DNA methylation arrays as surrogate measures of cell mixture distribution
 Eugene Andres Houseman^{1}Email author,
 William P Accomando^{2},
 Devin C Koestler^{3},
 Brock C Christensen^{3},
 Carmen J Marsit^{3},
 Heather H Nelson^{4},
 John K Wiencke^{5} and
 Karl T Kelsey^{2, 6}
DOI: 10.1186/147121051386
© Houseman et al.; licensee BioMed Central Ltd. 2012
Received: 22 February 2012
Accepted: 20 April 2012
Published: 8 May 2012
Abstract
Background
There has been a longstanding need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immunetoxic exposures, leukocyte composition will critically inform the underlying immunobiology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls.
Results
Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach.
Conclusions
Our method, in combination with an appropriate external validation set, promises new opportunities for largescale immunological studies of both disease states and noxious exposures.
Background
The biology of the development of any multisystem life form is fundamentally grounded in systematic cellular differentiation. This is essentially defined by lineage commitment of cells whose origin can be traced to a pluripotent progenitor and is marked by mitotically heritable epigenetic changes that reflect complex transcriptional programming of gene expression within the individual cell[1–3]. One such epigenetic mark is DNA methylation, which is tightly associated with alterations in the nucleosome DNA scaffold (and hence chromatin) that is responsible for coordination of gene expression in individual cells[1–3]. It is now appreciated that differentially methylated DNA regions (DMRs) distinguish cell lineages with high sensitivity and specificity[4] and considerable research is now underway to delineate precise DMRs that define and specify a particular cell lineage. The most developed understanding of epigenetic markers of lineage commitment to date is perhaps that of immune cell subclasses defined by populations of distinct circulating blood cells[5, 6].
Pluripotent hematopoietic stem cells residing in the bone marrow continually give rise to the entire hierarchy of blood cell subclasses through a developmental process known as hematopoiesis. Leukocytes, commonly called white blood cells, are critical in the host response to pathogens and foreign antigens and are divided into two compartments, the myeloid lineage and lymphoid lineage (also called lymphocytes). The composition of leukocyte populations is well known to reflect disease states and toxicant exposures and can be altered by signaling cascades that prompt migration of whole classes of cells into or out of tissues. Several DMRs that serve as reliable biomarkers of individual human white blood cell types have already been identified[5, 6]. Individual assays identifying cellspecific DMRs have proven useful for quantifying individual cell types in human tissues and peripheral blood. However, these assays are limited to detecting the relative proportion of one individual cell type compared with all others. On the other hand, simultaneous quantification of fluctuation in overall lymphocyte population composition can be accomplished only by using methods based on flow cytometry, which require large volumes of fresh blood and involve laborious antibody tagging. Hence, an approach that allows for the simultaneous quantification of the entire distribution of cell types, using an array of biomarkers based on generally available technology, would be considerably more informative, especially in studies of human disease and exposures.
In some instances, it is generally the overall balance of leukocyte subclasses in circulation or tissue that most prominently influences pathogenesis. For example, although incipient cancer cells are recognized and eliminated by cytotoxic Tcells (CTLs) and natural killer (NK) cells, tumorigenesis is also promoted by certain other inflammatory cells, including Blymphocytes, mast cells, neutrophils, regulatory Tcells (Tregs), and numerous others. All of these cells have been shown to promote angiogenesis, tumor cell proliferation, tissue invasion and metastasis[7, 8]. Likewise, while higher levels of NK cells and CTLs circulating in the blood and residing in adipose tissues are associated with lower incidence of metabolic diseases such as type II diabetes[9], higher levels of M1 macrophages in adipose tissue can induce inflammation and insulin resistance[10]. These examples illustrate incredible potential for methods of quantifying the composition of lymphocyte populations to critically inform the underlying immunobiology of disease states as well as the immune response to almost all chronic medical conditions. In addition, they offer great potential for predicting therapeutic outcomes[11].
