Identification of perturbed signaling pathways from gene expression data using information divergence
Xinying Hu1, Hang Wei, Haoran Zheng
1School of Computer Science and Technology, University of Science and Technology of China, Hefei, People's Republic of China. hrzheng@ustc.edu.cn.
Abstract:
Abnormal regulation of signaling pathways is the key causative factor in several diseases. Although many methods have been proposed to identify significantly differential pathways between two conditions via microarray gene expression datasets, most of them concentrate on differences in the pathway components-either the differential expression or the correlation of genes in a given pathway. However, as biological functional units, signaling pathways may have diverse activity patterns across different biological contexts. In order to detect overall changes in pathways, we propose an analysis model called SPAID (Signaling Pathway Analysis based on Information Divergence). SPAID is based on the concept of information divergence, which can be used to compare two conditions by computing the differential probability distribution of the regulation capacity. We compared SPAID with several classical algorithms using different datasets, and the results indicate that SPAID produces higher repeatability, has better performance and universality, and extracts more comprehensive information regarding the underlying biological processes. In conclusion, by introducing the idea of information divergence, our study measures differences in pathways from an overall perspective and will provide a complementary analysis framework for pathway analysis.
Insights
This study introduces SPAID, a novel method using information divergence to analyze overall signaling pathway activity. SPAID offers improved repeatability and comprehensive insights into disease-related biological processes.
Area of Science:
- Bioinformatics
- Systems Biology
- Computational Biology
Background:
- Abnormal signaling pathway regulation is implicated in various diseases.
- Existing methods often focus on individual gene expression or correlations within pathways, not overall pathway activity.
Purpose of the Study:
- To develop a new analysis model, SPAID (Signaling Pathway Analysis based on Information Divergence), for detecting overall changes in signaling pathways.
- To provide a more comprehensive framework for pathway analysis in different biological contexts.
Main Methods:
- SPAID utilizes information divergence to compare conditions by assessing differential probability distributions of pathway regulation capacity.
- The model was evaluated against classical algorithms using diverse microarray gene expression datasets.
Main Results:
- SPAID demonstrated higher repeatability and better overall performance compared to existing methods.
- The analysis extracted more comprehensive information regarding underlying biological processes.
- SPAID showed improved universality across different datasets.
Conclusions:
- Information divergence offers a novel perspective for measuring pathway differences holistically.
- SPAID provides a complementary analysis framework for understanding signaling pathway alterations in disease.
- The approach enhances the detection of functional pathway changes beyond individual gene metrics.
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