Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1000352
Title: Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples
Authors: White J.R.
Nagarajan N. 
Pop M.
Keywords: RNA 16S
bacterial DNA
article
bacterium
computer program
Fisher exact test
gene expression
gene sequence
human
human experiment
infant
intermethod comparison
intestine
metabolism
metagenomics
microbiome
normal human
simulation
statistical analysis
virus
bacterium
chromosome map
classification
gene expression profiling
genetics
intestine
isolation and purification
methodology
microbiology
obesity
Bacteria (microorganisms)
Bacteria
Chromosome Mapping
DNA, Bacterial
Gene Expression Profiling
Humans
Intestines
Obesity
Issue Date: 2009
Citation: White J.R., Nagarajan N., Pop M. (2009). Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples. PLoS Computational Biology 5 (4). ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1000352
Rights: Attribution 4.0 International
Abstract: Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them. We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied to digital gene expression studies (e.g. SAGE). A web server implementation of our methods and freely available source code can be found at http://metastats.cbcb.umd.edu/.
Source Title: PLoS Computational Biology
URI: https://scholarbank.nus.edu.sg/handle/10635/161676
ISSN: 1553734X
DOI: 10.1371/journal.pcbi.1000352
Rights: Attribution 4.0 International
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