Browsing by Author "Jiang, Yuchao"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Genome-wide circadian rhythm detection methods: systematic evaluations and practical guidelines
    (Oxford University Press, 2021-05-20) Mei, Wenwen; Jiang, Zhiwen; Chen, Yang; Chen, Li; Sancar, Aziz; Jiang, Yuchao; Medicine, School of Medicine
    Circadian rhythms are oscillations of behavior, physiology and metabolism in many organisms. Recent advancements in omics technology make it possible for genome-wide profiling of circadian rhythms. Here, we conducted a comprehensive analysis of seven existing algorithms commonly used for circadian rhythm detection. Using gold-standard circadian and non-circadian genes, we systematically evaluated the accuracy and reproducibility of the algorithms on empirical datasets generated from various omics platforms under different experimental designs. We also carried out extensive simulation studies to test each algorithm’s robustness to key variables, including sampling patterns, replicates, waveforms, signal-to-noise ratios, uneven samplings and missing values. Furthermore, we examined the distributions of the nominal equation M1-values under the null and raised issues with multiple testing corrections using traditional approaches. With our assessment, we provide method selection guidelines for circadian rhythm detection, which are applicable to different types of high-throughput omics data.
  • Thumbnail Image
    Item
    WEVar: a novel statistical learning framework for predicting noncoding regulatory variants
    (Oxford University Press, 2021) Wang, Ye; Jiang, Yuchao; Yao, Bing; Huang, Kun; Liu, Yunlong; Wang, Yue; Qin, Xiao; Saykin, Andrew J.; Chen, Li; Biostatistics and Health Data Science, School of Medicine
    Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are located in the noncoding regions, making the identification of causal variants a particular challenge. Existing computational approaches developed for prioritizing noncoding variants produce inconsistent and even conflicting results. To address these challenges, we propose a novel statistical learning framework, which directly integrates the precomputed functional scores from representative scoring methods. It will maximize the usage of integrated methods by automatically learning the relative contribution of each method and produce an ensemble score as the final prediction. The framework consists of two modes. The first 'context-free' mode is trained using curated causal regulatory variants from a wide range of context and is applicable to predict regulatory variants of unknown and diverse context. The second 'context-dependent' mode further improves the prediction when the training and testing variants are from the same context. By evaluating the framework via both simulation and empirical studies, we demonstrate that it outperforms integrated scoring methods and the ensemble score successfully prioritizes experimentally validated regulatory variants in multiple risk loci.