Qunhua Li

Associate Professor
Qunhua Li
Qunhua
Li
Associate Professor
Department Affiliations
Interests
Bioinformatics
Latent Variable Models
Genomics
Machine Learning
Address
Phone
(814) 865-9468
Website
Personal Site

Biography

Qunhua Li is an Associate Professor of Statistics at Penn State.

She received her Ph.D. in Statistics from University of Washington in 2008. 

Her primary research interests concern developing statistical methods for uncovering complicated patterns in large and complex biological data. Her work is at the interface between statistics and biology. She has been developing latent variable models and machine learning techniques to identify and infer scientifically meaningful structures from high-throughput genomic and proteomic data.

Li joined Penn State as an Assistant Professor in 2011. She is also a primary faculty member of the Bioinformatics and Genomics program.

 

Publications

  • An, L., Yang, T., Yang, J., Nuebler, J., Xiang, G., Hardison, R.C., Li, Q.+, and Zhang, Y.+.  OnTAD: hierarchical domain structure reveals the divergence of activity among TADs and boundaries, Genome Biology 20, 282 (2019)  (+: co-corresponding authors).

     
  • Koch, H., Starenki, D., Cooper, S.J., Myers, R.M., Li, Q.+. (2018) powerTCR: a model- based approach to comparative analysis of the clone size distribution of the T cell receptor repertoire, PLoS Computational Biology 14(11): e1006571.

     
  • Lyu, Y., Xue, L., Zhang, F., Koch, H., Saba, L., Kechris, K., Li, Q.+. (2018) Condition-adaptive fused graphical lasso (CFGL): an adaptive procedure for inferring condition specific gene co-expression network, PLoS Computational Biology.

     
  • Philtron, D., Lyu, Y., Li, Q.+, and Ghosh, D.+ (2018) Maximum rank reproducibility: a non-parametric approach to assessing reproducibility in replicate experiments, Journal of American Statistical Association. 113: 1028-1039 (+: co-corresponding authors).

     
  • Li, Q.+ and Zhang, F.. (2018) A regression framework for assessing covariate effects on the reproducibility of high-throughput experiments. Biometrics 74(3): 781-1138.

     
  • Yang, T., Zhang, F., Yardimci, G.G., Song, F., Hardison, R.C., Noble, W.S., Yue, F., Li, Q.+. (2017) HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Research, 27(11):1939-1949.

     
  • Zhang, F. and Li, Q.+. (2017) A continuous threshold expectile model. Computational Statistics & Data Analysis, 116: 49–66.

     
  • Zhang, F. and Li, Q.+. (2017) Robust bent line regression. Journal of Statistical Planning and Inference, 185: 41–55.

     
  • Lyu, Y. and Li, Q.+ (2016) A semi-parametric statistical model for integrating gene expression profiles across different platforms. BMC Bioinformatics, 17(Suppl 1):S5 (link, R package).

     
  • Li, Q., Brown, J.B., Huang, H. and Bickel, P.J. (2011) Measuring reproducibility of high-throughput experiments, Annals of Applied Statistics, 5(3), 1752-1779.

 

Teaching

STAT 555 - Statistical genomics

STAT504 - Analysis of discrete data

STAT 557 - Data mining

STAT 544 - Categorical data analysis

STAT 414 - Introduction to probability

STAT 415 -Introduction to mathematical statistics