Correcting for cell-type heterogeneity in epigenome-wide association studies: revisiting previous analyses
2017 Feb 28;14(3):216-217.
Shijie C Zheng
Andrew E Jaffe
Devin C Koestler
Kasper D Hansen
Andres E Houseman
Rafael A Irizarry
Andrew E Teschendorff
CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
University of Chinese Academy of Sciences, Beijing, China.
Medical Genomics, UCL Cancer Institute, University College London, London, UK.
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland, USA.
Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas, USA.
Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon, USA.
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Statistical Cancer Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, London, UK.
Department of Women's Cancer, University College London, London, UK.
No abstract available
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Genome-Wide Association Study*
P20 GM103418/GM/NIGMS NIH HHS/United States
KL2 TR000119/TR/NCATS NIH HHS/United States