Here we employ the concept of DMRs as markers of immune cell identity using a high density methylation platform, and propose a set of analytical tools for estimating the proportions of immune cells in unfractionated whole blood that does not require fresh cells. The backbone of the approach is the DNA methylation signature of each of the principal immune components of whole blood (B cells, granulocytes, monocytes, NK cells, and T cells subsets). We essentially seek a form of regression calibration, where we consider a methylation signature to be a highdimensional multivariate surrogate for the distribution of white blood cells. In turn, this distribution is of interest for predicting or modeling disease states. As a surrogate, the DNA methylation signature is assumed to be a highly correlated, yet imperfect, measure of leukocyte distribution, and thus fits into the framework of measurement error models, where the use of a noisy surrogate marker to investigate an association with a disease outcome of interest results in biased estimates, unless internal or external validation data can be obtained to “calibrate” the model and correct the bias[12]. However, in this case, the problem is complicated by the extremely high dimension of the surrogate, so we propose an alternative to the traditional regressioncalibration procedure that circumvents these complications but still allows us to extract the desired biological information.
We note that since we began this work, a small number of authors have published similar deconvolution algorithms using gene expression data[13–15]. The techniques are similar to the quadratic programming method we describe below in Methods for deconvolving a single sample, but none comprehensively addresses statistical properties or employs data from DNA methylation.
Methods
In this section we describe our proposed statistical methods, the data sets used to demonstrate their utility, and finally the design of simulation studies we have conducted to investigate statistical properties of our proposed algorithms.
Statistical methods
where B_{0} and B_{1} are, respectively, m × d_{0} and m × d_{1} matrices and e_{0} and e_{1} are error vectors. For simplicity we assume a oneway ANOVA parameterization for w, though in the Additional file1 we describe slight generalizations to account for design complications met in practice. We also assume a reasonable regression parameterization for z, including an intercept, and for convenience, denote the first column of B_{0} as μ_{1}, the m × 1 intercept. The error vectors e_{0} and e_{1} may reflect independence among arrays h and i, or else may have more complex random effects structure accounting for technical effects or biological replication; however, their substructures are incidental to this analysis, with the exception of the fine details of the bootstrap procedure proposed below.
where the m × 1 vector ξ^{(z)} represents cell types excluded from consideration among the purified samples in S_{0}, or else noncellspecific methylation, including alterations at the molecular level in the maintanence of DNA methylation patterns themselves (possibly exposure related, age, or disease related). It follows from (3) and (4) that the mixture coefficients are recoverable from Γ,${\omega}_{l}^{\left(\mathbf{z}\right)}={\gamma}_{l}^{\mathrm{T}}{\mathbf{z}}_{1i}$, provided ξ^{(z)} is orthogonal to the column space of B_{0}. As we discuss in detail in the Additional file1, bias can arise if differences in ξ^{(z)} between distinct values of z have nonzero projection onto the column space of B_{0}, although the magnitude of anticipated biases can be assessed through sensitivity analysis.
