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Exploring the complexity of pathway-drug relationships using latent Dirichlet allocation
Naruemon Pratanwanich1, Pietro Lio1
1Computer Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0FD, United Kingdom.
Computational Biology and Chemistry
|September 15, 2014
Summary
We developed a new pathway-based Latent Dirichlet Allocation (LDA) method to analyze gene expression data. This approach effectively identifies perturbed biological pathways in response to drug treatments, improving upon existing methods.
Area of Science:
- Genomics
- Computational Biology
- Systems Biology
Background:
- High-throughput microarray data reveal differentially expressed genes under various conditions.
- Analyzing long lists of genes requires methods to extract biological meaning and identify functional relationships.
Purpose of the Study:
- To present a novel pathway-based Latent Dirichlet Allocation (LDA) method for analyzing gene expression data.
- To identify biological pathways perturbed by chemical treatments using gene expression profiles.
Main Methods:
- Implemented a probabilistic Latent Dirichlet Allocation (LDA) model incorporating known gene-pathway memberships as prior knowledge.
- Applied the pathway-based LDA model to gene expression data from 1169 drugs across 236 known pathways.
Main Results:
- The pathway-based LDA method demonstrated superior predictive performance in identifying pathway responsiveness to drug treatments compared to existing methods.
- The model revealed key genes contributing to each pathway through probabilistic gene distributions.
Conclusions:
- The developed pathway-based LDA method offers a robust approach for understanding genome-wide pathway complexity.
- This method provides a valuable tool for interpreting functional genomics data and drug responses.
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