Conclusion
We developed PathActMarker, an R package for inferring pathway activity of complex diseases. The package integrates widely used normalization methods for gene expression data and provides pathway data from six sources. Meanwhile, eight state-of-the-art tools can be used to convert the high-dimensional gene expression data into a biologically interpretable low-dimensional pathway activity matrix, and extensive evaluations are also included to measure the performance of these tools. The package also contains functions to identify important pathways as biomarkers based on statistical and machine learning algorithms, and provides a set of functions for interpretation and analysis.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grant No. 62202383), Guangdong Basic and Applied Basic Research Foundation (No. 2024A1515012602), and the National Key Research and Development Program of China (No. 2022YFD1801200).
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Li, X., Hao, J., Zhao, Z. et al. PathActMarker: an R package for inferring pathway activity of complex diseases. Front. Comput. Sci. 19, 193908 (2025). https://doi.org/10.1007/s11704-024-40420-y
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DOI: https://doi.org/10.1007/s11704-024-40420-y