Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway.
Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.
BMC Bioinformatics. 2021 Feb 10;22(1):61. doi: 10.1186/s12859-021-03979-y.
We present here a computational shortcut to improve a powerful wavelet-based method by Shim and Stephens (Ann Appl Stat 9(2):665-686, 2015. https://doi.org/10.1214/14-AOAS776 ) 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. We applied a recent method by Zhou and Guan (J Am Stat Assoc 113(523):1362-1371, 2017. https://doi.org/10.1080/01621459.2017.1328361 ) to boost computational speed, which involves calculating the distribution of Bayes factors and estimating the significance of an association 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. The 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. As more and more DNAm datasets are being made available through public repositories, an attractive application of FFW would be to re-analyze these data and identify associations that might have been missed by previous efforts. The full R package for FFW is freely available at GitHub https://github.com/william-denault/ffw .
我们在此提出一种计算捷径，用于改进 Shim 和 Stephens（Ann Appl Stat 9(2):665-686, 2015. https://doi.org/10.1214/14-AOAS776 ）提出的一种名为 WaveQTL 的强大基于小波的方法，该方法最初用于识别 DNA 酶 I 超敏性数量性状基因座（dsQTL）。
WaveQTL 依赖于置换来评估关联的显著性。我们应用了 Zhou 和 Guan（J Am Stat Assoc 113(523):1362-1371, 2017. https://doi.org/10.1080/01621459.2017.1328361 ）的一种新方法来提高计算速度，该方法涉及计算贝叶斯因子的分布，并通过模拟而不是置换来估计关联的显著性。我们称这种基于模拟的方法为“快速功能小波”（FFW），并在公开的结直肠癌 DNA 甲基化（DNAm）数据集上进行了测试。模拟结果证实，与 WaveQTL 中的置换方法相比，计算速度有了显著提高。此外，我们还表明 FFW 可以令人满意地控制第一类错误，并具有检测差异甲基化区域的良好功效。
我们的方法具有广泛的适用性，可用于检测不同类型的功能和表型之间的关联。随着越来越多的 DNAm 数据集通过公共存储库提供，FFW 的一个有吸引力的应用将是重新分析这些数据并识别以前的研究可能遗漏的关联。FFW 的完整 R 包可在 GitHub 上免费获得 https://github.com/william-denault/ffw 。
