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BMC Bioinformatics. 2016 Mar 8;17:120. doi: 10.1186/s12859-016-0943-7.

Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL).

Author information

1
Department of Biostatistics, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, 66160, KS, USA. dkoestler@kumc.edu.
2
Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, The University of British Columbia, 950 West 28th Ave., Vancouver, V5Z 4H4, BC, Canada. mjones@cmmt.ubc.ca.
3
Department of Biostatistics, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, 66160, KS, USA. jusset@kumc.edu.
4
Department of Epidemiology, Geisel School of Medicine, Dartmouth College, 1 Medical Center Dr., Lebanon, 03756, NH, USA. brock.c.christensen@dartmouth.edu.
5
Department of Pharmacology and Toxicology, Dartmouth College, 1 Rope Ferry Rd., Hanover, 03755, NH, USA. brock.c.christensen@dartmouth.edu.
6
Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, 1 Medical Center Dr., Lebanon, 03756, NH, USA. brock.c.christensen@dartmouth.edu.
7
Department of Pathology and Laboratory Medicine, Brown University, 70 Ship St., Providence, 02912, RI, USA. rondi_butler@brown.edu.
8
Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, The University of British Columbia, 950 West 28th Ave., Vancouver, V5Z 4H4, BC, Canada. msk@cmmt.ubc.ca.
9
Department of Neurological Surgery, University of California San Francisco, 505 Parnassus Ave., San Francisco, 94143, CA, USA. john.wiencke@ucsf.edu.
10
Department of Pathology and Laboratory Medicine, Brown University, 70 Ship St., Providence, 02912, RI, USA. karl_kelsey@brown.edu.
11
Department of Epidemiology, Brown University, 121 South Main St., Providence, 02912, RI, USA. karl_kelsey@brown.edu.

Abstract

BACKGROUND:

Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogenous biospecimens offer a promising solution, however the performance of such methods depends entirely on the library of methylation markers being used for deconvolution. Here, we introduce a novel algorithm for Identifying Optimal Libraries (IDOL) that dynamically scans a candidate set of cell-specific methylation markers to find libraries that optimize the accuracy of cell fraction estimates obtained from cell mixture deconvolution.

RESULTS:

Application of IDOL to training set consisting of samples with both whole-blood DNA methylation data (Illumina HumanMethylation450 BeadArray (HM450)) and flow cytometry measurements of cell composition revealed an optimized library comprised of 300 CpG sites. When compared existing libraries, the library identified by IDOL demonstrated significantly better overall discrimination of the entire immune cell landscape (p = 0.038), and resulted in improved discrimination of 14 out of the 15 pairs of leukocyte subtypes. Estimates of cell composition across the samples in the training set using the IDOL library were highly correlated with their respective flow cytometry measurements, with all cell-specific R (2)>0.99 and root mean square errors (RMSEs) ranging from [0.97 % to 1.33 %] across leukocyte subtypes. Independent validation of the optimized IDOL library using two additional HM450 data sets showed similarly strong prediction performance, with all cell-specific R (2)>0.90 and R M S E<4.00 %. In simulation studies, adjustments for cell composition using the IDOL library resulted in uniformly lower false positive rates compared to competing libraries, while also demonstrating an improved capacity to explain epigenome-wide variation in DNA methylation within two large publicly available HM450 data sets.

CONCLUSIONS:

Despite consisting of half as many CpGs compared to existing libraries for whole blood mixture deconvolution, the optimized IDOL library identified herein resulted in outstanding prediction performance across all considered data sets and demonstrated potential to improve the operating characteristics of EWAS involving adjustments for cell distribution. In addition to providing the EWAS community with an optimized library for whole blood mixture deconvolution, our work establishes a systematic and generalizable framework for the assembly of libraries that improve the accuracy of cell mixture deconvolution.

PMID:
26956433
PMCID:
PMC4782368
DOI:
10.1186/s12859-016-0943-7
[Indexed for MEDLINE]
Free PMC Article

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