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Enlightening discriminative network functional modules behind Principal Component Analysis separation in
Sara Ciucci1,2, Yan Ge1, Claudio Durán1
1Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Department of Physics, Technische Universität Dresden, Tatzberg 47/49, 01307 Dresden, Germany.
Scientific Reports
|March 14, 2017
Summary
Principal Component Analysis (PCA) reveals sample patterns in omic data. Our new PC-corr algorithm identifies key features driving PCA separation, aiding biomarker discovery in systems biology and precision medicine.
Area of Science:
- Systems Biology
- Precision Medicine
- Bioinformatics
Background:
- Omic science generates complex datasets.
- Principal Component Analysis (PCA) is widely used for pattern exploration in omics.
- A gap exists in visualizing features driving PCA-driven sample separation.
Purpose of the Study:
- To introduce PC-corr, a novel algorithm for feature network inference from PCA.
- To provide an interpretable method for identifying key omic features contributing to sample segregation.
- To facilitate the discovery of multiscale biomarkers from complex omic data.
Main Methods:
- Developed PC-corr, a simple algorithm linking PCA segregation to feature networks.
- Applied PC-corr to diverse omic datasets including lipidomics, metagenomics, and cancer genomics.
- Validated the algorithm's efficacy across multiple biological domains.
Main Results:
- PC-corr successfully generates discriminative feature networks associated with PCA results.
- The algorithm effectively highlights functional modules within omic data.
- Demonstrated utility in lipidomic, metagenomic, genomic, promoteromic, and mechanomic data.
Conclusions:
- PC-corr offers an interpretable approach to understanding PCA in omics.
- The algorithm aids in identifying combinatorial and multiscale biomarkers.
- PC-corr is a generalizable network inference tool applicable to big data and complex systems.
