Robustness evaluations of pathway activity inference methods on gene expression data
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 (no...
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-05632-w |
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| author | Tay Xin Hui Shahreen Kasim Izzatdin Abdul Aziz Mohd Farhan Md Fudzee Nazleeni Samiha Haron Tole Sutikno Rohayanti Hassan Hairulnizam Mahdin Seah Choon Sen |
| author_facet | Tay Xin Hui Shahreen Kasim Izzatdin Abdul Aziz Mohd Farhan Md Fudzee Nazleeni Samiha Haron Tole Sutikno Rohayanti Hassan Hairulnizam Mahdin Seah Choon Sen |
| author_sort | Tay Xin Hui |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T14:12:08Z |
| format | Article |
| id | doaj.art-63e7d008de894a00913f61666e7ae209 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-08T14:12:08Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-63e7d008de894a00913f61666e7ae2092024-01-14T12:38:45ZengBMCBMC Bioinformatics1471-21052024-01-0125112410.1186/s12859-024-05632-wRobustness evaluations of pathway activity inference methods on gene expression dataTay Xin Hui0Shahreen Kasim1Izzatdin Abdul Aziz2Mohd Farhan Md Fudzee3Nazleeni Samiha Haron4Tole Sutikno5Rohayanti Hassan6Hairulnizam Mahdin7Seah Choon Sen8Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM)Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM)Computer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS (UTP)Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM)Computer and Information Sciences Department (CISD), Universiti Teknologi PETRONAS (UTP)Department of Electrical Engineering, Universitas Ahmad Dahlan (UAD)Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM)Soft Computing and Data Mining Center, Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM)Faculty of Computing, Universiti Teknologi Malaysia (UTM)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.https://doi.org/10.1186/s12859-024-05632-wPathway analysisReproducibility powerRobustnessPubMed text data miningLiterature validationPathway activity inference |
| spellingShingle | Tay Xin Hui Shahreen Kasim Izzatdin Abdul Aziz Mohd Farhan Md Fudzee Nazleeni Samiha Haron Tole Sutikno Rohayanti Hassan Hairulnizam Mahdin Seah Choon Sen Robustness evaluations of pathway activity inference methods on gene expression data BMC Bioinformatics Pathway analysis Reproducibility power Robustness PubMed text data mining Literature validation Pathway activity inference |
| title | Robustness evaluations of pathway activity inference methods on gene expression data |
| title_full | Robustness evaluations of pathway activity inference methods on gene expression data |
| title_fullStr | Robustness evaluations of pathway activity inference methods on gene expression data |
| title_full_unstemmed | Robustness evaluations of pathway activity inference methods on gene expression data |
| title_short | Robustness evaluations of pathway activity inference methods on gene expression data |
| title_sort | robustness evaluations of pathway activity inference methods on gene expression data |
| topic | Pathway analysis Reproducibility power Robustness PubMed text data mining Literature validation Pathway activity inference |
| url | https://doi.org/10.1186/s12859-024-05632-w |
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