It is possible to assign interpretations to the components of variation in (3). Let SS_{ o } represents overall variability in Y_{1i}, i.e.$S{S}_{o}=\sum _{i=1}^{{n}_{1}}\parallel {\mathbf{Y}}_{1i}{\stackrel{\u0304}{\mu}}_{1}{\parallel}^{2}$, where${\stackrel{\u0304}{\mu}}_{1}=\mathrm{E}\left({\mathbf{Y}}_{1i}\right)$. From multivariate probability theory it is straightforward to show that S S_{ o }= S S_{ e } + S S_{ v } + S S_{ u }, where$S{S}_{e}=\sum _{i=1}^{{n}_{1}}\parallel {\mathbf{e}}_{1i}{\parallel}^{2}$,$S{S}_{v}=\sum _{i=1}^{{n}_{1}}{({\mathbf{z}}_{1i}{\stackrel{\u0304}{\mathbf{z}}}_{1})}^{\mathrm{T}}{\Gamma}^{\mathrm{T}}{\mathbf{B}}_{0}^{\mathrm{T}}{\mathbf{B}}_{0}\Gamma ({\mathbf{z}}_{1i}{\stackrel{\u0304}{\mathbf{z}}}_{1})$, and$S{S}_{u}=\sum _{i=1}^{{n}_{1}}\left\{{({\mathbf{z}}_{1i}{\stackrel{\u0304}{\mathbf{z}}}_{1})}^{\mathrm{T}}{\mathbf{U}}^{\mathrm{T}}\mathbf{U}\right({\mathbf{z}}_{1i}{\stackrel{\u0304}{\mathbf{z}}}_{1})+m{({\mathbf{z}}_{1i}{\stackrel{\u0304}{\mathbf{z}}}_{1})}^{\mathrm{T}}{\gamma}_{0}{\gamma}_{0}^{\mathrm{T}}({\mathbf{z}}_{1i}{\stackrel{\u0304}{\mathbf{z}}}_{1}\left)\right\}$. S S_{ e } measures variation unexplained by the covariates z_{1i}, presumed to represent a combination of technical noise and unsystematic biological heterogeneity. SS_{ v } measures variability explained by mixtures of profiles in the set S_{0}, while SS_{ u } measures variability in systematic biological heterogeneity that nevertheless remains unexplained by mixtures of profiles in S_{0}, presumably due to some process other than differences in mixtures of cell types. Thus we propose two partial coefficient of determination measures:${R}_{1,0}^{2}=S{S}_{v}/S{S}_{o}$, which represents the proportion of total variation in S_{1} explained by S_{0}, and${R}_{1,1}^{2}=S{S}_{v}/(S{S}_{o}S{S}_{e})$, which represents the proportion of systematic variation in S_{1} explained by S_{0}. Note that${R}_{1,1}^{2}$ is poorly defined when S S_{ o }≈S S_{ e }.
Estimation procedes by applying an appropriate linear model, e.g. ordinary least squares, linear mixed effects models[16], limma[17], or surrogate variable analysis[18, 19], to obtain estimates${\hat{\mathbf{B}}}_{0}$ and${\hat{\mathbf{B}}}_{1}$. Estimates of γ_{0} and Γ are then obtained by projecting${\hat{\mathbf{B}}}_{1}$ onto the column space of${\stackrel{~}{\mathbf{B}}}_{0}=({1}_{m},{\mathbf{B}}_{0})$, as described in detail in the Additional file1. Standard errors can be obtained in one of three ways. The simplest estimator, S E_{0}, is the “naive” estimator from simple leastsquares theory, ignoring the fact that${\hat{\mathbf{B}}}_{0}$ and${\hat{\mathbf{B}}}_{1}$ are estimates, i.e. potentially variable. To account for variation in estimating${\hat{\mathbf{B}}}_{1}$, a simple alternative is to use a nonparametric bootstrap procedure. For each bootstrap iteration t, we sample with replacement from S_{1} (or sample errors in a manner consistent with a hierarchical experimental design) to obtain${S}_{1}^{\left(t\right)}$, producing bootstrap estimates${\hat{\mathbf{B}}}_{1}^{\left(t\right)}$ from which “singlebootstrap” standard errors SE_{1} are computed. Finally, it is possible to account for variation in estimating _{B 0}by also bootstrapping S_{0}; because of potentially small sample sizes n_{0}, we propose using a parametric bootstrap. A“doublebootstrap” standard error estimator, SE_{2}, is computed from these two sets of bootstraps. The doublebootstrap has the additional benefit over the singlebootstrap, in that it can be used to assess bias due to measurement error (variability) in${\hat{\mathbf{B}}}_{0}$. Estimation details are provided in the Additional file1, as are the results of simulation studies.
Beyond bias due to measurement error, which is easily corrected using the doublebootstrap procedure, there are additional sources of potential bias. For example, consider a univariate z_{1i} representing case/control status, where$\delta \equiv {\xi}^{\left(1\right)}{\xi}^{\left(0\right)}={\mathbf{B}}_{0}\alpha $ for some d_{0} × 1 vector α ≠ 0; i.e. δ is the mean difference in DNA methylation between a case and control, contributed by cell mixtures that remain uncharacterized or noncellspecific methylation. In such a situation, there will be a bias equal to α in estimating the mixture differences. The Additional file1 provides a detailed analysis of such biases, and proposes a sensitivity analysis procedure for assessing the magnitude of possible bias in a given data set.
While the focus of this paper is analysis of population data, it is possible to use S_{0} to predict distribution of leukocytes in a single sample having DNA methylation profile Y^{∗}. Equating the intercept term of B_{1} in (1) with Y^{∗} and applying (2), we obtain mixing proportion estimates${\Gamma}^{\ast}={\left({\stackrel{~}{\mathbf{B}}}_{0}^{\mathrm{T}}{\stackrel{~}{\mathbf{B}}}_{0}\right)}^{1}{\stackrel{~}{\mathbf{B}}}_{0}^{\mathrm{T}}{\mathbf{Y}}^{\ast}$. Estimates can be further refined with the use of quadratic programming techniques[20], restricting the components of Γ^{∗},γ_{ l }^{∗} ≥ 0, in minimizing$\parallel {\mathbf{Y}}^{\ast}{\stackrel{~}{\mathbf{B}}}_{0}{\Gamma}^{\ast}{\parallel}^{2}$ with respect to Γ^{∗}. Such individual projections of methylation profiles on the column space spanned by S_{0} facilitate the application of the fundamental ideas proposed above to individual, clinicallybased diagnostic procedures. Note, however, that DNA methylation arrays are typically focused on the comparison of methylated to unmethylated CpG dinucleotides, not quantifying actual amounts of DNA. Therefore, information on cell mixtures from DNA methylation is limited to distributions, not actual counts, as one might obtain from flow cytometry. Finally, we remark that it is possible to model z_{1i} directly as a function of mixture coefficients Γ^{∗} obtained individually via the constraint γ_{ l }^{∗} ≥ 0, but the inferential implications are less clear, and we view the proposed approach for populations as more statistically robust.
Implementation
We describe several examples using existing methylation data sets as benchmarks for validating the proposed method, in order to demonstrate its clinical or epidemiological utility. First we describe the validation data set S_{0} used in all examples. Next we describe a laboratory reconstruction experiment, which validates our fundamental proposition that DNA methylation retains substantial information about cell mixtures. Finally we describe the results of applying our methodology to several different target data sets S_{1}. For the head and neck cancer and ovarian cancer data sets, from which bead chip data were available, a linear mixed effects model with a random intercept for bead chip was used to estimate the corresponding row of B_{1}. For the remaining data sets, no bead chip data were available; consequently, ordinary least squares was used. 250 bootstrap iterations were used for each example and each of the two bootstrap methods of standard error estimation.
Validation data
Sorted white blood cells in S_{0}
Short name  Description  Number 

B cells  CD19+ Blymphocytes  6 
Granulocytes  CD15+ granulocytes  8 
Monocytes  CD14+ monocytes  5 
NK  CD56+ Natural Killer (NK) cells  11 
T cells (CD4+)^{1,2}  CD3+CD4+ Tlymphocytes  8 
T cells (CD8+)^{1,3}  CD3+CD8+ Tlymphocytes  2 
T cells (NKT)^{1}  CD3+CD56+ natural killer  1 
T cells (other)^{1}  CD3+ Tlymphocytes  5 
Cell mixture experiment
Proof of the utility of the proposed methods in predicting leukocyte distributions for individual samples requires extensive, detailed reconstruction experiments beyond the scope of the present paper. However, to provide evidence that such experiments are worthwhile and show promise of positive results, we conducted a simple experiment involving six known mixtures of monocytes and B cells and six known mixtures of granulocytes and T cells. The results of this experiment are described below in Results.
