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

BMC Genomics
Viktor JonssonErik Kristiansson

Abstract

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. 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 positi...Continue Reading

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Citations

Oct 9, 2016·Microbiome·Chandan PalD G Joakim Larsson
Nov 29, 2016·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Viktor JonssonErik Kristiansson
Jan 5, 2018·Genes·Sandra Christine Andersen, Jeffrey Hoorfar
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Nov 27, 2018·Statistical Methods in Medical Research·Viktor JonssonErik Kristiansson
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Apr 23, 2017·BMC Genomics·Tobias ÖsterlundErik Kristiansson
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Methods Mentioned

BETA
Illumina sequencing
RNA-seq
amplicon sequencing
metagenomeSeq

Software Mentioned

DESeq2
MetaStats
pBLAT
XIPE
Limma
Tentacle
TOTEC
FANTOM
Voom
edgeR

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