IFAA: Robust association identification and inference for absolute...

In quranwu/IFAA: Robust Inference for Absolute Abundance in Microbiome Analysis

Robust association identification and inference for absolute abundance in microbiome analyses

Description

The IFAA function is to make inference on the association of microbiome with covariates

To model the association, the following equation is used:

\loadmathjax \mjdeqn\log

(\mathcalY_i^k)|\mathcalY_i^k>0=\beta^0k+X_i^T\beta^k+W_i^T\gamma^k+Z_i^Tb_i+\epsilon_i^k,\hspace0.2cmk=1,...,K+1 where

• \mjeqn\mathcal

  Y_i^k is the AA of taxa \mjeqnk in subject \mjeqni in the entire ecosystem.

• \mjeqn

  X_i is the covariate matrix.

• \mjeqn

  W_i is the confounder matrix.

• \mjeqn

  Z_i is the design matrix for random effects.

• \mjeqn\beta

  ^k is the regression coefficients that will be estimated and tested with the IFAA() function.

The challenge in microbiome analysis is that \mjeqn\mathcalY_i^k can not be observed. What is observed is its small proportion: \mjeqnY_i^k=C_i\mathcalY^k_i, where \mjeqnC_i is an unknown number between 0 and 1 that denote the observed proportion.

The IFAA method can successfully addressed this challenge. The IFAA() will estimate the parameter \mjeqn\beta^k and their 95% confidence intervals. High-dimensional \mjeqnX_i is handled by regularization.

When using this function, most of the time, users just need to feed these three inputs to the function: experiment_dat, testCov and ctrlCov. All other inputs can just take their default values.

Usage

IFAA(
  experiment_dat,
  microbVar = "all",
  testCov = NULL,
  ctrlCov = NULL,
  sampleIDname = NULL,
  testMany = TRUE,
  ctrlMany = FALSE,
  nRef = 40,
  nRefMaxForEsti = 2,
  refTaxa = NULL,
  adjust_method = "BY",
  fdrRate = 0.05,
  paraJobs = NULL,
  bootB = 500,
  standardize = FALSE,
  sequentialRun = FALSE,
  refReadsThresh = 0.2,
  taxDropThresh = 0,
  SDThresh = 0.05,
  SDquantilThresh = 0,
  balanceCut = 0.2,
  verbose = TRUE
)

Arguments

experiment_dat	A SummarizedExperiment object containing microbiome data and covariates (see example on how to create a SummarizedExperiment object). The microbiome data can be absolute abundance or relative abundance with each column per sample and each row per taxon/OTU/ASV (or any other unit). No imputation is needed for zero-valued data points. The covariates data contains covariates and confounders with each row per sample and each column per variable. The covariates data has to be numeric or binary. Categorical variables should be converted into dummy variables.
microbVar	This takes a single or vector of microbiome variable names (e.g., taxa, OTU and ASV names) of interest. Default is "all" meaning all microbiome variables will be analyzed. If a subset of microbiome variables is specified, the output will only contain the specified variables, and p-value adjustment for multiple testing will only be applied to the subset.
testCov	Covariates that are of primary interest for testing and estimating the associations. It corresponds to $X_i$ in the equation. Default is NULL which means all covariates are testCov.
ctrlCov	Potential confounders that will be adjusted in the model. It corresponds to $W_i$ in the equation. Default is NULL which means all covariates except those in testCov are adjusted as confounders.
sampleIDname	Name of the sample ID variable in the data. In the case that the data does not have an ID variable, this can be ignored. Default is NULL.
testMany	This takes logical value TRUE or FALSE. If TRUE, the testCov will contain all the variables in CovData provided testCov is set to be NULL. The default value is TRUE which does not do anything if testCov is not NULL.
ctrlMany	This takes logical value TRUE or FALSE. If TRUE, all variables except testCov are considered as control covariates provided ctrlCov is set to be NULL. The default value is FALSE.
nRef	The number of randomly picked reference taxa used in phase 1. Default number is 40.
nRefMaxForEsti	The maximum number of final reference taxa used in phase 2. The default is 2.
refTaxa	A vector of taxa or OTU or ASV names. These are reference taxa specified by the user to be used in phase 1. If the number of reference taxa is less than 'nRef', the algorithm will randomly pick extra reference taxa to make up 'nRef'. The default is NULL since the algorithm will pick reference taxa randomly.
adjust_method	The adjusting method for p value adjustment. Default is "BY" for dependent FDR adjustment. It can take any adjustment method for p.adjust function in R.
fdrRate	The false discovery rate for identifying taxa/OTU/ASV associated with testCov. Default is 0.05.
paraJobs	If sequentialRun is FALSE, this specifies the number of parallel jobs that will be registered to run the algorithm. If specified as NULL, it will automatically detect the cores to decide the number of parallel jobs. Default is NULL.
bootB	Number of bootstrap samples for obtaining confidence interval of estimates in phase 2 for the high dimensional regression. The default is 500.
standardize	This takes a logical value TRUE or FALSE. If TRUE, the design matrix for X will be standardized in the analyses and the results. Default is FALSE.
sequentialRun	This takes a logical value TRUE or FALSE. Default is FALSE. This argument could be useful for debug.
refReadsThresh	The threshold of proportion of non-zero sequencing reads for choosing the reference taxon in phase 2. The default is 0.2 which means at least 20% non-zero sequencing reads.
taxDropThresh	The threshold of number of non-zero sequencing reads for each taxon to be dropped from the analysis. The default is 0 which means taxon without any sequencing reads will be dropped from the analysis.
SDThresh	The threshold of standard deviations of sequencing reads for been chosen as the reference taxon in phase 2. The default is 0.05 which means the standard deviation of sequencing reads should be at least 0.05 in order to be chosen as reference taxon.
SDquantilThresh	The threshold of the quantile of standard deviation of sequencing reads, above which could be selected as reference taxon. The default is 0.
balanceCut	The threshold of the proportion of non-zero sequencing reads in each group of a binary variable for choosing the final reference taxa in phase 2. The default number is 0.2 which means at least 20% non-zero sequencing reads in each group are needed to be eligible for being chosen as a final reference taxon.
verbose	Whether the process message is printed out to the console. The default is TRUE.

Value

A list containing 2 elements

• full_results: The main results for IFAA containing the estimation and testing results for all associations between all taxa and all test covariates in testCov. It is a dataframe with each row representing an association, and eight columns named as "taxon", "cov", "estimate", "SE.est", "CI.low", "CI.up", "adj.p.value", and "sig_ind". The columns correspond to taxon name, covariate name, association estimates, standard error estimates, lower bound and upper bound of the 95% confidence interval, adjusted p value, and the indicator showing whether the association is significant after multiple testing adjustment.

• metadata: The metadata is a list.

  • covariatesData: A dataset containing covariates and confounders used in the analyses.

  • final_ref_taxon: The final 2 reference taxon used for analysis.

  • ref_taxon_count: The counts of selection for the associations of all taxa with test covariates in Phase 1.

  • totalTimeMins: The average magnitude estimates for the associations of all taxa with test covariates in Phase 1.

  • ref_taxon_est: Total time used for the entire analysis.

  • fdrRate: FDR rate used for the analysis.

  • adjust_method: Multiple testing adjust method used for the analysis.

References

Li et al.(2021) IFAA: Robust association identification and Inference For Absolute Abundance in microbiome analyses. Journal of the American Statistical Association. 116(536):1595-1608

Examples

library(IFAA)
## A makeup example data from Scratch. 10 taxon, 20 subjects, 3 covariates

set.seed(1)

## create an ID variable for the example data
ID=seq_len(20)

## generate three covariates x1, x2, and x3, with x2 binary
x1<-rnorm(20)
x2<-rbinom(20,1,0.5)
x3<-rnorm(20)
dataC<-data.frame(cbind(ID,x1,x2,x3))

## Coefficients for x1, x2, and x3 among 10 taxa.
beta_1<-c(0.1,rep(0,9))
beta_2<-c(0,0.2,rep(0,8))
beta_3<-rnorm(10)
beta_mat<-cbind(beta_1,beta_2,beta_3)

## Generate absolute abundance for 10 taxa in ecosystem.
dataM_eco<-floor(exp(10+as.matrix(dataC[,-1])%*%t(beta_mat) + rnorm(200,sd=0.05)))

## Generate sequence depth and generate observed abundance
Ci<-runif(20,0.01,0.05)
dataM<-floor(apply(dataM_eco,2,function(x) x*Ci))
colnames(dataM)<-paste0("rawCount",1:10)

## 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 following steps and directly feed it to the IFAA function.

## Merge two dataset by ID variable
data_merged<-merge(dataM,dataC,by="ID",all=FALSE)

## Seperate microbiome data and covariate data, drop ID variable from 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 and directly feed it to the IFAA function.

set.seed(123) # For full reproducibility

results <- IFAA(experiment_dat = test_dat,
                testCov = c("x1", "x2"),
                ctrlCov = c("x3"),
                sampleIDname="ID",
                fdrRate = 0.05,
                nRef = 2,
                paraJobs = 2)
## to extract all results:
summary_res<-results$full_results
## to extract significant results:
sig_results=subset(summary_res,sig_ind==TRUE)

## If only interested in certain taxa, say "rawCount1", "rawCount2", 
## and "rawCount3", one can do: 
results <- IFAA(
experiment_dat = test_dat,
microbVar = c("rawCount1", "rawCount2", "rawCount3"),
testCov = c("x1", "x2"),
ctrlCov = c("x3"),
sampleIDname = "ID",
fdrRate = 0.05,
nRef = 2,
paraJobs = 2
)

quranwu/IFAA documentation built on Feb. 1, 2023, 10:38 a.m.