Robustness evaluations of pathway activity inference methods on gene expression data
Background: With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
BMC
2024
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/10952/1/J17448_cf6902c993cf26570c682ba1f102f077.pdf http://eprints.uthm.edu.my/10952/ https://doi.org/10.1186/s12859-024-05632-w |
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| Summary: | Background: With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed
the topic from diferent aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches.
Results: Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The frst assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identifed informative pathways and genes were evaluated. Based on the frst assessment, the mean
reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk
(e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment
shows that no methods provide satisfactory results across datasets.
Conclusion: However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared
to non-TB methods. |
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