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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
The Cancer Genome Atlas. https://cancergenome.nih.gov/
International Cancer Genome Consortium. http://icgc.org/
GWAS Catalog. https://www.ebi.ac.uk/gwas/
Wang, K., et al.: Genome-wide identification of post-translational modulators of transcription factor activity in human B cells. Nat. Biotechnol. 27(9), 829–39 (2009)
Babur, Ö., et al.: Discovering modulators of gene expression. Nucl. Acids Res. 38(17), 5648–56 (2010)
Hansen, M., et al.: Mimosa: mixture model of co-expression to detect modulators of regulatory interaction. Algorithms Mol. Biol. 5, 4 (2010)
Alvarez, M.J., et al.: Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48(8), 838–47 (2016)
Fazlollahi, M., et al.: Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets. Proc. Natl. Acad. Sci. U. S. A. 113(13), E1835–43 (2016)
Hsiao, T.H., et al.: Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers. Sci. Rep. 6, 23035 (2016)
Székely, G.J., et al.: Measuring and testing dependence by correlation of distances. Ann. Statist. 35(6), 2769–2794 (2007)
Székely, G.J., Rizzo, M.L.: Brownian distance covariance. Ann. Appl. Stat. 3(4), 1236–1265 (2009)
Nadaraya, E.A.: On estimating regression. Theory Probab. Appl. 9(1), 141–142 (1964)
Watson, G.S.: Smooth regression analysis. Indian J. Statist. Ser. A 26(4), 359–372 (1964)
Knijnenburg, T.A., et al.: Fewer permutations, more accurate P-values. Bioinformatics 25(12), i161–i168 (2009)
Matsui, M., et al.: D3M: detection of differential distributions of methylation patterns. Bioinformatics 32(15), 2248–2255 (2015)
Simon, N., Tibshirani, R.: Comment on “detecting novel associations in large data sets”. Science 334(6062), 1518–1524 (2011)
The Broad GDAC Firehose. http://gdac.broadinstitute.org/
Ingenuity Knowledge Base. https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/
Maxwell, P.H., et al.: The tumour suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis. Nature 399(6733), 271–275 (1999)
Kapur, P., et al.: Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation. Lancet Oncol. 14(2), 159–167 (2013)
Bregarolas, J.: Molecular genetics of clear-cell renal cell carcinoma. J. Clin. Oncol. 32(18), 1968–1976 (2014)
The Library of Integrated Cellular Signatures. http://www.lincsproject.org/
Lokody, I.: Signalling: FOXM1 and CENPF: co-pilots driving prostate cancer. Nat. Rev. Cancer 14(7), 450–451 (2014)
Efron, B., Tibshirani, R.: On testing the significance of sets of genes. Ann. Appl. Stat. 1(1), 107–129 (2007)
Wikipedia. https://en.wikipedia.org/wiki/Vorinostat
Bulter, L.M., et al.: Suberoylanilide hydroxamic acid, an inhibitor of histone deacetylase, suppresses the growth of prostate cancer cells in vitro and in vivo. Cancer Res. 60, 5165–5170 (2000)
Yang, H., et al.: The tumor proteasome is a primary target for the natural anticancer compound Withaferin A isolated for “Indian winter cherry”. Mol. Pharmacol. 71, 426–437 (2007)
Lian, F., et al.: The biology of castration-resistant prostate cancer. Curr. Probl. Cancer 39(1), 17–28 (2015)
Hong, S.W., et al.: NVP-BEZ235, a dual PI3K/mTOR inhibitor, induces cell death through alternate routes in prostate cancer cells depending on the PTEN genotype. Apoptosis 19(5), 895–904 (2014)
Nakabayashi, M., et al.: Phase II trial of RAD001 and bicalutamide for castration-resistant prostate cancer. BJU Int. 110(11), 1729–1735 (2012)
Templeton, A.J., et al.: Phase 2 trial of single-agent everolimus in chemotherapy-naive patients with castration-resistant prostate cancer (SAKK 08/08). Eur. Urol. 64(1), 150–158 (2013)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-14160-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14159-2
Online ISBN: 978-3-030-14160-8
eBook Packages: Computer ScienceComputer Science (R0)