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. 2024 Jan 12;25(1):23.
doi: 10.1186/s12859-024-05632-w.

Robustness evaluations of pathway activity inference methods on gene expression data

Tay Xin Hui  1 , Shahreen Kasim  2 , Izzatdin Abdul Aziz  3 , Mohd Farhan Md Fudzee  1 , Nazleeni Samiha Haron  3 , Tole Sutikno  4 , Rohayanti Hassan  5 , Hairulnizam Mahdin  1 , Seah Choon Sen  6
Affiliations

Robustness evaluations of pathway activity inference methods on gene expression data

Tay Xin Hui et al. BMC Bioinformatics. .

Abstract

Background: With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. 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 different 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 first 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 identified informative pathways and genes were evaluated. Based on the first 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.

Keywords: Cancer classification; Literature validation; Pathway activity inference; Pathway analysis; PubMed text data mining; Reproducibility power; Robustness.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Comparison of mean reproducibility power for seven pathway activity inference methods
Fig. 2
Fig. 2
Coefficient variation of pathway activity inference methods
Fig. 3
Fig. 3
Comparison of number of identified informative pathways
Fig. 4
Fig. 4
Comparison of number of identified informative genes
Fig. 5
Fig. 5
PubMed text data mining automation based on pathways and genes [52]
Fig. 6
Fig. 6
Workflow of evaluating pathway activity inference methods based on the robustness of pathway activity
Fig. 7
Fig. 7
Workflow of evaluating pathway activity inference methods based on the robustness of predicted risk-active pathways and genes
See this image and copyright information in PMC

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