Image 3_Learning Gaussian graphical models from correlated data.tiff
<p>Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of using partial correlation is to show the relation between two variables after “adjusting” f...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2025
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| الموضوعات: | |
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| _version_ | 1852018766858158080 |
|---|---|
| author | Zeyuan Song (13846951) |
| author2 | Sophia Gunn (18452454) Stefano Monti (171969) Gina M. Peloso (12036929) Ching-Ti Liu (100170) Kathryn Lunetta (3721699) Paola Sebastiani (32655) |
| author2_role | author author author author author author |
| author_facet | Zeyuan Song (13846951) Sophia Gunn (18452454) Stefano Monti (171969) Gina M. Peloso (12036929) Ching-Ti Liu (100170) Kathryn Lunetta (3721699) Paola Sebastiani (32655) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zeyuan Song (13846951) Sophia Gunn (18452454) Stefano Monti (171969) Gina M. Peloso (12036929) Ching-Ti Liu (100170) Kathryn Lunetta (3721699) Paola Sebastiani (32655) |
| dc.date.none.fl_str_mv | 2025-07-03T10:30:23Z |
| dc.identifier.none.fl_str_mv | 10.3389/fsysb.2025.1589079.s010 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Image_3_Learning_Gaussian_graphical_models_from_correlated_data_tiff/29468441 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Systems Biology Gaussian graphical models corelated data bootstrap polygenic risk score partial correlation |
| dc.title.none.fl_str_mv | Image 3_Learning Gaussian graphical models from correlated data.tiff |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of using partial correlation is to show the relation between two variables after “adjusting” for the effects of other variables and leads to more parsimonious and interpretable models. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error. In this paper, we propose a cluster-based bootstrap algorithm to infer GGMs from correlated data. We use extensive simulations of correlated data from family-based studies to show that the proposed bootstrap method does not inflate the Type I error while retaining statistical power compared to alternative solutions when there are sufficient number of clusters. We apply our method to learn the Gaussian Graphic Model that represents complex relations between 47 Polygenic Risk Scores generated using genome-wide genotype data from the Long Life Family Study. By comparing it to the conventional methods that ignore within-cluster correlation, we show that our method controls the Type I error well without power loss.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_7ecdcc69e1e5d8afb6d459ef838fc5a0 |
| identifier_str_mv | 10.3389/fsysb.2025.1589079.s010 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29468441 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Image 3_Learning Gaussian graphical models from correlated data.tiffZeyuan Song (13846951)Sophia Gunn (18452454)Stefano Monti (171969)Gina M. Peloso (12036929)Ching-Ti Liu (100170)Kathryn Lunetta (3721699)Paola Sebastiani (32655)Systems BiologyGaussian graphical modelscorelated databootstrappolygenic risk scorepartial correlation<p>Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of using partial correlation is to show the relation between two variables after “adjusting” for the effects of other variables and leads to more parsimonious and interpretable models. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error. In this paper, we propose a cluster-based bootstrap algorithm to infer GGMs from correlated data. We use extensive simulations of correlated data from family-based studies to show that the proposed bootstrap method does not inflate the Type I error while retaining statistical power compared to alternative solutions when there are sufficient number of clusters. We apply our method to learn the Gaussian Graphic Model that represents complex relations between 47 Polygenic Risk Scores generated using genome-wide genotype data from the Long Life Family Study. By comparing it to the conventional methods that ignore within-cluster correlation, we show that our method controls the Type I error well without power loss.</p>2025-07-03T10:30:23ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.3389/fsysb.2025.1589079.s010https://figshare.com/articles/figure/Image_3_Learning_Gaussian_graphical_models_from_correlated_data_tiff/29468441CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294684412025-07-03T10:30:23Z |
| spellingShingle | Image 3_Learning Gaussian graphical models from correlated data.tiff Zeyuan Song (13846951) Systems Biology Gaussian graphical models corelated data bootstrap polygenic risk score partial correlation |
| status_str | publishedVersion |
| title | Image 3_Learning Gaussian graphical models from correlated data.tiff |
| title_full | Image 3_Learning Gaussian graphical models from correlated data.tiff |
| title_fullStr | Image 3_Learning Gaussian graphical models from correlated data.tiff |
| title_full_unstemmed | Image 3_Learning Gaussian graphical models from correlated data.tiff |
| title_short | Image 3_Learning Gaussian graphical models from correlated data.tiff |
| title_sort | Image 3_Learning Gaussian graphical models from correlated data.tiff |
| topic | Systems Biology Gaussian graphical models corelated data bootstrap polygenic risk score partial correlation |