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DISCO-SCA and properly applied GSVD as swinging methods to find common and distinctive processes
Katrijn Van Deun1, Iven Van Mechelen, Lieven Thorrez
1Department of Psychology, Katholieke Universiteit Leuven, Leuven, Belgium. katrijn.vandeun@ppw.kuleuven.be
Plos One
|June 14, 2012
Summary
Integrating multi-source biological data is challenging. DISCO-SCA effectively identifies common and distinct biological processes, outperforming standard GSVD. Properly adapted GSVD also shows promise for data integration.
Area of Science:
- Systems Biology
- Bioinformatics
- Genomics
- Metabolomics
Background:
- Integrating data from multiple sources is crucial in systems biology.
- A key challenge is distinguishing common biological processes from source-specific ones.
- Generalized Singular Value Decomposition (GSVD) and DISCO-SCA are emerging methods for this task.
Purpose of the Study:
- To compare the effectiveness of GSVD and DISCO-SCA for multi-source data integration.
- To identify common and distinctive biological processes within integrated datasets.
- To provide insights into the application of these methods in comparative genomics and metabolomics.
Main Methods:
- Simultaneous Component Analysis with rotation to common and distinctive components (DISCO-SCA).
- Generalized Singular Value Decomposition (GSVD) with pre-processing and algorithmic adaptations.
- Application of Gene Set Enrichment Analysis for biological annotation.
Main Results:
- DISCO-SCA demonstrates strong performance in identifying common and distinct biological processes.
- Standard GSVD applications yield unsatisfactory results, but adapted GSVD achieves comparable performance to DISCO-SCA.
- DISCO-SCA successfully identified cell cycle and pheromone response themes in comparative genomics and platform-specific metabolites in metabolomics.
Conclusions:
- Both DISCO-SCA and properly applied GSVD are effective for integrating multi-source biological data.
- These methods can successfully disentangle common and distinctive biological processes.
- Open-source code is available for both DISCO-SCA and GSVD.
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