Dataset Information

A robust approach for identifying differentially abundant features in metagenomic samples.

ABSTRACT: The analysis of differential abundance for features (e.g. species or genes) can provide us with a better understanding of microbial communities, thus increasing our comprehension and understanding of the behaviors of microbial communities. However, it could also mislead us about the characteristics of microbial communities if the abundances or counts of features on different scales are not properly normalized within and between communities, prior to the analysis of differential abundance. Normalization methods used in the differential analysis typically try to adjust counts on different scales to a common scale using the total sum, mean or median of representative features across all samples. These methods often yield undesirable results when the difference in total counts of differentially abundant features (DAFs) across different conditions is large.We develop a novel method, Ratio Approach for Identifying Differential Abundance (RAIDA), which utilizes the ratio between features in a modified zero-inflated lognormal model. RAIDA removes possible problems associated with counts on different scales within and between conditions. As a result, its performance is not affected by the amount of difference in total abundances of DAFs across different conditions. Through comprehensive simulation studies, the performance of our method is consistently powerful, and under some situations, RAIDA greatly surpasses other existing methods. We also apply RAIDA on real datasets of type II diabetes and find interesting results consistent with previous reports.An R package for RAIDA can be accessed from http://cals.arizona.edu/%7Eanling/sbg/software.htm.

SUBMITTER: Sohn MB

PROVIDER: S-EPMC4495302 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A robust approach for identifying differentially abundant features in metagenomic samples.

Sohn Michael B MB Du Ruofei R An Lingling L

Bioinformatics (Oxford, England) 20150319 14

<h4>Motivation</h4>The analysis of differential abundance for features (e.g. species or genes) can provide us with a better understanding of microbial communities, thus increasing our comprehension and understanding of the behaviors of microbial communities. However, it could also mislead us about the characteristics of microbial communities if the abundances or counts of features on different scales are not properly normalized within and between communities, prior to the analysis of differentia ...[more]

PMID: 25792553

Similar Datasets

Project description: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/.

Project description:BACKGROUND:Microbial longitudinal studies are powerful experimental designs utilized to classify diseases, determine prognosis, and analyze microbial systems dynamics. In longitudinal studies, only identifying differential features between two phenotypes does not provide sufficient information to determine whether a change in the relative abundance is short-term or continuous. Furthermore, sample collection in longitudinal studies suffers from all forms of variability such as a different number of subjects per phenotypic group, a different number of samples per subject, and samples not collected at consistent time points. These inconsistencies are common in studies that collect samples from human subjects. RESULTS:We present MetaLonDA, an R package that is capable of identifying significant time intervals of differentially abundant microbial features. MetaLonDA is flexible such that it can perform differential abundance tests despite inconsistencies associated with sample collection. Extensive experiments on simulated datasets quantitatively demonstrate the effectiveness of MetaLonDA with significant improvement over alternative methods. We applied MetaLonDA to the DIABIMMUNE cohort ( https://pubs.broadinstitute.org/diabimmune ) substantiating significant early lifetime intervals of exposure to Bacteroides and Bifidobacterium in Finnish and Russian infants. Additionally, we established significant time intervals during which novel differentially relative abundant microbial genera may contribute to aberrant immunogenicity and development of autoimmune disease. CONCLUSION:MetaLonDA is computationally efficient and can be run on desktop machines. The identified differentially abundant features and their time intervals have the potential to distinguish microbial biomarkers that may be used for microbial reconstitution through bacteriotherapy, probiotics, or antibiotics. Moreover, MetaLonDA can be applied to any longitudinal count data such as metagenomic sequencing, 16S rRNA gene sequencing, or RNAseq. MetaLonDA is publicly available on CRAN ( https://CRAN.R-project.org/package=MetaLonDA ).

Project description:Immune surveillance cells such as T cells and phagocytes utilize integral plasma membrane receptors to recognize surface signatures on triggered and activated cells such as those in apoptosis. One such family of plasma membrane sensors, the transmembrane immunoglobulin and mucin domain (Tim) proteins, specifically recognize phosphatidylserine (PS) but elicit distinct immunological responses. The molecular basis for the recognition of lipid signals on target cell surfaces is not well understood. Previous results suggest that basic side chains present at the membrane interface on the Tim proteins might facilitate association with additional anionic lipids including but not necessarily limited to PS. We, therefore, performed a comparative quantitative analysis of the binding of the murine Tim1, Tim3, and Tim4, to synthetic anionic phospholipid membranes under physiologically relevant conditions. X-ray reflectivity and vesicle binding studies were used to compare the water-soluble domain of Tim3 with results previously obtained for Tim1 and Tim4. Although a calcium link was essential for all three proteins, the three homologs differed in how they balance the hydrophobic and electrostatic interactions driving membrane association. The proteins also varied in their sensing of phospholipid chain unsaturation and showed different degrees of cooperativity in their dependence on bilayer PS concentration. Surprisingly, trace amounts of anionic phosphatidic acid greatly strengthened the bilayer association of Tim3 and Tim4, but not Tim1. A novel mathematical model provided values for the binding parameters and illuminated the complex interplay among ligands. In conclusion, our results provide a quantitative description of the contrasting selectivity used by three Tim proteins in the recognition of phospholipids presented on target cell surfaces. This paradigm is generally applicable to the analysis of the binding of peripheral proteins to target membranes through the heterotropic cooperative interactions of multiple ligands.

Dataset Information

A robust approach for identifying differentially abundant features in metagenomic samples.

Publications

A robust approach for identifying differentially abundant features in metagenomic samples.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets