1. Singh, R., Zhang, F., and Li, Q.+ (2022). Assessing Reproducibility of High-throughput Experiments in the Case of Missing Data. Statistics in Medicine. Feb 17. (link)

  2. Chai, Z., Lyu, Y., Chen, Q., Wei, C.-H., Snyder, L. M., Weaver, V., Sebastian, A., Albert, I., Li, Q., Cantorna, M. T., and Ross, A. Catherine (2021). RNAseq studies reveal distinct transcriptional response to vitamin A deficiency in small intestine versus colon, uncovering novel vitamin A-regulated genes. The Journal of Nutritional Biochemistry, 98. (link)

  3. Osotsi, A., and Li, Q.+ (2021) Learning Robust Representations using a Change Point Framework. the 7th Workshop on Mining and Learning from Time Series (MiLeTS) at KDD 2021. (link)

  4. McGuire, D., Jiang, Y., Liu, M., Weissenkampen, J.D., Eckert, S., Yang, L., Chen, F., GWAS and Sequencing Consortium of Alcohol and Nicotine Use (GSCAN), Berg, A., Vrieze, S., Jiang, B.+, Li, Q.+, and Liu, D.+, (2021) Model-based Assessment of Replicability for Genome-wide Association Meta-analysis, Nature Communications. (link) (+co-corresponding authors)

  5. Xiang, G., Keller, C., Giardine, B., An, L., Li, Q. , Zhang, Y., Hardison, R. C. (2020). S3norm: simultaneous normalization of sequencing depth and signal-to-noise ratio in epigenomic data. Nucleic Acid Research, 48:8, e43 (link).

  6. Xiang, G., Keller, C. A., Heuston, E., Giardine, B. M., An, L., Wixom, A. Q., Miller, A., Cockburn, A., Lichtenberg, J., Gottgens, Berthold, Li, Q., Bodine, D., Mahony, S., Taylor, J., Blobel, G. A., Weiss, M. J., Cheng, Y., Yue, F., Hughes, J., Higgs, D. R., Zhang, Y., Hardison, R. C. (2020). An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis. Genome Research, Mar;30(3):472-484. (link)

  7. Osotsi, A., Oravecz, Z., Li, Q., Smyth, J., Brick, T. (2020) Individualized modeling to dis- tinguish between high and low arousal states using physiological data. Journal of Healthcare Informatics Research, pp1-19 (link).

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

  9. Hardison, R.C., Zhang, Y., Keller, C.A., Xiang, G., Heuston, E.F., An, L., Lichtenberg, J., Giardine, B.M., Bodine, D., Mahony, S., Li, Q., Yue, F., Weiss, M. J., Blobel, G., Taylor, J., Hughes, J., Higgs, D., Gottgens, B. (2019) Systematic integration of GATA transcription factors and epigenomes via IDEAS paints the regulatory landscape of hematopoietic cells. IUBMB life 2020;72:27–3 (paper).

  10. Yardimci, G., Ozadam, H., Sauria, M.E.G., Ursu, O., Yan, K.K., Yang, T., Chakraborty, A., Kaul, A., Lajoie, B.R., Song, F., Zhan, Y., Ay, F.+, Gerstein, M.+, Kundaje, A.+, Li, Q.+, Taylor, J.+, Yue, F.+, Dekker, J.+, Noble, W.S.+. (2019) Measuring the reproducibility and quality of Hi-C data. Genome Biology 20, 57(paper) (+: co-corresponding authors)

  11. 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 (paper, R package)

  12. 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 (paper, R package)

  13. 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 (link, preprint)
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  14. 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(R package, preprint).

  15. 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. (link, biorxiv, R package), (Penn State News)

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

  17. Charepalli, V., Reddivari, L., Radhakrishnan, S., Eriksson, E., Xiao, X., Kim, S. W., Shen, F., Matam, V. K., Li, Q., Bhat, V., Knight, R., and Vanamala, J. (2017) Pigs, Unlike Mice, Have Two Distinct Colonic Stem Cell Populations Similar to Humans That Respond to High-Calorie Diet prior to Insulin Resistance. Cancer Prevention Research, 10(8): 442–450. (link)

  18. Sido, A., Radhakrishnan, S., Kim, S.W., Eriksson, E., Shen, F., Li, Q., Bhat, V., Reddivari, L. and Vanamala, J. (2017) A food-based approach that targets interleukin-6, a key regulator of chronic intestinal inflammation and colon carcinogenesis. The Journal of Nutritional Biochemistry, 43: 11–17. (link)

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

  20. 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)

  21. Song, C., Pan, X., Ge, Z., Gowda, C., Ding, Y., Li, H., Li, Z., Yochum, G., Muschen, M., Li, Q., Payne, K. J., and Dovat, S. (2016) Epigenetic regulation of gene expression by Ikaros, HDAC1 and Casein Kinase II (CK2) in leukemia. Leukemia, 30, 1436–1440. (link)

  22. Kunz, R.F., Gaskin, B.J., Dong, C., Li, Q., Davanloo-Tajbakhsh, S. (2015). Multi-scale biological and physical modeling of the tumor micro-environment. Drug discovery today, 16: 7-15. (link)

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    This paper proposes a new statistical measure, called irreproducible discovery rate (IDR), to measure the reproducibility of replicate rank lists (e.g. peaks identified from replicate ChIP-seq experiments). Here is a preliminary introduction (IDR101) I wrote for biologists without technical details. (R-package: IDR on CRAN) IDR is used as part of the pipeline for uniformly processing ChIP-seq data in ENCODE. The pipeline is available at Anshul Kundaje’s website here.

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