Literature DB >> 35505206

Using R for Cell-Type Composition Imputation in Epigenome-Wide Association Studies.

Chong Wu1.   

Abstract

Adjusting cell type composition is challenging but critical in epigenome-wide association studies (EWAS). In this chapter, we describe how to apply reference-based and reference-free methods in R to impute cell type composition in whole blood samples.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Cell-type heterogeneity; DNA methylation; Reference-based method; Reference-free method

Mesh：

Year:  2022        PMID： 35505206     DOI： 10.1007/978-1-0716-1994-0_4

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN： 1064-3745


  13 in total

1.  Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies.

Authors:  Andrew E Teschendorff; Joanna Zhuang; Martin Widschwendter
Journal:  Bioinformatics       Date:  2011-04-06       Impact factor: 6.937

2.  Correcting for cell-type heterogeneity in DNA methylation: a comprehensive evaluation.

Authors:  Elior Rahmani; Noah Zaitlen; Yael Baran; Celeste Eng; Donglei Hu; Joshua Galanter; Sam Oh; Esteban G Burchard; Eleazar Eskin; James Zou; Eran Halperin
Journal:  Nat Methods       Date:  2017-02-28       Impact factor: 28.547

Review 3.  Cell-type deconvolution in epigenome-wide association studies: a review and recommendations.

Authors:  Andrew E Teschendorff; Shijie C Zheng
Journal:  Epigenomics       Date:  2017-03-14       Impact factor: 4.778

4.  Correlation of Smoking-Associated DNA Methylation Changes in Buccal Cells With DNA Methylation Changes in Epithelial Cancer.

Authors:  Andrew E Teschendorff; Zhen Yang; Andrew Wong; Christodoulos P Pipinikas; Yinming Jiao; Allison Jones; Shahzia Anjum; Rebecca Hardy; Helga B Salvesen; Christina Thirlwell; Samuel M Janes; Diana Kuh; Martin Widschwendter
Journal:  JAMA Oncol       Date:  2015-07       Impact factor: 31.777

5.  Epigenome-wide association studies without the need for cell-type composition.

Authors:  James Zou; Christoph Lippert; David Heckerman; Martin Aryee; Jennifer Listgarten
Journal:  Nat Methods       Date:  2014-01-26       Impact factor: 28.547

6.  Charting a dynamic DNA methylation landscape of the human genome.

Authors:  Michael J Ziller; Hongcang Gu; Fabian Müller; Julie Donaghey; Linus T-Y Tsai; Oliver Kohlbacher; Philip L De Jager; Evan D Rosen; David A Bennett; Bradley E Bernstein; Andreas Gnirke; Alexander Meissner
Journal:  Nature       Date:  2013-08-07       Impact factor: 49.962

7.  Reference-free cell mixture adjustments in analysis of DNA methylation data.

Authors:  Eugene Andres Houseman; John Molitor; Carmen J Marsit
Journal:  Bioinformatics       Date:  2014-01-21       Impact factor: 6.937

8.  Robust enumeration of cell subsets from tissue expression profiles.

Authors:  Aaron M Newman; Chih Long Liu; Michael R Green; Andrew J Gentles; Weiguo Feng; Yue Xu; Chuong D Hoang; Maximilian Diehn; Ash A Alizadeh
Journal:  Nat Methods       Date:  2015-03-30       Impact factor: 28.547

9.  Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis.

Authors:  Yun Liu; Martin J Aryee; Leonid Padyukov; M Daniele Fallin; Espen Hesselberg; Arni Runarsson; Lovisa Reinius; Nathalie Acevedo; Margaret Taub; Marcus Ronninger; Klementy Shchetynsky; Annika Scheynius; Juha Kere; Lars Alfredsson; Lars Klareskog; Tomas J Ekström; Andrew P Feinberg
Journal:  Nat Biotechnol       Date:  2013-01-20       Impact factor: 54.908

10.  Capturing heterogeneity in gene expression studies by surrogate variable analysis.

Authors:  Jeffrey T Leek; John D Storey
Journal:  PLoS Genet       Date:  2007-08-01       Impact factor: 5.917
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