Research

Overview

The broad mission of my research involves the development and application of bioinformatics/statistical methodologies for analyzing high-throughput 'omic data. I also have deeply rooted interest in epigenetics and molecular epidemiology, specifically DNA methylation and its implications for human health and disease. Depending on the disease or condition being studied, it is possible that only a small fraction of the molecular markers measured (i.e., CpG sites, genes, etc.) vary across subjects in any biologically meaningful way. My job is therefore to develop new and/or apply existing statistical methodologies for identifying these markers and to understand their functional role in the context of the disease under study. An appropriate analogy for this task is the age-old adage, "finding a needle in a haystack" - actually it is often like trying to find a needle in a barn full of haystacks! In addition to my methodological interests, which include, statistical 'omics, computational statistics, multivariate statistics, machine learning, unsupervised clustering, mixture deconvolution, mixture models, and mixed-effects models, I am also passionate about my collaborations, which span from environmental health, the human microbiome, to a wide variety of different epigenetics studies. The shared theme across my collaborative research projects is the use of high-dimensional 'omic data to gain further insight into some biological process.

  • Koestler, D., C, Christensen, B, Karagas, M., R, Marsit, C., J, Langevin, S., M, Kelsey, K., T, Wiencke, J., K, Houseman, E., A. 2013. Blood-based profiles of DNA methylation predict the underlying distribution of cell types: a validation analysis.. Epigenetics, 8 (8), 816-26
  • Koestler, D., C, Marsit, C., J, Christensen, B., C, Accomando, W, Langevin, S., M, Houseman, E., A, Nelson, H., H, Karagas, M., R, Wiencke, J., K, Kelsey, K., T. 2012. Peripheral blood immune cell methylation profiles are associated with nonhematopoietic cancers.. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 21 (8), 1293-302
  • Koestler, D., C, Marsit, C., J, Christensen, B., C, Kelsey, K., T, Houseman, E., A. 2014. A recursively partitioned mixture model for clustering time-course gene expression data.. Translational cancer research, 3 (3), 217-232
  • Koestler, D., C, Jones, M., J, Usset, J, Christensen, B., C, Butler, R., A, Kobor, M., S, Wiencke, J., K, Kelsey, K., T. 2016. Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL).. BMC bioinformatics, 17, 120
  • Xia, Qing, Mudaranthakam, Dinesh Pal, Chollet-Hinton, Lynn, Krebill, Hope, Kuo, Hanluen, Chen, Ronald, Koestler, Devin. 2021. A User-friendly ShinyR Application for Visualizing Cancer Risk Factors and Mortality across the University of Kansas Cancer Center Catchment Area
  • Koestler, D., C, Marsit, C., J, Christensen, B., C, Karagas, M., R, Bueno, R, Sugarbaker, D., J, Kelsey, K., T, Houseman, E., A. 2010. Semi-supervised recursively partitioned mixture models for identifying cancer subtypes.. Bioinformatics (Oxford, England), 26 (20), 2578-85
  • Meier, R, Nissen, E, Koestler, D., C. 2021. Low variability in the underlying cellular landscape adversely affects the performance of interaction-based approaches for conducting cell-specific analyses of DNA methylation in bulk samples.. Statistical applications in genetics and molecular biology, 20 (3), 73-84
  • Salas, L., A, Koestler, D., C, Butler, R., A, Hansen, H., M, Wiencke, J., K, Kelsey, K., T, Christensen, B., C. 2018. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray.. Genome biology, 19 (1), 64
  • Seyednasrollah, F, Koestler, D., C, Wang, T, Piccolo, S., R, Vega, R, Greiner, R, Fuchs, C, Gofer, E, Kumar, L, Wolfinger, R., D, Kanigel Winner, K, Bare, C, Neto, E., C, Yu, T, Shen, L, Abdallah, K, Norman, T, Stolovitzky, G, Soule, H., R, Sweeney, C., J, Ryan, C., J, Scher, H., I, Sartor, O, Elo, L., L, Zhou, F., L, Guinney, J, Costello, J., C. 2017. A DREAM Challenge to Build Prediction Models for Short-Term Discontinuation of Docetaxel in Metastatic Castration-Resistant Prostate Cancer.. JCO clinical cancer informatics, 1, 1-15
  • Xia, Q, Thompson, J., A, Koestler, D., C. 2021. Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE).. Statistical applications in genetics and molecular biology, 20 (4-6), 101-119