Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms
Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/24/10/8591 |
| _version_ | 1797599899905163264 |
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| author | Anna Budkina Yulia A. Medvedeva Alexey Stupnikov |
| author_facet | Anna Budkina Yulia A. Medvedeva Alexey Stupnikov |
| author_sort | Anna Budkina |
| collection | DOAJ |
| description | Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods’ performance and DM signatures’ quality in the absence of gold standard data solving a long-existing problem in DM analysis. |
| first_indexed | 2024-03-11T03:40:51Z |
| format | Article |
| id | doaj.art-b68e9a5e36d34749aff885cd70951bf4 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-11T03:40:51Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-b68e9a5e36d34749aff885cd70951bf42023-11-18T01:38:24ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-05-012410859110.3390/ijms24108591Assessing the Differential Methylation Analysis Quality for Microarray and NGS PlatformsAnna Budkina0Yulia A. Medvedeva1Alexey Stupnikov2Department of Biomedical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, RussiaDepartment of Biomedical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, RussiaDepartment of Biomedical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, RussiaDifferential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods’ performance and DM signatures’ quality in the absence of gold standard data solving a long-existing problem in DM analysis.https://www.mdpi.com/1422-0067/24/10/8591differential methylationmicroarraysWGBSRRBSmethylation signaturerank statistic |
| spellingShingle | Anna Budkina Yulia A. Medvedeva Alexey Stupnikov Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms International Journal of Molecular Sciences differential methylation microarrays WGBS RRBS methylation signature rank statistic |
| title | Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms |
| title_full | Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms |
| title_fullStr | Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms |
| title_full_unstemmed | Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms |
| title_short | Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms |
| title_sort | assessing the differential methylation analysis quality for microarray and ngs platforms |
| topic | differential methylation microarrays WGBS RRBS methylation signature rank statistic |
| url | https://www.mdpi.com/1422-0067/24/10/8591 |
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