Head and neck cancer
Ovarian cancer
We next applied our method to an ovarian cancer data set[22]. DNA methylation data for blood samples are available from Gene Expression Omnibus (GEO,http://www.ncbi.nlm.nih.gov/geo/, Accession number GSE19711). We used only those cases having blood drawn pretreatment. After removing 4 arrays with a preponderance of missing values, the data set consisted of 272 controls and 129 cases having blood drawn prior to treatment. A clustering heatmap displaying the DNA methylation data appears in the Additional file1. In this analysis, z consisted of casecontrol status, age (categorized in 5year increments), and 2 bisulfite conversion efficiency measures.
Down syndrome
We also applied our method to a trisomy 21 (Down syndrome) data set[23] consisting of 29 total peripheral blood leukocyte samples from Down syndrome cases and 21 controls, as well as 6 T cell samples from cases and 4 T cell samples from controls (GEO Accession number GSE25395). Because of the potential for bias induced by copy number amplification, we excluded 4 CpG sites on Chromosome 21, resulting in m = 96 CpG sites used for analysis. A clustering heatmap displaying the DNA methylation data appears in the Additional file1. In one analysis, we compared cases and controls using the total leukocyte samples only, and in another we compared total leukocytes to T cells, pooling cases and controls. The Additional file1. presents coefficient estimates.
Obesity in African Americans
Finally, we applied our method to an obesity data set[24] consisting of 7 lean AfricanAmericans and 7 Obese AfricanAmericans (GEO Accession number GSE25301). A clustering heatmap displaying the DNA methylation data appears in the Additional file1. In this analysis, z consisted of obesity status.
Additional analyses
If the subject population for which z = 0 is sufficiently homogeneous with respect to blood cell distribution to admit sensible characterization of that distribution, then it is possible to recover estimates from$\hat{\Gamma}$. The Additional file1 reports the results of such an analysis applied to the HNSCC case/control data set. Finally, we conducted an additional analysis where we took S_{0} to consist of only samples with pure CD4+ or CD8+ cells and S_{1} to consist only of samples having the less purified Tlymphocytes. For such S_{1}, there were no covariates, so z consisted only of an intercept.
Simulations
We specified n_{1}/2 cases and n_{0}/2 controls, n_{0}∈{100,200,500}. Among the controls, methylation profiles were generated by a white blood cell population of 7% Bcells, 62% granulocytes, 6% monocytes, 2% NK cells, and 13% were Tcells, of which 65% were CD4+ cells and 35% were CD8+ cells, and the remaining 5% were unspecified (and assumed to have mean methylation equal to that of the unsorted Tlymphocytes). Among cases, we specified one of the following scenarios: a 4% reduction in CD4+ cells, a 2% reduction in CD8+ cells, and an 8% increase in granulocytes (alternative with changes in both CD4+ and CD8+, “Strong Alternative I”); a 6% reduction in CD4+ cells, and an 8% increase in granulocytes (alternative with changes in CD4+ but not CD8+, “Strong Alternative II”); a weaker alternative with half the effects of Strong Alternative I (“Mixed Alternative” elaborated upon below); and two null scenarios with no changes in cell population, each with a different assumption about δ. Note that these changes reflect absolute changes in percentage points, not relative changes. Note also that these values were actually used to generate Dirichletdistributed mixture weights for each simulated subject, with Dirichlet parameters equal to a precision parameter (100 corresponding to “precise” and 10 corresponding to“noisy”) times the mean weight described above. Residual effects${\xi}_{i}^{\left(0\right)}$ for controls were set equal to 0.1 times estimated intercept estimate${\hat{\mu}}_{1}$ obtained from the HNSCC data set, while residual effects${\xi}_{i}^{\left(1\right)}$ for cases were set equal to 0.08 or 0.09 times${\hat{\mu}}_{1}$ plus multiples 10θ of the column of$\hat{\mathbf{U}}$ corresponding to case. The constants of proportionality 0.1, 0.08, and 0.09 were chosen to correspond to assumed contributions of ξ to an overall methylation signature presumed to be dominated by profiled populations of white blood cells in specified proportions, with 0.08 used for the strong alternatives and 0.09 used for the Mixed Alternative. The constant 10 was used to amplify the scale of δ so that its effect could be detected in simulation; note that$\hat{\mathbf{U}}$ was orthogonal to the white blood cell profiles, by construction. The multiplier θ = 0 was used for strong alternatives, and the “Strong Null” case (i.e. no methylation differences between cases and controls) while θ = 0.5 was used for the Mixed Alternative, and θ = 1 was used for the “Mixed Null” with case/control differences not mediated by cellular population differences. A simple normal error structure for e_{0h}and e_{0i} was specified, with no chip effects, but with variance equal to the sum of chip and residual variance estimated (individually for each CpG) for the HNSCC data. For each simulation, 50 bootstraps were used to estimate standard errors. 1000 simulations were run for each scenario.
