Keegan Korthauer, PhDPostdoctoral Research Fellow
Keegan Korthauer is a Postdoctoral Research Fellow with Rafael Irizarry in the Biostatistics Department at the Harvard T.H. Chan School of Public Health and at the Department of Biostatistics and Computational Biology at Dana-Farber Cancer Institute. As a member of the Irizarry lab, she focuses on developing statistical methodology and computational tools for the analysis of high-dimensional genomics data. Most of her work resolves around experiments that use next-generation sequencing technologies to characterize the genomic basis of complex traits. She is particularly interested in analyzing the mutation spectrum of cancer genomes, studying the gene expression patterns of single cells, and characterizing changes in genome-wide methylation patterns.


Department of Biostatistics and Computational Biology
Dana-Farber Cancer Institute
450 Brookline Avenue
Boston, MA 02115


2015 - Statistics PhD, University of Wisconsin Madison

2009 - Biostatistics MS, University of Minnesota

2007 - Biology BS, University of Minnesota


I develop open-source, freely available software for the analysis of high-throughput genomic data. My code is available through R packages that can be installed from GitHub or the Bioconductor Project.

  • dmrseq: R package for inference for differentially methylated regions (DMRs) from bisulfite sequencing, available on GitHub
  • scDD: R package for the identification of differentially distributed genes in single-cell RNA-seq, available on Bioconductor
  • MADGiC: R package for the identification of cancer driver genes by integrating somatic mutation, expression, replication timing, and functional impact, available on GitHub
For a complete list see here: Google Scholar

  1. K. Korthauer, S. Chakraborty, Y. Benjamini, R. A. Irizarry. Detection and accurate False Discovery Rate control of differentially methylated regions from Whole Genome Bisulfite Sequencing. bioRxiv, doi:, 2017.
  1. K. Korthauer, L.-F. Chu, M. A. Newton, Y. Li, J. Thomson, R. Stewart and C. Kendziorski. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology, 17:222, 2016.
  1. K. Korthauer, C. Kendziorski. MADGiC: a model-based approach for identifying driver genes in cancer. Bioinformatics, 31(10): 1526–1535, 2015.
  1. Y. J. Sung, K. Korthauer, M. Swartz and C. Engelman. Methods for Collapsing Multiple Rare Variants in Whole Genome Sequencing Data. Genetic Epidemiology, 38(S1):S13-S20, 2014.
  1. K. Korthauer, J. Dawson and C. Kendziorski. Predicting cancer subtypes using survival-supervised latent Dirichlet allocation models. In Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data, K.-A. Do, Z. S. Qin and M. Vannucci (Eds). Cambridge University Press, 2013.
  • Travel Award, Workshop on Statistical Challenges in Single-Cell Biology, Ascona, Switzerland, 2017
  • Travel Award, Biometrics Section, American Statistical Association, Joint Statistical Meetings, Chicago, Illinois, 2016
  • Poster Award, Regional Advisory Board, International Biometric Society, Eastern North American Region Meeting, Miami, Florida, 2015
  • Predoctoral Training Grant in Biostatistics, National Institute for General Medical Sciences, University of Wisconsin, 2010-2013
  • Outstanding Teaching Assistant Award, University of Minnesota, 2008
  • Research Grant, Undergraduate Research Opportunities Program, University of Minnesota, 2006
  • National Merit James E. Casey Scholarship, National Merit Scholarship Program & United Parcel Service (UPS), 2003-2007