An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray - PubMed (original) (raw)

An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray

Lucas A Salas et al. Genome Biol. 2018.

Abstract

Genome-wide methylation arrays are powerful tools for assessing cell composition of complex mixtures. We compare three approaches to select reference libraries for deconvoluting neutrophil, monocyte, B-lymphocyte, natural killer, and CD4+ and CD8+ T-cell fractions based on blood-derived DNA methylation signatures assayed using the Illumina HumanMethylationEPIC array. The IDOL algorithm identifies a library of 450 CpGs, resulting in an average R2 = 99.2 across cell types when applied to EPIC methylation data collected on artificial mixtures constructed from the above cell types. Of the 450 CpGs, 69% are unique to EPIC. This library has the potential to reduce unintended technical differences across array platforms.

Keywords: Adults; B-cells; Cytotoxic T-lymphocytes; DNA methylation; Epigenetics; Helper T-cells; Leukocytes; Monocytes; Natural killer cells; Neutrophils.

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Conflict of interest statement

Cells used in these experiments were obtained commercially. All donors are anonymous. All the subjects provided written informed consent before donation to the commercial houses which provided the commercial cells.

Competing interests

The authors declare that they have no competing interests.

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Figures

Fig. 1

Fig. 1

Comparison of L-DMR libraries among automatic selection in minfi and the IDOL algorithm for optimization. a Reinius reference dataset [13] probes from the 450 K array (n = 600 CpGs). b Probes selected from the new reference samples measured with the EPIC array (n = 600 CpGs). c L-DMR library derived from IDOL using the EPIC array (n = 450 CpGs). d Overlapping of the probes of the three methods. DHS DNase hypersensitive sites

Fig. 2

Fig. 2

Comparison of estimate cell proportions using constrained projection/quadratic programming (CP/QP) versus the reconstructed (true) DNA fraction in the artificial DNA mixtures using the EPIC IDOL method. a Cell-specific DNA proportions per sample included in the two mixture reconstruction methods (methods A and B). b R2 and RMSE using the EPIC IDOL method and the two reconstruction methods

Fig. 3

Fig. 3

Observed estimates of absolute error by deconvolution method per cell type (top panel) and global per method (bottom panel)

Fig. 4

Fig. 4

Comparison of the longitudinal assessment of cell type proportions and cell ratio changes using DNA methylation data and two different reference L-DMR libraries (EPIC IDOL and 450 K)

Fig. 5

Fig. 5

Comparison of the estimated cell proportions using constrained projection/quadratic programming (CP/QP) versus the FACS measured fraction in EPIC and 450 K platforms. a Whole blood cell samples arrayed using the EPIC platform with known (FACS) fractions for the six main cell subtypes. Cell estimates were obtained using the EPIC IDOL method. b Whole blood cell samples arrayed using the Illumina 450 K platform with known (FACS) fractions for the six main cell subtypes. Cell estimates were obtained using the EPIC IDOL 450 K legacy method. c Five out of 11 observations on the longitudinal dataset run with EPIC had FACS information

Fig. 6

Fig. 6

Examples of critical CpGs for cell deconvolution selected by IDOL

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