Change point detection for clustered expression data
Abstract Background To detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence betwe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-022-08680-9 |
| _version_ | 1811219087440740352 |
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| author | Miriam Sieg Lina Katrin Sciesielski Karin Michaela Kirschner Jochen Kruppa |
| author_facet | Miriam Sieg Lina Katrin Sciesielski Karin Michaela Kirschner Jochen Kruppa |
| author_sort | Miriam Sieg |
| collection | DOAJ |
| description | Abstract Background To detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence between the examined samples over time. In addition, however, the examinations were clustered at each time point by measuring littermates from relatively few mother mice at each developmental stage. As each examination was lethal, we had an independent data structure over the entire history, but a dependent data structure at a particular time point. Over the course of these historical data, we wanted to identify abrupt changes in the parameter of interest - change points. Results In this study, we demonstrated the application of generalized hypothesis testing using a linear mixed effects model as a possible method to detect change points. The coefficients from the linear mixed model were used in multiple contrast tests and the effect estimates were visualized with their respective simultaneous confidence intervals. The latter were used to determine the change point(s). In small simulation studies, we modelled different courses with abrupt changes and compared the influence of different contrast matrices. We found two contrasts, both capable of answering different research questions in change point detection: The Sequen contrast to detect individual change points and the McDermott contrast to find change points due to overall progression. We provide the R code for direct use with provided examples. The applicability of those tests for real experimental data was shown with in-vivo data from a preclinical study. Conclusion Simultaneous confidence intervals estimated by multiple contrast tests using the model fit from a linear mixed model were capable to determine change points in clustered expression data. The confidence intervals directly delivered interpretable effect estimates representing the strength of the potential change point. Hence, scientists can define biologically relevant threshold of effect strength depending on their research question. We found two rarely used contrasts best fitted for detection of a possible change point: the Sequen and McDermott contrasts. |
| first_indexed | 2024-04-12T07:19:32Z |
| format | Article |
| id | doaj.art-8e31d0ac53cd40cfbf12068bb0dee5d4 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-12T07:19:32Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-8e31d0ac53cd40cfbf12068bb0dee5d42022-12-22T03:42:22ZengBMCBMC Genomics1471-21642022-07-0123111610.1186/s12864-022-08680-9Change point detection for clustered expression dataMiriam Sieg0Lina Katrin Sciesielski1Karin Michaela Kirschner2Jochen Kruppa3Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Biometry and Clinical EpidemiologyCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of NeonatologyCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Translational PhysiologyHochschule Osnabrück - University of Applied SciencesAbstract Background To detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence between the examined samples over time. In addition, however, the examinations were clustered at each time point by measuring littermates from relatively few mother mice at each developmental stage. As each examination was lethal, we had an independent data structure over the entire history, but a dependent data structure at a particular time point. Over the course of these historical data, we wanted to identify abrupt changes in the parameter of interest - change points. Results In this study, we demonstrated the application of generalized hypothesis testing using a linear mixed effects model as a possible method to detect change points. The coefficients from the linear mixed model were used in multiple contrast tests and the effect estimates were visualized with their respective simultaneous confidence intervals. The latter were used to determine the change point(s). In small simulation studies, we modelled different courses with abrupt changes and compared the influence of different contrast matrices. We found two contrasts, both capable of answering different research questions in change point detection: The Sequen contrast to detect individual change points and the McDermott contrast to find change points due to overall progression. We provide the R code for direct use with provided examples. The applicability of those tests for real experimental data was shown with in-vivo data from a preclinical study. Conclusion Simultaneous confidence intervals estimated by multiple contrast tests using the model fit from a linear mixed model were capable to determine change points in clustered expression data. The confidence intervals directly delivered interpretable effect estimates representing the strength of the potential change point. Hence, scientists can define biologically relevant threshold of effect strength depending on their research question. We found two rarely used contrasts best fitted for detection of a possible change point: the Sequen and McDermott contrasts.https://doi.org/10.1186/s12864-022-08680-9Simultaneous confidence intervalsChange point detectionMultiple contrast testsLinear mixed modelsExpression analysis |
| spellingShingle | Miriam Sieg Lina Katrin Sciesielski Karin Michaela Kirschner Jochen Kruppa Change point detection for clustered expression data BMC Genomics Simultaneous confidence intervals Change point detection Multiple contrast tests Linear mixed models Expression analysis |
| title | Change point detection for clustered expression data |
| title_full | Change point detection for clustered expression data |
| title_fullStr | Change point detection for clustered expression data |
| title_full_unstemmed | Change point detection for clustered expression data |
| title_short | Change point detection for clustered expression data |
| title_sort | change point detection for clustered expression data |
| topic | Simultaneous confidence intervals Change point detection Multiple contrast tests Linear mixed models Expression analysis |
| url | https://doi.org/10.1186/s12864-022-08680-9 |
| work_keys_str_mv | AT miriamsieg changepointdetectionforclusteredexpressiondata AT linakatrinsciesielski changepointdetectionforclusteredexpressiondata AT karinmichaelakirschner changepointdetectionforclusteredexpressiondata AT jochenkruppa changepointdetectionforclusteredexpressiondata |