Christina Kendziorski

Representative Awards

• 2016  Vilas Associate Award, University of Wisconsin-Madison
• 2018  Elected Fellow of  the American Statistical Association

Research

Professor Kendziorski is a leader in the field of statistical genetics and genomics. Her research focuses on the development of statistical methods and software for genomic based studies of development and disease. The beginning of her career coincided with the introduction of DNA microarrays, a technology that allows for genome wide profiling of gene expression. Microarrays introduced datasets with thousands of features but relatively few samples, which rendered many traditional statistical methods ineffective. Dr. Kendziorski’s group focused on developing novel statistical methods in this area, and was the first to address critical experimental design questions associated with resource allocation and downstream analyses. More recently, her group has detailed the statistical and computational challenges associated with analysis of single-cell RNA-sequencing data which allows for genome wide profiling of gene expression in single cells; and they have developed statistical methods to address challenges in normalization and analysis. Dr. Kendziorski has served on a number of review boards including a three year term on the NIH Bench to Bedside Program and a six year term on the NIH Genomics, Computational Biology, and Technology (GCAT) study section. Her research has been continuously funded by the NIH for the past 15 years; and she was recently elected a fellow of the American Statistical Association. Although her primary focus is on research and training, Dr. Kendziorski is dedicated to eradicating socio-economic and geographic inequities in education, and toward that end she recently chaired the University of Wisconsin-Madison committee on undergraduate admissions, recruitment, and financial aid (CURAFA). In addition to her work in academia, Dr. Kendziorski has collaborated with a number of companies including Nimblegen, ThirdWave Technologies, Stratatech, Eli Lilly, and Merck Pharmaceuticals.

Representative Publications  (Google Scholar | PUBMED)

• Bacher, R. et al. Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments. BMC bioinformatics 19, 380, doi:10.1186/s12859-018-2405-x (2018).
• Bacher, R. et al. SCnorm: robust normalization of single-cell RNA-seq data. Nature methods 14, 584-586, doi:10.1038/nmeth.4263 (2017).
• Korthauer, K. D. et al. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome biology 17, 222, doi:10.1186/s13059-016-1077-y (2016).
• Tran, K. A. et al. Collaborative rewiring of the pluripotency network by chromatin and signalling modulating pathways. Nature communications 6, 6188, doi:10.1038/ncomms7188 (2015).
• Leng, N. et al. Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments. Nature methods 12, 947-950, doi:10.1038/nmeth.3549 (2015).