AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs
Abstract Background Multi-omic studies provide comprehensive insight into biological systems by evaluating cellular changes between normal and pathological conditions at multiple levels of measurement. Biological networks, which represent interactions or associations between biomolecules, have been...
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BMC
2025-02-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06063-x |
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| author | Samuel S. Boyd Chad Slawson Jeffrey A. Thompson |
| author_facet | Samuel S. Boyd Chad Slawson Jeffrey A. Thompson |
| author_sort | Samuel S. Boyd |
| collection | DOAJ |
| description | Abstract Background Multi-omic studies provide comprehensive insight into biological systems by evaluating cellular changes between normal and pathological conditions at multiple levels of measurement. Biological networks, which represent interactions or associations between biomolecules, have been highly effective in facilitating omic analysis. However, current network-based methods lack generalizability to accommodate multiple data types across a range of diverse experiments. Results We present AMEND 2.0, an updated active module identification method which can analyze multiplex and/or heterogeneous networks integrated with multi-omic data in a highly generalizable framework, in contrast to existing methods, which are mostly appropriate for at most two specific omic types. It is powered by Random Walk with Restart for multiplex-heterogeneous networks, with additional capabilities including degree bias adjustment and biased random walk for multi-objective module identification. AMEND was applied to two real-world multi-omic datasets: renal cell carcinoma data from The cancer genome atlas and an O-GlcNAc Transferase knockout study. Additional analyses investigate the performance of various subroutines of AMEND on tasks of node ranking and degree bias adjustment. Conclusions While the analysis of multi-omic datasets in a network context is poised to provide deeper understanding of health and disease, new methods are required to fully take advantage of this increasingly complex data. The current study combines several network analysis techniques into a single versatile method for analyzing biological networks with multi-omic data that can be applied in many diverse scenarios. Software is freely available in the R programming language at https://github.com/samboyd0/AMEND . |
| format | Article |
| id | doaj-art-d3f37656e765468e9523421a576316c2 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-d3f37656e765468e9523421a576316c22025-02-09T12:57:01ZengBMCBMC Bioinformatics1471-21052025-02-0126112910.1186/s12859-025-06063-xAMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphsSamuel S. Boyd0Chad Slawson1Jeffrey A. Thompson2Department of Biostatistics and Data Science, University of Kansas Medical CenterDepartment of Biochemistry, University of Kansas Medical CenterDepartment of Biostatistics and Data Science, University of Kansas Medical CenterAbstract Background Multi-omic studies provide comprehensive insight into biological systems by evaluating cellular changes between normal and pathological conditions at multiple levels of measurement. Biological networks, which represent interactions or associations between biomolecules, have been highly effective in facilitating omic analysis. However, current network-based methods lack generalizability to accommodate multiple data types across a range of diverse experiments. Results We present AMEND 2.0, an updated active module identification method which can analyze multiplex and/or heterogeneous networks integrated with multi-omic data in a highly generalizable framework, in contrast to existing methods, which are mostly appropriate for at most two specific omic types. It is powered by Random Walk with Restart for multiplex-heterogeneous networks, with additional capabilities including degree bias adjustment and biased random walk for multi-objective module identification. AMEND was applied to two real-world multi-omic datasets: renal cell carcinoma data from The cancer genome atlas and an O-GlcNAc Transferase knockout study. Additional analyses investigate the performance of various subroutines of AMEND on tasks of node ranking and degree bias adjustment. Conclusions While the analysis of multi-omic datasets in a network context is poised to provide deeper understanding of health and disease, new methods are required to fully take advantage of this increasingly complex data. The current study combines several network analysis techniques into a single versatile method for analyzing biological networks with multi-omic data that can be applied in many diverse scenarios. Software is freely available in the R programming language at https://github.com/samboyd0/AMEND .https://doi.org/10.1186/s12859-025-06063-xBiological networksActive module identificationMulti-omic data integration |
| spellingShingle | Samuel S. Boyd Chad Slawson Jeffrey A. Thompson AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs BMC Bioinformatics Biological networks Active module identification Multi-omic data integration |
| title | AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs |
| title_full | AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs |
| title_fullStr | AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs |
| title_full_unstemmed | AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs |
| title_short | AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs |
| title_sort | amend 2 0 module identification and multi omic data integration with multiplex heterogeneous graphs |
| topic | Biological networks Active module identification Multi-omic data integration |
| url | https://doi.org/10.1186/s12859-025-06063-x |
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