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Variability in Metagenomic Count Data and Its Influence on the Identification of Differentially Abundant Genes

Journal article
Authors Viktor Jonsson
Tobias Österlund
Olle Nerman
Erik Kristiansson
Published in Journal of computational biology : a journal of computational molecular cell biology
Volume 24
Issue 4
Pages 311-326
ISSN 1557-8666
Publication year 2017
Published at Department of Mathematical Sciences
Pages 311-326
Language en
Subject categories Bioinformatics and Systems Biology, Microbiology, Mathematical statistics


Metagenomics is the study of microorganisms in environmental and clinical samples using high-throughput sequencing of random fragments of their DNA. Since metagenomics does not require any prior culturing of isolates, entire microbial communities can be studied directly in their natural state. In metagenomics, the abundance of genes is quantified by sorting and counting the DNA fragments. The resulting count data are high-dimensional and affected by high levels of technical and biological noise that make the statistical analysis challenging. In this article, we introduce an hierarchical overdispersed Poisson model to explore the variability in metagenomic data. By analyzing three comprehensive data sets, we show that the gene-specific variability varies substantially between genes and is dependent on biological function. We also assess the power of identifying differentially abundant genes and show that incorrect assumptions about the gene-specific variability can lead to unacceptable high rates of false positives. Finally, we evaluate shrinkage approaches to improve the variance estimation and show that the prior choice significantly affects the statistical power. The results presented in this study further elucidate the complex variance structure of metagenomic data and provide suggestions for accurate and reliable identification of differentially abundant genes.

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