README.md

In XiaoyunHuang33/MiATDS: A powerful adaptive microbiome-based association test for microbial association signals with diverse sparsity levels

MiATDS

Type: Package

Title: A powerful adaptive microbiome-based association test for microbial association signals with diverse sparsity levels Version: 1.0

Author: Xiaoyun Huang

Maintainer: Xiaoyun Huang [e-mail]

Imports: phyloseq, cluster, compositions, permute, vegan, ape, aSPU, MiSPU, devtools, MiHC

Description: An adaptive microbiome-based association test for detecting microbial association signals with diverse sparsity levels

License: GPL-2

Encoding: UTF-8

LazyData: true

URL: https://github.com/XiaoyunHuang33/MiATDS

Introduction

MiATDS, an adaptive microbiome-based association test combining the microbiome higher criticism analysis (MiHC) and adaptive weighted sum of powered score tests (aWSPU), has a good performance for detecting microbial association signals with diverse sparsity levels.

Installation

phyloseq:

BiocManager::install("phyloseq")

cluster:

install.packages("cluster")

compositions:

install.packages("compositions")

permute:

install.packages("permute")

vegan:

install.packages("vegan")

ape:

install.packages("ape")

aSPU:

install.packages("aSPU")

MiSPU:

install.packages("MiSPU")

devtools:

install.packages("devtools")

MiHC:

devtools::install_github("hk1785/MiHC", force=T)

You may install MiATDS from GitHub using the following code:

devtools::install_github("XiaoyunHuang33/MiATDS", force=T)

Description

The function MiATDS tests association between microbiome and a host phenotype. The association signals of association test can be at diverse sparsity levels. And host phenotype must be continuous or binary.

Usage

MiATDS(y, otu.tab, cov=NULL, tree=NULL, model = c("gaussian", "binomial"), pow=c(1:5), comp=FALSE, CLR=FALSE, opt.ncl=30, n.perm=5000)

Arguments

• y - host phenotype of interest (Continuous or binary).
• otu.tab - OTU count table.
• covs - covariate (e.g., age, gender).
• tree - A rooted phylogenetic tree.
• model - "gaussian" for Gaussian outcomes, "binomial" for Binomial outcomes.
• pow - A set of gamma values. Default is c(1:5).
• comp - An indicator if the OTU table contains absolute abundances or relative abundances. Default is comp=FALSE for absolute abundances.
• CLR - The centered log-ratio (CLR) transformation. Default is CLR=FALSE for no CLR transformation.
• opt.ncl - A upper limit to find the optimal number of clusters. Default is opt.ncl=30.
• n.perm - A number of permutations. Default is n.perm=5000.

Values

$pd.rank - The ranking for probability degree.

$awSPU.pvs - The p-value for the wSPU test and awSPU test.

$aWSPU.pvs - The p-value for the WSPU test and aWSPU test.

$omnibus.pvs - The p-value for the MiATDS test.

Example

Import requisite R packages:

library(cluster)
library(permute)
library(phyloseq)
library(MiHC)
library(MiATDS)  

Import example microbiome data:

data(obesity_data)
otu.tab <- obesity_data@otu_table
tree <- obesity_data@phy_tree
y<- obesity_data@sam_data$label
cov <- as.matrix(obesity_data@sam_data$x1)

Fit GEEMiHC:

set.seed(123)
out <- MiATDS(y, otu.tab, cov=cov, tree, model = "binomial")
out

References

• Pan, W., Kim, J., Zhang, Y., Shen, X., Wei, P., 2014. A powerful and adaptive association test for rare variants. Genetics 197, 1081-1095.

• Koh, H., Blaser, M.J., Li, H., 2017. A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping. Microbiome 5, 45.

• Koh, H., Zhao, N., 2020. A powerful microbial group association test based on the higher criticism analysis for sparse microbial association signals. Microbiome 8, 63.

Other Resources

OTUs_simulated function

Description

We generate the OTUs count table simulated based on the Dirichlet-multinomial model according to real data.

Usage

OTUs_simulated(data, nSam, nOTU, n_repeat, mu, size)

Arguments

• data - real data.

• nSam - Sample size.

• nOTU - The number of OTUs.

• n_repeat - The number of repeat.

• mu - The mean of the negative binomial distribution.

• size - The size of the negative binomial distribution.

Values

$OTU_simulated - OTU counts table simulated based on real data.

Example

data("throat.otu.tab", package ="MiSPU")
otu.tab <- round(OTUs_simulated(data=throat.otu.tab, nSam=100, nOTU=100, n_repeat=10, mu=1000, size=25)$OTU_simulated)

References

• Wu, C., Chen, J., Kim, J., Pan,W., 2016. An adaptive association test for microbiome data. Genome Med. 8, 56.

• La Rosa, P.S., Brooks, J.P., Deych, E., Boone, E.L., Edwards, D.J., Wang, Q., Sodergren, E., Weinstock, G., Shannon, W.D., 2012. Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data. PLoS ONE 7, e52078.

The datasets of case applications

(1) Association analysis between gut microbiome and obesity：

the information of processed data: data(obesity_data).

(2) Association analysis between gut microbiome and colorectal cancer:

the information of processed data: data(crc_data).

(3) Association analysis between gut microbiome and autism:

the information of processed data: data(autism_data).

References

• Escobar, J.S., Klotz, B., Valdes, B.E., Agudelo, G.M., 2014. The gut microbiota of Colombians differs from that of Americans, Europeans and Asians. BMC Microbiol. 14, 311.

• Wang, T., Cai, G., Qiu, Y., Fei, N., Zhang, M., Pang, X., Jia, W., Cai, S., Zhao, L., 2012. Structural segregation of gut microbiota between colorectal cancer patients and healthy volunteers. ISME J. 6, 320-329.

• Kang, D.-W., Park, J.G., Ilhan, Z.E., Wallstrom, G., LaBaer, J., Adams, J.B., Krajmalnik-Brown, R., 2013. Reduced incidence of Prevotella and Other fermenters in intestinal microflora of autistic children. PLoS ONE 8, e68322.

Statement

Our code mainly refers to R packages, MiHC, OMiAT, and MiSPU, where MiATDS function refers to MiHC and OMiAT, the generation of OTU table (i.e., OTUs_simulated function) refers to MiSPU.

Citation

If you use this code for you research, please cite our paper.

@article{
title={A powerful adaptive microbiome-based association test for microbial association signals with diverse sparsity levels},
author={Han Sun, Xiaoyun Huang, Lingling Fu, Ban Huo, Tingting He, Xingpeng Jiang},
journal={Journal of Genetics and Genomics},
volume={48(9)},
pages={851–859},
year={2021},
url={https://linkinghub.elsevier.com/retrieve/pii/S1673852721002599},
doi={10.1016/j.jgg.2021.08.002}
}

XiaoyunHuang33/MiATDS documentation built on Dec. 18, 2021, 7:23 p.m.