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Saha E, Fanfani V, Mandros P, Ben Guebila M, Fischer J, Shutta KH, DeMeo DL, Lopes-Ramos CM, Quackenbush J. Bayesian inference of sample-specific coexpression networks. Genome Res 2024; 34:1397-1410. [PMID: 39134413 DOI: 10.1101/gr.279117.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 07/31/2024] [Indexed: 08/28/2024]
Abstract
Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression networks is a critical element of GRN inference, as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate coexpression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. Bayesian optimized networks obtained by assimilating omic data (BONOBO) is a scalable Bayesian model for deriving individual sample-specific coexpression matrices that recognizes variations in molecular interactions across individuals. For each sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific coexpression matrix constructed from all other samples in the data. Combining the sample-specific gene coexpression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific coexpression matrices, thus allowing the analysis of large data sets. We demonstrate BONOBO's utility in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, the prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other methods that have been used for sample-specific coexpression network inference and provides insight into individual differences in the drivers of biological processes.
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Affiliation(s)
- Enakshi Saha
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Viola Fanfani
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Panagiotis Mandros
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Marouen Ben Guebila
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Jonas Fischer
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Katherine H Shutta
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Camila M Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA;
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA
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Saha E, Fanfani V, Mandros P, Ben-Guebila M, Fischer J, Hoff-Shutta K, Glass K, DeMeo DL, Lopes-Ramos C, Quackenbush J. Bayesian Optimized sample-specific Networks Obtained By Omics data (BONOBO). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.16.567119. [PMID: 38014256 PMCID: PMC10680741 DOI: 10.1101/2023.11.16.567119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring co-expression networks is a critical element of GRN inference as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate co-expression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. To address these concerns, we introduce BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data), a scalable Bayesian model for deriving individual sample-specific co-expression networks by recognizing variations in molecular interactions across individuals. For every sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific co-expression matrix constructed from all other samples in the data. Combining the sample-specific gene expression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices, thus making the method extremely scalable. We demonstrate the utility of BONOBO in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other sample-specific co-expression network inference methods and provides insight into individual differences in the drivers of biological processes.
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Affiliation(s)
- Enakshi Saha
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Viola Fanfani
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Panagiotis Mandros
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Marouen Ben-Guebila
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Jonas Fischer
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Katherine Hoff-Shutta
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Kimberly Glass
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Dawn Lisa DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Camila Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
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