Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome.
High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantag...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0281594 |
| _version_ | 1797901836250775552 |
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| author | Suguru Fujita Yasuaki Karasawa Ken-Ichi Hironaka Y-H Taguchi Shinya Kuroda |
| author_facet | Suguru Fujita Yasuaki Karasawa Ken-Ichi Hironaka Y-H Taguchi Shinya Kuroda |
| author_sort | Suguru Fujita |
| collection | DOAJ |
| description | High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion of 75 g oral glucose. We characterized each molecule by individual dependence (constant or variable) and time dependence (later peak or early peak). Three of the four features extracted by TD were characterized by our previous hypothesis-driven study, indicating that TD can extract some of the same features obtained by hypothesis-driven analysis in a non-biased manner. In contrast to the years taken for our previous hypothesis-driven analysis, the data-driven analysis in this study took days, indicating that TD can extract biological features in a non-biased manner without the time-consuming process of hypothesis generation. |
| first_indexed | 2024-04-10T09:08:14Z |
| format | Article |
| id | doaj.art-eb454a6cba9d408ba65c2dddff5a2941 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T09:08:14Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-eb454a6cba9d408ba65c2dddff5a29412023-02-21T05:31:37ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01182e028159410.1371/journal.pone.0281594Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome.Suguru FujitaYasuaki KarasawaKen-Ichi HironakaY-H TaguchiShinya KurodaHigh-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion of 75 g oral glucose. We characterized each molecule by individual dependence (constant or variable) and time dependence (later peak or early peak). Three of the four features extracted by TD were characterized by our previous hypothesis-driven study, indicating that TD can extract some of the same features obtained by hypothesis-driven analysis in a non-biased manner. In contrast to the years taken for our previous hypothesis-driven analysis, the data-driven analysis in this study took days, indicating that TD can extract biological features in a non-biased manner without the time-consuming process of hypothesis generation.https://doi.org/10.1371/journal.pone.0281594 |
| spellingShingle | Suguru Fujita Yasuaki Karasawa Ken-Ichi Hironaka Y-H Taguchi Shinya Kuroda Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome. PLoS ONE |
| title | Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome. |
| title_full | Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome. |
| title_fullStr | Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome. |
| title_full_unstemmed | Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome. |
| title_short | Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome. |
| title_sort | features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome |
| url | https://doi.org/10.1371/journal.pone.0281594 |
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