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Göteborgs universitets publikationer

Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics

Författare och institution:
Viktor Jonsson (Institutionen för matematiska vetenskaper, Chalmers/GU); Tobias Österlund (Institutionen för matematiska vetenskaper, matematisk statistik, Chalmers/GU); Olle Nerman (Institutionen för matematiska vetenskaper, matematisk statistik, Chalmers/GU); Erik Kristiansson (Institutionen för matematiska vetenskaper, matematisk statistik, Chalmers/GU)
Publicerad i:
BMC Genomics, 17
ISSN:
1471-2164
Publikationstyp:
Artikel, refereegranskad vetenskaplig
Publiceringsår:
2016
Språk:
engelska
Fulltextlänk:
Fulltextlänk (lokalt arkiv):
Sammanfattning (abstract):
Background: Metagenomics is the study of microbial communities by sequencing of genetic material directly from environmental or clinical samples. The genes present in the metagenomes are quantified by annotating and counting the generated DNA fragments. Identification of differentially abundant genes between metagenomes can provide important information about differences in community structure, diversity and biological function. Metagenomic data is however high-dimensional, contain high levels of biological and technical noise and have typically few biological replicates. The statistical analysis is therefore challenging and many approaches have been suggested to date. Results: In this article we perform a comprehensive evaluation of 14 methods for identification of differentially abundant genes between metagenomes. The methods are compared based on the power to detect differentially abundant genes and their ability to correctly estimate the type I error rate and the false discovery rate. We show that sample size, effect size, and gene abundance greatly affect the performance of all methods. Several of the methods also show non-optimal model assumptions and biased false discovery rate estimates, which can result in too large numbers of false positives. We also demonstrate that the performance of several of the methods differs substantially between metagenomic data sequenced by different technologies. Conclusions: Two methods, primarily designed for the analysis of RNA sequencing data (edgeR and DESeq2) together with a generalized linear model based on an overdispersed Poisson distribution were found to have best overall performance. The results presented in this study may serve as a guide for selecting suitable statistical methods for identification of differentially abundant genes in metagenomes.
Ämne (baseras på Högskoleverkets indelning av forskningsämnen):
NATURVETENSKAP ->
Matematik
Nyckelord:
Environmental sequencing, Next generation sequencing, Categorical data analysis, Differential, false discovery rate
Chalmers styrkeområden:
Livsvetenskaper
Postens nummer:
232639
Posten skapad:
2016-03-02 13:36
Posten ändrad:
2016-06-30 14:29

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