Overview

IFAA offers a robust approach to make inference on the associations of covariates with the absolute abundance (AA) of microbiome in an ecosystem, and the associations of covariates with the abundance ratios of microbiome taxa/OTU/ASV (or other units).

Installation

# Install from CRAN
install.packages("IFAA", repos = "http://cran.us.r-project.org")

# Or from GitHub:
devtools::install_github("gitlzg/IFAA")

Usage

Use example datasets to run IFAA() function. The example generates an example data from scratch, with 60 taxon, 40 subjects, and 3 covariates.

# Detailed instructions on the package are 
# provided in the vignettes and manual
library(IFAA)

## create an ID variable for the example data
ID=seq_len(40)

## generate three covariates x1, x2, and x3, with x2 binary
x1<-rnorm(40)
x2<-rbinom(40,1,0.5)
x3<-rnorm(40)
dataC<-data.frame(cbind(ID,x1,x2,x3))

## Coefficients for x1, x2, and x3 among 60 taxa.
beta_1<-c(0.1,rep(0,59))
beta_2<-c(0,0.2,rep(0,58))
beta_3<-rnorm(60)
beta_mat<-cbind(beta_1,beta_2,beta_3)

## Generate absolute abundance for 60 taxa in ecosystem.
dataM_eco<-floor(exp(10+as.matrix(dataC[,-1]) %*% t(beta_mat) + rnorm(2400,sd=0.05)))

## Generate sequence depth and generate observed abundance
Ci<-runif(40,0.01,0.05)
dataM<-floor(apply(dataM_eco,2,function(x) x*Ci))
colnames(dataM)<-paste0("rawCount",1:60)

## Randomly introduce 0 to make 25% sparsity level.
dataM[sample(seq_len(length(dataM)),length(dataM)/4)]<-0

dataM<-data.frame(cbind(ID,dataM))

# The following steps are to create a SummarizedExperiment object.
# If you already have a SummarizedExperiment format data, you can 
# ignore the data processing steps below

# merge microbiome and covariates data 
data_merged<-merge(dataM,dataC,by="ID",all=FALSE)

# Seperate microbiome data and covariate data
# and drop ID variable from the microbiome data
dataM_sub<-data_merged[,colnames(dataM)[!colnames(dataM)%in%c("ID")]]
dataC_sub<-data_merged[,colnames(dataC)]

# Create SummarizedExperiment object
test_dat<-SummarizedExperiment::SummarizedExperiment(
                assays=list(MicrobData=t(dataM_sub)), 
                            colData=dataC_sub)

# Again, if you already have a SummarizedExperiment format data, you can 
# ignore the above steps

# run IFAA
set.seed(123) # For full reproducibility
results <- IFAA(experiment_dat = test_dat,
                testCov = c("x1","x2"),
                ctrlCov = c("x3"),
                sampleIDname="ID",
                fdrRate = 0.05)

Once the analysis is done, you can extract the full resuts and significant regression coefficients along with 95% confidence intervals using this command:

# to extract all results:
summary_res<-results$full_results

# to extract significant results:
sig_results=subset(summary_res,sig_ind==TRUE)

Use the same datasets to run MZILN() function.

set.seed(123) # For full reproducibility
results <- MZILN(experiment_dat=test_dat,
                 refTaxa=c("rawCount34"),
                 allCov=c("x1","x2","x3"),
                 sampleIDname="ID",
                 fdrRate=0.05)

Regression results including confidence intervals for the targeted ratios can be extracted in the following way:

# to extract the results for all ratios with rawCount34 
# as the denominator:
 summary_res<-results$full_results
 
# to extract results for the ratio of a specific taxon (e.g., 
# rawCount18) over rawCount34:
 target_ratio=summary_res[summary_res$taxon=="rawCount18",]
 
# to extract all ratios having significant associations:
 sig_ratios=subset(summary_res,sig_ind==TRUE)

References