Evidence map›Paper›PMID 27358049›Full record

ArticleBMC bioinformatics2016

Reference-free deconvolution of DNA methylation data and mediation by cell composition effects.

E Andres Houseman, Molly L Kile, David C Christiani, Tan A Ince, Karl T Kelsey, Carmen J Marsit

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PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 156 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
156citing papers in PubMed, 7 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

156 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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96 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

E Andres HousemanSchool of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA. andres.houseman@oregonstate.edu.ORCID http://orcid.org/0000-0003-0703-1830
Molly L KileSchool of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA.
David C ChristianiDepartment of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Tan A InceDepartment of Pathology, University of Miami, Miller School of Medicine, Miami, FL, USA.
Karl T KelseyDepartment of Epidemiology, Department of Pathology and Laboratory Medicine, Brown University, Providence, USA.
Carmen J MarsitDepartment of Community and Family Medicine, Dartmouth Medical School, Hanover, NH, USA.

Funding

Project 3: Placental Biomarkers of Exposure and Outcome (Marsit)P01ES022832 · NIEHS · DARTMOUTH COLLEGE · PI KARAGAS, MARGARET RITA · 2013 to 2018
$4.7M
Epigenetics in Neurodevelopment and Mental HealthR01MH094609 · NIMH · DARTMOUTH COLLEGE · PI MARSIT, CARMEN JOSEPH · 2011 to 2015
$2.3M
Arsenic Exposure and Birth Outcomes in BangladeshR01ES015533 · NIEHS · HARVARD SCHOOL OF PUBLIC HEALTH · PI CHRISTIANI, DAVID C · 2007 to 2011
$2.1M
Epigenetic Effects of Prenatal Arsenic Exposure and Fetal GrowthK01ES017800 · NIEHS · OREGON STATE UNIVERSITY · PI KILE, MOLLY L · 2010 to 2014
$529k
Computational and Biological Deconvolution of Epigenomic DatasetsR01ES024991 · NIEHS · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI INCE, TAN A. · 2014 to 2014
$323k
NIEHS NIH HHS K01 ES017800NIEHS NIH HHS K01-ES017800NIEHS NIH HHS P01 ES022832NIEHS NIH HHS P01-ES022832NIEHS NIH HHS R01 ES015533NIEHS NIH HHS R01-ES015533NIEHS NIH HHS R01 ES024991NIEHS NIH HHS R01-ES024991NIMH NIH HHS R01 MH094609NIMH NIH HHS R01-MH094609
6 · The paper itself

Abstract

backgroundRecent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying cell types.

resultsWe propose a simple method for reference-free deconvolution that provides both proportions of putative cell types defined by their underlying methylomes, the number of these constituent cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of cells. We demonstrate these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent cell types.

conclusionsOur methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by cell composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data.

Indexed as

AlgorithmsDNA MethylationEpigenomicsHumansNeoplasmsDeconvolutionDNA methylationEpigeneticsNon-negative matrix factorization

Identifiers

PMID27358049
PMCPMC4928286

What OpenQuestion holds

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Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.