Robust volcano plot: identification of differential metabolites in the presence of outliers
Abstract Background 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 availabl...
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| Format: | Article |
| Language: | English |
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BMC
2018-04-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2117-2 |
| _version_ | 1831838329310543872 |
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| author | Nishith Kumar Md. Aminul Hoque Masahiro Sugimoto |
| author_facet | Nishith Kumar Md. Aminul Hoque Masahiro Sugimoto |
| author_sort | Nishith Kumar |
| collection | DOAJ |
| description | Abstract Background 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. Results 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. Conclusion 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. |
| first_indexed | 2024-12-23T05:31:58Z |
| format | Article |
| id | doaj.art-1fe33229489541e8994d97d54a321df1 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-23T05:31:58Z |
| publishDate | 2018-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-1fe33229489541e8994d97d54a321df12022-12-21T17:58:26ZengBMCBMC Bioinformatics1471-21052018-04-0119111110.1186/s12859-018-2117-2Robust volcano plot: identification of differential metabolites in the presence of outliersNishith Kumar0Md. Aminul Hoque1Masahiro Sugimoto2Department of Statistics, Rajshahi UniversityDepartment of Statistics, Rajshahi UniversityHealth Promotion and Preemptive Medicine, Research and Development Center for Minimally Invasive Therapies, Tokyo Medical UniversityAbstract Background 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. Results 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. Conclusion 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.http://link.springer.com/article/10.1186/s12859-018-2117-2MetabolomicsDifferential metabolitesFold changeClassical volcano plotReceiver operating characteristic (ROC) curve |
| spellingShingle | Nishith Kumar Md. Aminul Hoque Masahiro Sugimoto Robust volcano plot: identification of differential metabolites in the presence of outliers BMC Bioinformatics Metabolomics Differential metabolites Fold change Classical volcano plot Receiver operating characteristic (ROC) curve |
| title | Robust volcano plot: identification of differential metabolites in the presence of outliers |
| title_full | Robust volcano plot: identification of differential metabolites in the presence of outliers |
| title_fullStr | Robust volcano plot: identification of differential metabolites in the presence of outliers |
| title_full_unstemmed | Robust volcano plot: identification of differential metabolites in the presence of outliers |
| title_short | Robust volcano plot: identification of differential metabolites in the presence of outliers |
| title_sort | robust volcano plot identification of differential metabolites in the presence of outliers |
| topic | Metabolomics Differential metabolites Fold change Classical volcano plot Receiver operating characteristic (ROC) curve |
| url | http://link.springer.com/article/10.1186/s12859-018-2117-2 |
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