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Comparison of normalization methods for the analysis of metagenomic gene abundance data

Overview of attention for article published in BMC Genomics, April 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
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Title
Comparison of normalization methods for the analysis of metagenomic gene abundance data
Published in
BMC Genomics, April 2018
DOI 10.1186/s12864-018-4637-6
Pubmed ID
Authors

Mariana Buongermino Pereira, Mikael Wallroth, Viktor Jonsson, Erik Kristiansson

Abstract

In shotgun metagenomics, microbial communities are studied through direct sequencing of DNA without any prior cultivation. By comparing gene abundances estimated from the generated sequencing reads, functional differences between the communities can be identified. However, gene abundance data is affected by high levels of systematic variability, which can greatly reduce the statistical power and introduce false positives. Normalization, which is the process where systematic variability is identified and removed, is therefore a vital part of the data analysis. A wide range of normalization methods for high-dimensional count data has been proposed but their performance on the analysis of shotgun metagenomic data has not been evaluated. Here, we present a systematic evaluation of nine normalization methods for gene abundance data. The methods were evaluated through resampling of three comprehensive datasets, creating a realistic setting that preserved the unique characteristics of metagenomic data. Performance was measured in terms of the methods ability to identify differentially abundant genes (DAGs), correctly calculate unbiased p-values and control the false discovery rate (FDR). Our results showed that the choice of normalization method has a large impact on the end results. When the DAGs were asymmetrically present between the experimental conditions, many normalization methods had a reduced true positive rate (TPR) and a high false positive rate (FPR). The methods trimmed mean of M-values (TMM) and relative log expression (RLE) had the overall highest performance and are therefore recommended for the analysis of gene abundance data. For larger sample sizes, CSS also showed satisfactory performance. This study emphasizes the importance of selecting a suitable normalization methods in the analysis of data from shotgun metagenomics. Our results also demonstrate that improper methods may result in unacceptably high levels of false positives, which in turn may lead to incorrect or obfuscated biological interpretation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 456 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 456 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 97 21%
Researcher 96 21%
Student > Master 63 14%
Student > Bachelor 34 7%
Student > Doctoral Student 24 5%
Other 53 12%
Unknown 89 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 115 25%
Biochemistry, Genetics and Molecular Biology 98 21%
Environmental Science 32 7%
Immunology and Microbiology 26 6%
Computer Science 21 5%
Other 60 13%
Unknown 104 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 43. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 September 2023.
All research outputs
#969,969
of 25,563,770 outputs
Outputs from BMC Genomics
#120
of 11,282 outputs
Outputs of similar age
#21,317
of 340,957 outputs
Outputs of similar age from BMC Genomics
#2
of 238 outputs
Altmetric has tracked 25,563,770 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,282 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 340,957 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 238 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.