- Genentech, Inc. South San Francisco, CA
- Principal Statistical Scientist October 2022 - Present
- Senior Statistical Scientist November 2020 - September 2022
Education
- Ph.D. in Statistics August 2015
- University of North Carolina at Chapel Hill Chapel Hill, NC
- Advisors: Yufeng Liu, J. S. Marron, D. Neil Hayes
- B.A. in Mathematics May 2009
- Pomona College Claremont, CA
- Advisor: Jo Hardin
Prior Work
- Postdoctoral Fellow, Data Sciences June 2017 - October 2020
- Dana-Farber Cancer Institute Boston, MA
- Mentor: Rafael Irizarry
- Principal Scientist, Bioinformatics June 2015 - April 2017
- Roche Sequencing Pleasanton, CA
- Graduate Research Assistant January 2012 - May 2015
- Lineberger Comprehensive Cancer Center Chapel Hill, NC
- Network Pharmacology Intern June 2014 - August 2014
- Janssen Research & Development (Johnson & Johnson) Spring House, PA
Publications
Google Scholar for complete list.
*: joint first author.
†: as TCGA Research Network.
- Schröfelbauer B, Kimes PK, Hauke P, Reid CE, Shao K, Hill SJ, Irizarry R, Hahn WC. (2023). Discovery of antibodies and cognate surface targets for ovarian cancer by surface profiling. Proceedings of the National Academy of Sciences.
- Korthauer K*, Kimes PK*, Duvallet C, Reyes A, Subramanian A, Teng M, Shukla C, Alm EJ and Hicks SC. (2019). A practical guide to methods controlling false discoveries in computational biology. Genome Biology.
- Kimes PK* and Reyes A*. (2018). Reproducible and replicable comparisons using SummarizedBenchmark. Bioinformatics.
- VanDussen KL, Stojmirović A, Li K, Liu TC, Kimes PK, Muegge BD, Simpson KF, Ciorba MA, Perrigoue JG, Friedman JR, Towne JE, Head RD, Stappenbeck TS. (2018). Abnormal small intestinal epithelial microvilli in patients with Crohn’s disease. Gastroenterology.
- Kimes PK, Liu Y, Hayes DN, and Marron JS. (2017). Statistical significance for hierarchical clustering. Biometrics.
- Kimes PK, Hayes DN, Marron JS, and Liu Y. (2016). Large-margin classification with multiple decision rules. Statistical Analysis and Data Mining.
- Kimes PK*, Cabanski CR*, Wilkerson MD, Zhao N, Johnson AR, Perou CM, Makowski L, Maher CA, Liu Y, Marron JS, and Hayes DN. (2014). SigFuge: single gene unsupervised clustering of RNA-seq reveals differential isoform usage among cancer samples. Nucleic Acids Research.
Consortia Papers
- Radovich M et al.† (2018). The integrated genomic landscape of thymic epithelial tumors. Cancer Cell.
- Fishbein L et al.† (2017). Comprehensive molecular characterization of pheochromocytoma and paraganglioma. Cancer Cell.
- Robertson AG et al. (2017). Integrative analysis identifies four molecular and clinical subsets in uveal melanoma. Cancer Cell.
- The Cancer Genome Atlas Research Network.† (2015). Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature.
- The Cancer Genome Atlas Research Network.† (2014). Comprehensive molecular profiling of lung adenocarcinoma. Nature.
Proceedings/Abstracts
- Herrera AF, McCord R, Kimes P, et al. (2022). Risk Profiling of Patients with Previously Untreated Diffuse Large B-Cell Lymphoma (DLBCL) by Measuring Circulating Tumor DNA (ctDNA): Results from the POLARIX Study. Blood. (selected for oral presentation at ASH 2022)
- VanDussen K, Li K, Stojmirović A, Liu T, Kimes PK, Perrigoue J, Friedman J, Towne J, Head R, Stappenbeck T. (2018) P050: Enterocyte microvillus length and associated gene expression are reduced in Crohn’s disease. Journal of Crohn’s and Colitis. (Abstracts of the 13th Congress of ECCO).
- Ko YH, Walter V, Catalano M, Yin X, Kimes PK, Xiao X, and Hayes DN. (2015). Abstract 4007: Integrative analysis of miRNAs classify two distinct stages of epithelial cell differentiation in head and neck squamous cell carcinoma (HNSCC). Cancer Research. (Proceedings: AACR 106th Annual Meeting 2015)
Software
R/upbm(GitHub)- Methods for analyzing universal-design protein binding microarrays (uPBMs).
R/SummarizedBenchmark(companion site), (Bioconductor)- Framework for benchmarking of methods.
R/sigclust2(GitHub)- Methods for assessing statistical significance in clustering.