iDMET: network-based approach for integrating differential analysis of cancer metabolomics
Abstract Background Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different s...
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| Format: | Article |
| Language: | English |
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BMC
2022-11-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-05068-0 |
| _version_ | 1811207563542265856 |
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| author | Rira Matsuta Hiroyuki Yamamoto Masaru Tomita Rintaro Saito |
| author_facet | Rira Matsuta Hiroyuki Yamamoto Masaru Tomita Rintaro Saito |
| author_sort | Rira Matsuta |
| collection | DOAJ |
| description | Abstract Background Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study. Results We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered. Conclusions We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena. |
| first_indexed | 2024-04-12T04:06:29Z |
| format | Article |
| id | doaj.art-850608c5c203452b8df8f783c448aa35 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T04:06:29Z |
| publishDate | 2022-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-850608c5c203452b8df8f783c448aa352022-12-22T03:48:36ZengBMCBMC Bioinformatics1471-21052022-11-0123112010.1186/s12859-022-05068-0iDMET: network-based approach for integrating differential analysis of cancer metabolomicsRira Matsuta0Hiroyuki Yamamoto1Masaru Tomita2Rintaro Saito3Institute for Advanced Biosciences, Keio UniversityHuman Metabolome Technologies, Inc.Institute for Advanced Biosciences, Keio UniversityInstitute for Advanced Biosciences, Keio UniversityAbstract Background Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study. Results We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered. Conclusions We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.https://doi.org/10.1186/s12859-022-05068-0MetabolomicsData integrationMulti-laboratory comparisonReproducibilityOdds ratio |
| spellingShingle | Rira Matsuta Hiroyuki Yamamoto Masaru Tomita Rintaro Saito iDMET: network-based approach for integrating differential analysis of cancer metabolomics BMC Bioinformatics Metabolomics Data integration Multi-laboratory comparison Reproducibility Odds ratio |
| title | iDMET: network-based approach for integrating differential analysis of cancer metabolomics |
| title_full | iDMET: network-based approach for integrating differential analysis of cancer metabolomics |
| title_fullStr | iDMET: network-based approach for integrating differential analysis of cancer metabolomics |
| title_full_unstemmed | iDMET: network-based approach for integrating differential analysis of cancer metabolomics |
| title_short | iDMET: network-based approach for integrating differential analysis of cancer metabolomics |
| title_sort | idmet network based approach for integrating differential analysis of cancer metabolomics |
| topic | Metabolomics Data integration Multi-laboratory comparison Reproducibility Odds ratio |
| url | https://doi.org/10.1186/s12859-022-05068-0 |
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