Overview

Innovative technologies now allow us to probe the genome in more dimensions and at higher resolution than ever before, providing a wealth of information for studying the genomic basis of complex traits. However, meaningful biological insights are often masked by technical artifacts, systematic biases, or low signal-to-noise ratio (“needle in a haystack”). These challenges demand tailored statistical methodology in order to unlock the full potential of emerging assays.

My research group focuses on developing novel frameworks and rigorous inferential procedures that exploit the increased scope and scale of high-throughput sequencing data, with the ultimate goal of uncovering new molecular signals in cancer, child health, and development.

Publications

Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing.
Biostatistics (Oxford, England)
Korthauer K and Chakraborty S and Benjamini Y and Irizarry RA
DOI: 10.1093/biostatistics/kxy007
PubMed: 29481604
07/2019

A practical guide to methods controlling false discoveries in computational biology.
Genome biology
Korthauer K and Kimes PK and Duvallet C and Reyes A and Subramanian A and Teng M and Shukla C and Alm EJ and Hicks SC
DOI: 10.1186/s13059-019-1716-1
PubMed: 31164141
06/2019

A Compositional Model to Assess Expression Changes from Single-Cell Rna-Seq Data
By Xiuyu Ma and Keegan Korthauer and Christina Kendziorski and Michael A. Newton
DOI: 10.1101/655795
05/2019

A practical guide to methods controlling false discoveries in computational biology
Keegan Korthauer and Patrick K Kimes and Claire Duvallet and Alejandro Reyes and Ayshwarya Subramanian and Mingxiang Teng and Chinmay Shukla and Eric J Alm and Stephanie C Hicks
DOI: 10.1101/458786
10/2018

Genome-wide repressive capacity of promoter DNA methylation is revealed through epigenomic manipulation
Keegan Korthauer and Rafael A. Irizarry
DOI: 10.1101/381145
08/2018

A Somatically Acquired Enhancer of the Androgen Receptor Is a Noncoding Driver in Advanced Prostate Cancer.
Cell
Takeda DY and Spisák S and Seo JH and Bell C and O'Connor E and Korthauer K and Ribli D and Csabai I and Solymosi N and Szállási Z and Stillman DR and Cejas P and Qiu X and Long HW and Freedman ML
DOI: 10.1016/j.cell.2018.05.037
PubMed: 29909987
06/2018

High-throughput identification of RNA nuclear enrichment sequences
The EMBO Journal
Chinmay J Shukla and Alexandra L McCorkindale and Chiara Gerhardinger and Keegan D Korthauer and Moran N Cabili and David M Shechner and Rafael A Irizarry and Philipp G Maass and John L Rinn
DOI: 10.15252/embj.201798452
03/2018

High-throughput identification of RNA nuclear enrichment sequences
Shukla CJ and McCorkindale AL and Gerhardinger C and Korthauer KD and Cabili MN and Shechner DM and Irizarry RA and Maass PG and Rinn JL
DOI: 10.1101/189654
09/2017

Detection and accurate False Discovery Rate control of differentially methylated regions from Whole Genome Bisulfite Sequencing
Keegan D. Korthauer and Sutirtha Chakraborty and Yuval Benjamini and Rafael A. Irizarry
DOI: 10.1101/183210
08/2017

A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.
Genome biology
Korthauer KD and Chu LF and Newton MA and Li Y and Thomson J and Stewart R and Kendziorski C
DOI: 10.1186/s13059-016-1077-y
PubMed: 27782827
10/2016

IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients.
Statistics in biosciences
Choi J and Ye S and Eng KH and Korthauer K and Bradley WH and Rader JS and Kendziorski C
DOI: 10.1007/s12561-016-9144-1
PubMed: 28966695
03/2016

scDD: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
Korthauer KD and Chu L and Newton MA and Li Y and Thomson J and Stewart R and Kendziorski C
DOI: 10.1101/035501
12/2015

Chromosomal copy number alterations and HPV integration in cervical precancer and invasive cancer.
Carcinogenesis
Bodelon C and Vinokurova S and Sampson JN and den Boon JA and Walker JL and Horswill MA and Korthauer K and Schiffman M and Sherman ME and Zuna RE and Mitchell J and Zhang X and Wentzensen N
DOI: 10.1093/carcin/bgv171
PubMed: 26660085
12/2015

MADGiC: a model-based approach for identifying driver genes in cancer.
Bioinformatics (Oxford, England)
Korthauer KD and Kendziorski C
DOI: 10.1093/bioinformatics/btu858
PubMed: 25573922
01/2015

Methods for collapsing multiple rare variants in whole-genome sequence data.
Genetic epidemiology
Sung YJ and Korthauer KD and Swartz MD and Engelman CD
DOI: 10.1002/gepi.21820
PubMed: 25112183
09/2014

Limited model antigen expression by transgenic fungi induces disparate fates during differentiation of adoptively transferred T cell receptor transgenic CD4+ T cells: robust activation and proliferation with weak effector function during recall.
Infection and immunity
Wüthrich M and Ersland K and Pick-Jacobs JC and Gern BH and Frye CA and Sullivan TD and Brennan MB and Filutowicz HI and O'Brien K and Korthauer KD and Schultz-Cherry S and Klein BS
DOI: 10.1128/IAI.05326-11
PubMed: 22124658
11/2011

Predicting Cancer Subtypes Using Survival-Supervised Latent Dirichlet Allocation Models
Advances in Statistical Bioinformatics
Keegan Korthauer and John Dawson and Christina Kendziorski
DOI: 10.1017/cbo9781139226448.019

Research

Unraveling the spatial landscape of epigenomic signals
A common task in the interpretation of epigenomic data, which holds information about the genome not encoded in the DNA sequence itself, is the detection and inference of regions of interest. For example, it is of interest to detect segments of the genome that show significantly higher or lower DNA methylation levels with respect to disease state or developmental stage, as this particular modification to the DNA is known to influence gene regulation. However, the number of possible segments of all possible sizes is near infinite, leading to a massive multiple testing problem. Our group develops tailored statistical and computational approaches for powerful detection and inference of region-based epigenomic signals, while paying particular attention to spatial patterns. We are interested in designing and applying these techniques for the analysis of DNA methylation, histone modification, and chromatin accessibility assay data.

Predicting gene expression from epigenomic signals
It is widely known that epigenetic information, such as DNA methylation and histone modifications, plays a role in gene regulation. However, the prediction of gene expression from epigenomic signals is challenging due to interactions between different epigenomic marks as well as interactions between different regions of the genome. We are working on developing predictive models that account for these challenges and assess the predictive capacity for various epigenomic signals.

Understanding the genomic basis of complex traits
Our group develops computational approaches to study the genomic basis of a variety of complex traits. Our main focus areas currently include modeling the mutation spectrum of cancer genomes, revealing heterogeneity in single-cell gene expression during development, and characterizing the epigenomic landscape of prostate cancer. To maximize impact of our work, we also provide open source computational tools that enable other scientists to make meaningful biological insights.

Research Group Members

Marco Tello, Masters Student