Abstract
Bisulfite sequencing (BS-seq) is a popular method for measuring DNA methylation in basepair-resolution. Many BS-seq data analysis tools utilize the assumption of spatial correlation among the neighboring cytosines’ methylation states. While being a fair assumption, most existing methods leave out the possibility of deviation from the spatial correlation pattern. Our approach builds on a method which combines a generalized linear mixed model (GLMM) with a likelihood that is specific for BS-seq data and that incorporates a spatial correlation for methylation levels. We propose a novel technique using a sparsity promoting prior to enable cytosines deviating from the spatial correlation pattern. The method is tested with both simulated and real BS-seq data and compared to other differential methylation analysis tools.
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
Äijö, T., Yue, X., Rao, A., Lähdesmäki, H.: LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs. Bioinformatics 32(17), i511–i519 (2016)
Carvalho, C.M., Polson, N.G., Scott, J.G.: Handling sparsity via the horseshoe. In: Artificial Intelligence and Statistics, pp. 73–80 (2009)
Dolzhenko, E., Smith, A.D.: Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments. BMC Bioinform. 15(1), 215 (2014)
Domcke, S., Bardet, A.F., Ginno, P.A., Hartl, D., Burger, L., Schübeler, D.: Competition between dna methylation and transcription factors determines binding of NRF1. Nature 528(7583), 575 (2015)
Halla-aho, V., Lähdesmäki, H.: LuxUS: detecting differential DNA methylation using generalized linear mixed model with spatial correlation structure. bioRxiv, p. 536722 (2019)
Hansen, K.D.: bsseqData: example whole genome bisulfite data for the bsseq package. R package version 0.12.0. (2016)
Hebestreit, K., Dugas, M., Klein, H.U.: Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 29(13), 1647–1653 (2013)
Mayo, T.R., Schweikert, G., Sanguinetti, G.: M3D: a kernel-based test for spatially correlated changes in methylation profiles. Bioinformatics 31(6), 809–816 (2014)
Piironen, J., Vehtari, A.: On the hyperprior choice for the global shrinkage parameter in the horseshoe prior. arXiv preprint arXiv:1610.05559 (2016)
Acknowlegements
The calculations presented above were performed using computer resources within the Aalto University School of Science “Science-IT” project.
Funding
This work has been supported by the Academy of Finland (project numbers: 292660 and 314445).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Halla-aho, V., Lähdesmäki, H. (2020). LuxHS: DNA Methylation Analysis with Spatially Varying Correlation Structure. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-45385-5_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45384-8
Online ISBN: 978-3-030-45385-5
eBook Packages: Computer ScienceComputer Science (R0)