.

p <- 40
set.seed(1)
tree <- simulate_tree(p)

The following function shows how to simulate data from the Dirichlet-tree multinomial model.

set.seed(1)    
control_pi = case_pi = c()
for(j in (p+1):(p+tree$Nnode)){
   set.seed(j)
   random_pi <- runif(1,0.2,0.4)
   control_pi[which(tree$edge[,1]==j)] <- c(random_pi, 1-random_pi)
   case_pi[which(tree$edge[,1]==j)] <- c(random_pi, 1-random_pi)
}
control_theta = case_theta = rep(0.1, tree$Nnode) 
group <- rep(c(0,1),each =20)  
tree_table <- simulation_dtm(p=40,tree, seed=1, N=20,control_pi, case_pi,control_theta,case_theta)  

Run the eBay_tree function to normalize the data and return a set of differentially abundant taxa.

ebay_tree.res <- eBay_tree(otu.data=tree_table, tree=tree, group=group, test.method="t", cutf=0.05,adj.m="BH")  
ebay_tree.res  

[1] Tiantian Liu, Tao Wang. (2020) An empirical Bayes approach to normalization and differential abundance testing for microbiome data (Under review).

liudoubletian/eBay documentation built on Oct. 10, 2020, 8:43 p.m.