Selected Statistical Papers, +: co-first author; *: corresponding author; 　　: student paper

    2022

  1. Lingsong Meng, Dorina Avram, George Tseng, Zhiguang Huo*. Outcome-guided Sparse K-means for Disease Subtype Discovery via Integrating Phenotypic Data with High-dimensional Transcriptomic Data. Journal of the Royal Statistical Society: Series C (Applied Statistics) (Accepted) [arXiv]
  2. Wei Zong, Marianne L. Seney, Kyle D. Ketchesin, Michael T. Gorczyca, Andrew C. Liu, Karyn A. Esser, George C. Tseng*, Colleen A. McClung*, Zhiguang Huo*. Experimental Design and Power Calculation in Omics Circadian Rhythmicity Detection. (Submitted) [biorxiv]
  3. Lingsong Meng, Zhiguang Huo*. Outcome-guided Bayesian Clustering for Disease Subtype Discovery Using High-dimensional Transcriptomic Data. (Submitted) [arXiv]
  4. Haocheng Ding, Lingsong Meng, Chengguo Xing, Karyn Esser, Zhiguang Huo*. Statistical Methods for Detecting Circadian Rhythmicity and Differential Circadian Patterns with Repeated Measurement in Transcriptomic Applications. (Submitted) [biorxiv]

    5. 2021

  5. Haocheng Ding, Lingsong Meng, Andrew C. Liu, Michelle L. Gumz, Andrew J. Bryant, Colleen A. Mcclung, George Tseng, Karyn A. Esser, Zhiguang Huo*. Likelihood-based Tests for Detecting Circadian Rhythmicity and Differential Circadian Patterns in Transcriptomic Applications. Briefings in Bioinformatics Volume 22, Issue 6, November 2021, bbab224. [biorxiv]

    6. 2020

  6. Zhiguang Huo, Shaowu Tang, Yongseok Park*, George Tseng*. (2020) P-value evaluation, variability index and biomarker categorization for adaptively weighted Fisher's meta-analysis method in omics applications. Bioinformatics 36(2), 524-532. [arXiv] [GitHub] [Bioconductor]

    7. 2019

  7. Zhiguang Huo*, Li Zhu, Tianzhou Ma, Hongcheng Liu, Song Han, Daiqing Liao, Jinying Zhao and George Tseng*. Two-way Horizontal and Vertical Omics Integration for Disease Subtype Discovery. Statistics in Bioscience (2019) 1-22. [pdf] [GitHub]
  8. Zhiguang Huo, Chi Song, George C. Tseng. Bayesian latent hierarchical model for transcriptomic meta-analysis to detect biomarkers with clustered meta-patterns of differential expression signals. Annals of Applied Statistics 13, no. 1 (2019): 340-366. [arXiv] [GitHub] [poster] (An earlier version won ASA Biometrics Section JSM Travel Award 2018)

    9. 2018

  9. Tianzhou Ma+, Zhiguang Huo+, Anche Kuo+, Li Zhu, Fang Zhou, Xiangrui Zeng, Chien-Wei Lin, Silvia Liu, Lin Wang, Tanbin Rahman, Lun-Ching Chang, Sunghwan Kim, Jia Li, Yongseok Park, Chi Song, Steffi Oesterreich, Etienne Sibille and George C. Tseng. (2019). MetaOmics - Comprehensive Analysis Pipeline and Web-based Software Suite for Transcriptomic Meta-Analysis. Bioinformatics 35, no. 9 (2018): 1597-1599. [GitHub]
  10. Kelly Cahill+, Zhiguang Huo+ , George Tseng, Ryan W. Logan*, Marianne L. Seney* (2018), Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach. Scientific Reports 8.1 (2018): 9588.

    11. 2017

  11. Zhiguang Huo, George C. Tseng. Integrative sparse K-means with overlapping group lasso in genomic applications for disease subtype discovery. The Annals of Applied Statistics, 11, no. 2 (2017): 1011-1039. [pdf] [GitHub]

    12. 2016

  12. Zhiguang Huo, Ying Ding, Silvia Liu, Steffi Oesterreich, and George Tseng. Meta-Analytic Framework for Sparse K-Means to Identify Disease Subtypes in Multiple Transcriptomic Studies. Journal of the American Statistical Association, 111, no. 513 (2016): 27-42. [pdf] [GitHub]