eBay: An method for differential abundance detection

In liudoubletian/eBay: An empirical bayes approach to normalization

Description

An method for differential abundance detection

Usage

eBay(otu.data, group, cutf, test.methods, adj.m)

Arguments

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

Value

final.p the adjusted p values

dif.otus the set of differentially abundant OTUs

Examples

####generate data####
library(eBay)
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)
#####differential abundance testing###
ebay.res <- eBay(otu.data=ntree_table, group=group, test.method="t",
cutf=0.05,adj.m="BH")

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