Addressing dispersion in mis‐measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data
Fuente:
Wiley Open Access
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , |
|---|---|
| Format: | Artículo Open Access |
| Veröffentlicht: |
Wiley
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1867014197095694336 |
|---|---|
| author | Kaiqiong Zhao Karim Oualkacha Yixiao Zeng Cathy Shen Kathleen Klein Lajmi Lakhal‐Chaieb Aurélie Labbe Tomi Pastinen Marie Hudson Inés Colmegna Sasha Bernatsky Celia M. T. Greenwood |
| author_facet | Kaiqiong Zhao Karim Oualkacha Yixiao Zeng Cathy Shen Kathleen Klein Lajmi Lakhal‐Chaieb Aurélie Labbe Tomi Pastinen Marie Hudson Inés Colmegna Sasha Bernatsky Celia M. T. Greenwood Kaiqiong Zhao Karim Oualkacha Yixiao Zeng Cathy Shen Kathleen Klein Lajmi Lakhal‐Chaieb Aurélie Labbe Tomi Pastinen Marie Hudson Inés Colmegna Sasha Bernatsky Celia M. T. Greenwood |
| collection | Wiley Open Access |
| contents | Addressing dispersion in mis‐measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data Kaiqiong Zhao Karim Oualkacha Yixiao Zeng Cathy Shen Kathleen Klein Lajmi Lakhal‐Chaieb Aurélie Labbe Tomi Pastinen Marie Hudson Inés Colmegna Sasha Bernatsky Celia M. T. Greenwood Statistics in Medicine Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra‐parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non‐constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi‐binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace‐approximated quasi‐likelihood of our model, we further develop a specialized two‐stage expectation‐maximization (EM) algorithm, where a plug‐in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non‐zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti‐citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA‐related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called “SOMNiBUS.” 10.1002/sim.10149 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| doi_str_mv | 10.1002/sim.10149 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_10149 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Addressing dispersion in mis‐measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data Kaiqiong Zhao Karim Oualkacha Yixiao Zeng Cathy Shen Kathleen Klein Lajmi Lakhal‐Chaieb Aurélie Labbe Tomi Pastinen Marie Hudson Inés Colmegna Sasha Bernatsky Celia M. T. Greenwood Statistics in Medicine Addressing dispersion in mis‐measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data Kaiqiong Zhao Karim Oualkacha Yixiao Zeng Cathy Shen Kathleen Klein Lajmi Lakhal‐Chaieb Aurélie Labbe Tomi Pastinen Marie Hudson Inés Colmegna Sasha Bernatsky Celia M. T. Greenwood Statistics in Medicine Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra‐parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non‐constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi‐binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace‐approximated quasi‐likelihood of our model, we further develop a specialized two‐stage expectation‐maximization (EM) algorithm, where a plug‐in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non‐zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti‐citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA‐related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called “SOMNiBUS.” 10.1002/sim.10149 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| title | Addressing dispersion in mis‐measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.10149 |