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Correcting for cell-type heterogeneity in epigenome-wide association studies: premature analyses and conclusions

Shijie C Zheng, Stephan Beck, Andrew E. Jaffe, Devin C. Koestler, Kasper D. Hansen, Andres E. Houseman, Rafael A. Irizarry, Martin Widschwendter, Andrew E. Teschendorff
doi: https://doi.org/10.1101/121533
Shijie C Zheng
1CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
2University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing, China.
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Stephan Beck
3Medical Genomics, Paul O’Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London, United Kingdom.
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Andrew E. Jaffe
4Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
5Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland, United States of America.
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Devin C. Koestler
6Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA.
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Kasper D. Hansen
7Center for Epigenetics, Johns Hopkins University School of Medicine, USA.
8McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, USA.
9Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, USA.
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Andres E. Houseman
10School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA.
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Rafael A. Irizarry
11Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, USA.
12Department of Biostatistics, Harvard T.H. Chan School of Public Health, USA.
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Martin Widschwendter
13Statistical Cancer Genomics, Paul O’Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, United Kingdom.
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Andrew E. Teschendorff
1CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
13Statistical Cancer Genomics, Paul O’Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, United Kingdom.
14Department of Women’s Cancer, University College London, 74 Huntley Street, London, United Kingdom.
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  • For correspondence: a.teschendorff@ucl.ac.uk
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Abstract

Recently, a study by Rahmani et al [1] claimed that a reference-free cell-type deconvolution method, called ReFACTor, leads to improved power and improved estimates of cell-type composition compared to competing reference-free and reference-based methods in the context of Epigenome-Wide Association Studies (EWAS). However, we identified many critical flaws (both conceptual and statistical in nature), which seriously question the validity of their claims. We outlined constructive criticism in a recent correspondence letter, Zheng et al [2]. The purpose of this letter is two-fold. First, to present additional analyses, which demonstrate that our original criticism is statistically sound. Second, to highlight additional serious concerns, which Rahmani et al have not yet addressed. In summary, we find that ReFACTor has not been demonstrated to outperform state-of-the-art reference-free methods such as SVA or RefFreeEWAS, nor state-of-the-art reference-based methods. Thus, the claim by Rahmani et al (a claim reiterated in their recent response letter [3]) that ReFACT or represents an advance over the state-of-the-art is not supported by an objective and rigorous statistical analysis of the data.

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Posted March 28, 2017.
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Correcting for cell-type heterogeneity in epigenome-wide association studies: premature analyses and conclusions
Shijie C Zheng, Stephan Beck, Andrew E. Jaffe, Devin C. Koestler, Kasper D. Hansen, Andres E. Houseman, Rafael A. Irizarry, Martin Widschwendter, Andrew E. Teschendorff
bioRxiv 121533; doi: https://doi.org/10.1101/121533
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Correcting for cell-type heterogeneity in epigenome-wide association studies: premature analyses and conclusions
Shijie C Zheng, Stephan Beck, Andrew E. Jaffe, Devin C. Koestler, Kasper D. Hansen, Andres E. Houseman, Rafael A. Irizarry, Martin Widschwendter, Andrew E. Teschendorff
bioRxiv 121533; doi: https://doi.org/10.1101/121533

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