Jordan Bryan

Research

Preprints

  1. Bryan, JG, Zhou, H., and Li, D. “A compromise criterion for weighted least squares estimates.” https://arxiv.org/abs/2404.00753.

  2. Bryan, JG, and Hoff, PD. “Linear source apportionment using generalized least squares.” https://arxiv.org/abs/2310.12460.

  3. Bryan, JG, Niles-Weed, J, and Hoff, PD. “The multirank likelihood for semiparametric canonical correlation analysis.” https://arxiv.org/abs/2112.07465.

Publications

  1. de Matos Simoes, R, Shirasaki, R, Downey-Kopyscinski, SL, Matthews, GM, Barwick, BG, Gupta, VA, Dupere-Richer, D, Yamano, S, Hu, Y, Sheffer, M, Dhimolea, E, Dashevsky, O, Gandolfini, S, Ishiguro, K, Meyers, RM, Bryan, JG, Dharia, NV, Hengeveld, PG, Bruggenthies, JB, Tang, H, Aguirre, AJ, Sievers, QL, Ebert, BL, Glassner, BJ, Ott, CJ, Bradner, JE, Kwiatkowski, NP, Auclair, D, Levy, J, Keats, JJ, Groen, RWJ, Gray, NS, Culhane, AC, McFarland, JM, Dempster, JM, Licht, JD, Boise, LH, Hahn, WC, Vazquez, F, Tsherniak, A, Mitsiades, CS.. “Genome-scale functional genomics identify genes preferentially essential for multiple myeloma cells compared to other neoplasias.” Nature Cancer, (May 26 2023). https://doi.org/10.1038/s43018-023-00550-x

  2. Bryan, JG, Hoff, PD, and Osburn CL. “Routine Estimation of Dissolved Organic Matter Sources Using Fluorescence Data and Linear Least Squares.” ACS ES&T Water, (May 20, 2023). https://doi.org/10.1021/acsestwater.2c00605

  3. Bryan, JG, and Hoff, PD. “Smaller \(p\)-values in genomics studies using distilled auxiliary information.” Biostatistics, (July 16, 2021). https://doi.org/10.1093/biostatistics/kxaa053

  4. Sheffer, M, Lowry, E, Beelen, N, Borah, M, Amara, SN, Mader, CC, Roth, JA, Tsherniak, A, Freeman, SS, Dashevsky, O, Gandolfi, S, Bender, S, Bryan, JG, Zhu, C, Wang, L, Tariq, I, Kamath, GM, Simoes, RD, Dhimolea, E, Yu, C, Hu, Y, Dufva, O, Giannakis, M, Syrgkanis, V, Fraenkel, E, Golub, T, Romee, R, Mustjoki, S, Culhane, AC, Wieten, L, Mitsiades, CS. “Genome-scale screens identify factors regulating tumor cell responses to natural killer cells.” Nature Genetics, (July, 12, 2021). https://doi.org/10.1038/s41588-021-00889-w

  5. Bryan, J, Mandan, A, Kamat, G, Gottschalk, WK, Badea, A, Adams, KJ, Thompson, JW, Colton, CA, Mukherjee, S, Lutz, MW. “Likelihood ratio statistics for gene set enrichment in Alzheimer’s disease pathways.” Alzheimer’s & Dementia, (January 21, 2021). https://doi.org/10.1002/alz.12223

  6. Dhimolea, E, Simoes, RDM, Kansara, D, Al-Khafaji, A, Bouyssou, J, Weng, X, Sharma, S, Raja, J, Awate, P, Shirasaki, R, Tang, H, Glassner, BJ, Liu, Z, Gao, D, Bryan, J, Bender, S, Roth, J, Scheffer, M, Jeselsohn, R, Gray, NS, Georgakoudi, I, Vazquez, F, Tsherniak, A, Chen, Y, Welm, A, Cihangir, D, Melnick, A, Bartholdy, B, Brown, M, Culhane, AC, Mitsiades, CS. “An Embryonic Diapause-like Adaptation with Suppressed Myc Activity Enables Tumor Treatment Persistence.” Cancer Cell, (January 07, 2021). https://doi.org/10.1016/j.ccell.2020.12.002

  7. Wu, J, Bryan, J, Rubinstein, SM, Wang, L, Lenoue-Newton, M, Zuhour, R, Levy, M, Micheel, C, Xu, Y, Bhavnani, SK, Mackey, L, and Warner, JL. “Opportunities and Challenges for Analyzing Cancer Data at the Inter- and Intra-Institutional Levels.” JCO Precision Oncology, (June 25, 2020): 743-756

