A positive statistical benchmark to assess network agreement
Abstract Current computational methods for validating experimental network datasets compare overlap, i.e., shared links, with a reference network using a negative benchmark. However, this fails to quantify the level of agreement between the two networks. To address this, we propose a positive statis...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-38625-z |
| _version_ | 1797817938997149696 |
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| author | Bingjie Hao István A. Kovács |
| author_facet | Bingjie Hao István A. Kovács |
| author_sort | Bingjie Hao |
| collection | DOAJ |
| description | Abstract Current computational methods for validating experimental network datasets compare overlap, i.e., shared links, with a reference network using a negative benchmark. However, this fails to quantify the level of agreement between the two networks. To address this, we propose a positive statistical benchmark to determine the maximum possible overlap between networks. Our approach can efficiently generate this benchmark in a maximum entropy framework and provides a way to assess whether the observed overlap is significantly different from the best-case scenario. We introduce a normalized overlap score, Normlap, to enhance comparisons between experimental networks. As an application, we compare molecular and functional networks, resulting in an agreement network of human as well as yeast network datasets. The Normlap score can improve the comparison between experimental networks by providing a computational alternative to network thresholding and validation. |
| first_indexed | 2024-03-13T09:00:45Z |
| format | Article |
| id | doaj.art-b7e5114df7634ca7b2f0e23125c25589 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-13T09:00:45Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-b7e5114df7634ca7b2f0e23125c255892023-05-28T11:21:43ZengNature PortfolioNature Communications2041-17232023-05-0114111110.1038/s41467-023-38625-zA positive statistical benchmark to assess network agreementBingjie Hao0István A. Kovács1Department of Physics and Astronomy, Northwestern UniversityDepartment of Physics and Astronomy, Northwestern UniversityAbstract Current computational methods for validating experimental network datasets compare overlap, i.e., shared links, with a reference network using a negative benchmark. However, this fails to quantify the level of agreement between the two networks. To address this, we propose a positive statistical benchmark to determine the maximum possible overlap between networks. Our approach can efficiently generate this benchmark in a maximum entropy framework and provides a way to assess whether the observed overlap is significantly different from the best-case scenario. We introduce a normalized overlap score, Normlap, to enhance comparisons between experimental networks. As an application, we compare molecular and functional networks, resulting in an agreement network of human as well as yeast network datasets. The Normlap score can improve the comparison between experimental networks by providing a computational alternative to network thresholding and validation.https://doi.org/10.1038/s41467-023-38625-z |
| spellingShingle | Bingjie Hao István A. Kovács A positive statistical benchmark to assess network agreement Nature Communications |
| title | A positive statistical benchmark to assess network agreement |
| title_full | A positive statistical benchmark to assess network agreement |
| title_fullStr | A positive statistical benchmark to assess network agreement |
| title_full_unstemmed | A positive statistical benchmark to assess network agreement |
| title_short | A positive statistical benchmark to assess network agreement |
| title_sort | positive statistical benchmark to assess network agreement |
| url | https://doi.org/10.1038/s41467-023-38625-z |
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