Variability in Metagenomic Count Data and Its Influence on the Identification of Differentially Abundant Genes.
Artikel i vetenskaplig tidskrift, 2016

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.

Författare

Viktor Jonsson

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Tobias Österlund

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Olle Nerman

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Erik Kristiansson

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Journal of computational biology : a journal of computational molecular cell biology

1557-8666 (ISSN)

Vol. 24 4 311-326

Drivkrafter

Hållbar utveckling

Fundament

Grundläggande vetenskaper

Ämneskategorier

Mikrobiologi

Bioinformatik och systembiologi

Sannolikhetsteori och statistik

Genetik

Styrkeområden

Livsvetenskaper och teknik

DOI

10.1089/cmb.2016.0180

PubMed

27892712