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Robust volcano plot: identification of differential metabolites in the presence of outliers

Overview of attention for article published in BMC Bioinformatics, April 2018
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Title
Robust volcano plot: identification of differential metabolites in the presence of outliers
Published in
BMC Bioinformatics, April 2018
DOI 10.1186/s12859-018-2117-2
Pubmed ID
Authors

Nishith Kumar, Md. Aminul Hoque, Masahiro Sugimoto

Abstract

The identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers. We propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets. Two numerical experiments are used to evaluate the performance of the proposed technique against nine existing techniques, including the t-test and the Kruskal-Wallis test. Artificially generated data with outliers reveal that the proposed method results in a lower misclassification error rate and a greater area under the receiver operating characteristic curve compared with existing methods. An experimentally measured breast cancer dataset to which outliers were artificially added reveals that our proposed method produces only two non-overlapping differential metabolites whereas the other nine methods produced between seven and 57 non-overlapping differential metabolites. Our data analyses show that the performance of the proposed differential metabolite identification technique is better than that of existing methods. Thus, the proposed method can contribute to analysis of metabolomics data with outliers. The R package and user manual of the proposed method are available at https://github.com/nishithkumarpaul/Rvolcano .

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 26%
Researcher 8 14%
Student > Master 8 14%
Student > Doctoral Student 4 7%
Other 3 5%
Other 5 9%
Unknown 14 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 19%
Agricultural and Biological Sciences 8 14%
Medicine and Dentistry 6 11%
Chemistry 4 7%
Computer Science 4 7%
Other 7 12%
Unknown 17 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 30 October 2018.
All research outputs
#8,270,761
of 13,715,693 outputs
Outputs from BMC Bioinformatics
#3,291
of 5,103 outputs
Outputs of similar age
#153,162
of 272,715 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 24 outputs
Altmetric has tracked 13,715,693 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,103 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.