  8. Corsello, SM, Nagari, RT, Spangler, RD, Rossen, J, Kocak, M, Bryan, JG, Humeidi, R, Peck, D, Wu, X, Tang, AA, Wang, VM, Bender, SA, Lemire, E, Narayan, R, Montgomery, P, Ben-David, U, Garvie, CW, Chen, Y, Rees, MG, Lyons, NJ, McFarland, JM, Wong, BT, Wang, L, Dumont, N, O’Hearn, PJ, Stefan, E, Doench, JG, Greulich, H, Meyerson, M, Vazquez, F, Subramanian, A, Roth, JA, Bittker, JA, Boehm, JS, Mader, CC, Tsherniak, A and Golub, TR. “Discovering the anticancer potential of non-oncology drugs by systematic viability profiling.” Nature Cancer, (January 2020) doi:10.1038/s43018-019-0018-6

  9. McFarland, JM, Ho, ZV, Kugener, G, Dempster, JM, Montgomery, PG, Bryan, JG, Krill-Burger, JM, Green, TM, Vazquez, F, Boehm, JS, Golub, TR, Hahn, WC, Root, DE and Tsherniak, A. “Improved Estimation of Cancer Dependencies from Large-Scale RNAi Screens Using Model-Based Normalization and Data Integration.” Nature Communications 9, no. 1 (December 2018). https://doi.org/10.1038/s41467-018-06916-5.

  10. Gray, SW, Gagan, J, Cerami, E, Cronin, AM, Uno, H, Oliver, N, Lowenstein, C, Lederman, R, Revette, A, Suarez, A, Lee, C, Bryan, J, Sholl, L, and Van Allen, EM. “Interactive or static reports to guide clinical interpretation of cancer genomics.” Journal of the American Medical Informatics Association 25.5 (May 1, 2018): 458-464.

  11. Meyers, RM, Bryan, JG, McFarland, JM, Weir, BA, Sizemore, AE, Xu, H, Dharia, NV, Montgomery, PG, Cowley, GS, Pantel, S, Goodale, A, Lee, Y, Ali, LD, Jiang, G, Lubonja, R, Harrington, WF, Strickland, M, Wu, T, Hawes, DC, Zhivich, VA, Wyatt, MR, Kalani, Z, Chang, JJ, Okamoto, M, Stegmaier, K, Golub, TR, Boehm, JS, Vazquez, F, Root, DE, Hahn, WC, and Tsherniak, A. “Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells.” Nature Genetics 49.12 (October 30, 2017): 1779-1784.

  12. Mackey, L, Bryan, J, and Mo, MY. “Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge.” Ed. Cowan, G, Germain, C, Guyon, I, Kégl, B and Rousseau, D. Proceedings of the Nips 2014 Workshop on High Energy Physics and Machine Learning 42 (December 13, 2015): 129-134.

Software

BTF: R package for Bayesian inference in a low-rank tensor factorization model

ceres: R package for correction of copy-number effect in CRISPR-Cas9 essentiality screens in cancer cell lines

Talks

Applying least squares principles to estimating sources of contamination in the Neuse River basin. Webinar for the North Carolina Chapter of the American Statistical Association. April 9th, 2024.

Least squares principles for the source apportionment problem. Invited talk at CMStatistics, Berlin, Germany. December 17th, 2023.

Least squares principles for the source apportionment problem. Invited talk at TUM, Munich, Germany. November 6th, 2023.

Incompleteness and the Underground. Paper presented at the meeting of the International Dostoevsky Society, Nagoya, Japan. August 28, 2023.

The multirank likelihood for semiparametric CCA. Contributed talk at BNP Networking Event, Nicosia, Cyprus. April 27, 2022.

The multirank likelihood for semiparametric CCA. Invited talk at CFE-CMStatistics, Online. December 19th, 2021.

Nonparametric empirical Bayes estimation using entropic optimal transport. Speed session talk at Joint Statistical Meetings, Online. August 12th, 2021.

Smaller \(p\)-Values in Genomics Studies Using Distilled Auxilliary Information. Contributed talk at the World Meeting of the International Society for Bayesian Analysis, Online. June 28th, 2021.

Smaller \(p\)-Values in Genomics Studies Using Distilled Historical Information. Contributed talk at Joint Statistical Meetings, Online. August 3rd, 2020.

Incompleteness and the Underground. Paper presented at the 2nd Annual Duke-Stanford Graduate Conference, Durham, NC. April 6, 2019.

Incompleteness and the Underground. Paper presented at the 57th Annual Southern Conference on Slavic Studies, Mobile, AL. March 15, 2019.

CERES: A New Approach to Correct for Copy Number in CRISPR-Cas9 Screens. CTD^2 D-HIP Webinar Series. CTD^2 Network. July 12, 2018.

CERES—A model for inferring genetic dependencies in cancer cell lines from CRISPR knockout screens. Meeting on Biological Data Science. Cold Spring Harbor Laboratory. October 26, 2016.