Abstract
The regulation of transcription factor activity dynamically changes across cellular conditions and disease subtypes. The identification of biological modulators contributing to context-specific gene regulation is one of the challenging tasks in systems biology, which is necessary to understand and control cellular responses across different genetic backgrounds and environmental conditions. Previous approaches for identifying biological modulators from gene expression data were restricted to the capturing of a particular type of a three-way dependency among a regulator, its target gene, and a modulator; these methods cannot describe the complex regulation structure, such as when multiple regulators, their target genes, and modulators are functionally related. Here, we propose a statistical method for identifying biological modulators by capturing multivariate local dependencies, based on energy statistics, which is a class of statistics based on distances. Subsequently, our method assigns a measure of statistical significance to each candidate modulator through a permutation test. We compared our approach with that of a leading competitor for identifying modulators, and illustrated its performance through both simulations and real data analysis. Our method, entitled genome-wide identification of modulators using local energy statistical test (GIMLET), is implemented with R (\(\ge \)3.2.2) and is available from github (https://github.com/tshimam/GIMLET).
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Acknowledgement
This work was supported by JSPS Grant-in-Aid for Challenging Exploratory Research (15K12139), JSPS Grant-in-Aid for Young Scientists A (15H05325), and JSPS Grant-in-Aid for Scientific Research on Innovative Areas (15H05912 and 18H04798). It was also supported in part by Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan as a social and scientific priority issue (Integrated computational life science to support personalized and preventive medicine; hp170227, hp180198) to be tackled by using post-K computer. The super-computing resources were provided by Human Genome Center, University of Tokyo.
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Shimamura, T., Matsui, Y., Kajino, T., Ito, S., Takahashi, T., Miyano, S. (2019). GIMLET: Identifying Biological Modulators in Context-Specific Gene Regulation Using Local Energy Statistics. In: Bartoletti, M., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2017. Lecture Notes in Computer Science(), vol 10834. Springer, Cham. https://doi.org/10.1007/978-3-030-14160-8_13
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DOI: https://doi.org/10.1007/978-3-030-14160-8_13
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