Dataset Information

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 (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.

SUBMITTER: Hui TX

PROVIDER: S-EPMC10785356 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Robustness evaluations of pathway activity inference methods on gene expression data.

Hui Tay Xin TX Kasim Shahreen S Aziz Izzatdin Abdul IA Fudzee Mohd Farhan Md MFM Haron Nazleeni Samiha NS Sutikno Tole T Hassan Rohayanti R Mahdin Hairulnizam H Sen Seah Choon SC

BMC bioinformatics 20240112 1

<h4>Background</h4>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 robu ...[more]

PMID: 38216898

Similar Datasets

Project description:BackgroundMicroarray experiments produce expression measurements in genomic scale. A way to derive functional understanding of the data is to focus on functional sets of genes, such as pathways, instead of individual genes. While a common practice for the pathway-level analysis has been functional enrichment analysis such as over-representation analysis and gene set enrichment analysis, an alternative approach has also been explored. In this approach, gene expression data are first aggregated at pathway level to transform the original data into a compact representation in which each row corresponds to a pathway instead of a gene. Thereafter the pathway expression data can be used for differential expression and classification analyses in pathway space, leveraging existing algorithms usually applied to gene expression data. While several studies have proposed the pathway-level aggregation methods, it remains unclear how they compare with one another, since the evaluations were done to a limited extent. Thus this study presents a comprehensive evaluation of six most prominent aggregation methods.ResultsThe compared methods include five existing methods--mean of all member genes (Mean all), mean of condition-responsive genes (Mean CORGs), analysis of sample set enrichment scores (ASSESS), principal component analysis (PCA), and partial least squares (PLS)--and a variant of an existing method (Mean top 50%, averaging top half of member genes). Comprehensive and stringent benchmarking was performed by collecting seven pairs of related but independent datasets encompassing various phenotypes. Aggregation was done in the space of KEGG pathways. Performance of the methods was assessed by classification accuracy validated both internally and externally, and by examining the correlative extent of pathway signatures between the dataset pairs. The assessment revealed that (i) the best accuracy and correlation were obtained from ASSESS and Mean top 50%, (ii) Mean all showed the lowest accuracy, and (iii) Mean CORGs and PLS gave rise to the largest extent of discordance in the pathway signature correlation.ConclusionsThe two best performing method (ASSESS and Mean top 50%) are suggested to be preferred. The benchmarking analysis also suggests that there is both room and necessity for developing a novel method for pathway-level aggregation.

Dataset Information

Robustness evaluations of pathway activity inference methods on gene expression data.

Background

Results

Conclusion

Publications

Robustness evaluations of pathway activity inference methods on gene expression data.

Similar Datasets

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