Literature DB >> 28245219

Correcting for cell-type heterogeneity in epigenome-wide association studies: revisiting previous analyses.

Shijie C Zheng1,2, Stephan Beck3, Andrew E Jaffe4,5, Devin C Koestler6, Kasper D Hansen7,8,4, Andres E Houseman9, Rafael A Irizarry10,11, Andrew E Teschendorff1,12,13.   

Abstract

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Year:  2017        PMID： 28245219      PMCID： PMC6659733          DOI： 10.1038/nmeth.4187

Source DB:  PubMed          Journal:  Nat Methods        ISSN： 1548-7091            Impact factor:   28.547


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  9 in total

1.  Using control genes to correct for unwanted variation in microarray data.

Authors:  Johann A Gagnon-Bartsch; Terence P Speed
Journal:  Biostatistics       Date:  2011-11-17       Impact factor: 5.899

2.  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

3.  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

4.  Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies.

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:  2016-03-28       Impact factor: 28.547

5.  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

6.  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

7.  DNA methylation arrays as surrogate measures of cell mixture distribution.

Authors:  Eugene Andres Houseman; William P Accomando; Devin C Koestler; Brock C Christensen; Carmen J Marsit; Heather H Nelson; John K Wiencke; Karl T Kelsey
Journal:  BMC Bioinformatics       Date:  2012-05-08       Impact factor: 3.169

8.  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

9.  Accounting for cellular heterogeneity is critical in epigenome-wide association studies.

Authors:  Andrew E Jaffe; Rafael A Irizarry
Journal:  Genome Biol       Date:  2014-02-04       Impact factor: 13.583
  9 in total
  24 in total

1.  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

2.  Cumulative lifetime maternal stress and epigenome-wide placental DNA methylation in the PRISM cohort.

Authors:  Kelly J Brunst; Nicole Tignor; Allan Just; Zhonghua Liu; Xihong Lin; Michele R Hacker; Michelle Bosquet Enlow; Robert O Wright; Pei Wang; Andrea A Baccarelli; Rosalind J Wright
Journal:  Epigenetics       Date:  2018-08-15       Impact factor: 4.528

3.  Variability in DNA methylation at the serotonin transporter gene promoter: epigenetic mechanism or cell-type artifact?

Authors:  Sarah R Moore; Michael S Kobor
Journal:  Mol Psychiatry       Date:  2018-08-06       Impact factor: 15.992

Review 4.  Analysis of DNA modifications in aging research.

Authors:  Dustin R Masser; Niran Hadad; Hunter Porter; Michael B Stout; Archana Unnikrishnan; David R Stanford; Willard M Freeman
Journal:  Geroscience       Date:  2018-01-11       Impact factor: 7.713

5.  Epigenetic marks of prenatal air pollution exposure found in multiple tissues relevant for child health.

Authors:  Christine Ladd-Acosta; Jason I Feinberg; Shannon C Brown; Frederick W Lurmann; Lisa A Croen; Irva Hertz-Picciotto; Craig J Newschaffer; Andrew P Feinberg; M Daniele Fallin; Heather E Volk
Journal:  Environ Int       Date:  2019-02-28       Impact factor: 9.621

Review 6.  Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health.

Authors:  Diana M Manu; Jessica Mwinyi; Helgi B Schiöth
Journal:  Int J Mol Sci       Date:  2022-05-23       Impact factor: 6.208

Review 7.  Statistical and integrative system-level analysis of DNA methylation data.

Authors:  Andrew E Teschendorff; Caroline L Relton
Journal:  Nat Rev Genet       Date:  2017-11-13       Impact factor: 53.242

8.  Systems-epigenomics inference of transcription factor activity implicates aryl-hydrocarbon-receptor inactivation as a key event in lung cancer development.

Authors:  Yuting Chen; Martin Widschwendter; Andrew E Teschendorff
Journal:  Genome Biol       Date:  2017-12-20       Impact factor: 13.583

9.  Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM.

Authors:  Jerry Fong; Jacob R Gardner; Jared M Andrews; Amanda F Cashen; Jacqueline E Payton; Kilian Q Weinberger; John R Edwards
Journal:  Nucleic Acids Res       Date:  2021-09-20       Impact factor: 16.971

10.  Detecting cord blood cell type-specific epigenetic associations with gestational diabetes mellitus and early childhood growth.

Authors:  Tianyuan Lu; Andres Cardenas; Patrice Perron; Marie-France Hivert; Luigi Bouchard; Celia M T Greenwood
Journal:  Clin Epigenetics       Date:  2021-06-26       Impact factor: 6.551
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