William Robert Paul Denault: Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis

William Robert Paul Denault (Department of Genetics and Bioinformatics, Norwegian Institute of Public Health) will give a talk on December 8th at 14:15 (held with restricted attendance in the Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor and streamed in Zoom - the link will be sent by mail one day in advance).

Title: Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis

Abstract: We present here a computational shortcut to improve a powerful wavelet-based method by Shim and Stephens (2015) called WaveQTL that was originally designed to identify DNase I hypersensitivity quantitative trait loci (dsQTL). WaveQTL relies on permutations to evaluate the significance of an association. To boost computational speed, we applied a recent method by Zhou and Guan (2017) for calculating the distribution of Bayes factors, which allowed the significance of an association to be estimated by simulations rather than permutations. We called this simulation-based approach ``fast functional wavelet" (FFW), and tested it on a publicly available DNA methylation (DNAm) dataset on colorectal cancer. Our simulations confirmed a substantial gain in computational speed compared to the permutation-based approach in WaveQTL. Furthermore, we show that FFW controls the type I error satisfactorily and has good power for detecting differentially methylated regions. Our approach has broad utility and can be applied to detect associations between different types of functions and phenotypes.

Download the flyer here.

Tags: Seminar Series in Statistics and Data Science
Published Sep. 23, 2020 5:29 PM - Last modified Dec. 1, 2020 1:22 PM