Results
In this section we report the results of the data analyses described above in Implementation, as well as the results of our simulation experiments.
Cell mixture experiment
Summary statistics for errors in cell mixture reconstruction results^{*}
B cell  Granulocyte  Monocyte  NK  T cell  

minimum  0.0  0.3  0.0  0.0  0.0 
median  0.1  6.5  1.1  2.1  0.3 
maximum  5.5  10.0  4.1  6.4  5.3 
Head and neck cancer
Table3 presents coefficient estimates$\hat{\Gamma}$ for case status, doublebootstrap bias estimates (estimates of bias arising from measurement error), as well as naive, singlebootstrap, and doublebootstrap standard error estimates. Each of these quantities is measured in percentage points (%). Estimates of bias arising from measurement error (i.e. substituting estimated quantities for known ones in a twostage statistical procedure) were almost always less than half a percentage point, and for significant coefficient estimates, always towards the null. The proportion of CD4+ Tlymphocytes decreased in cases compared with controls, with a biascorrected estimate of −10.4 percentage points and approximate 95% confidence interval (−13.1%,−3.3%); the proportion of NK cells decreased, with a biascorrected estimate of 1.5 percentage points and 95% confidence interval (−2.2%,−0.75%); and the proportion of granulocytes increased, with a biascorrected estimate of 7.6 percentage points and 95% confidence interval (4.2%,10.9%). There was also somewhat weaker evidence of an increase in CD8+ Tlymphocytes, with an estimate of 4.5 percentage points and 95% confidence interval (2.0%,7.0%). As reported in the complete set of results appearing in the Additional file1, the proportion of CD4+ Tlymphocytes decreased by 3.3 percentage points (−4.4%,−2.2%) per decade of age, while CD8+ Tlymphocytes increased by 2.0 percentage point (1.0%,3.0%) per decade. All other coefficients were insignificant.
For this analysis,${R}_{1,0}^{2}$ was estimated at 14.2%, while${R}_{1,1}^{2}$ was estimated at 93.9%. Thus, a small but nonnegligible proportion of total variation (systematic variation + unexplained biological heterogeneity + technical noise) appeared to be driven by changes in cell population between cases and controls and as a result of aging. Note that SS_{ e } comprised 85% of total variation, so a substantial portion of variability in DNA methylation appeared to remain unexplained (presumably due, in large part, to technical noise). However, almost all of the systematic variation appeared to be explained by changes in cell population.
These results were consistent with previous studies, as HNSCC patients are known to display an absolute and relative increase in myeloid derived granulocytes[25] while also displaying an alteration in lymphoid Tcell homeostasis that leads to decreases in CD4+ Tcells[26, 27]. In addition, the proportion of Treg cells (a subclass of CD4+ T cells) is known to decrease from infancy to adulthood[28].
The bias estimates obtained from the doublebootstrap procedure allow the correction of bias arising from measurement error. However, there is no statistical procedure for correcting the other possible sources of bias, those arising from changes in distribution among unprofiled cell types as well as nonimmunemediated methylation differences. The Additional file1 presents a detailed sensitivity analysis, from which we show that the magnitude of the resulting bias is likely to be small, less than a percentage point.
Ovarian cancer
Estimates for HNSCC analysis (case vs. control)
Est  Bias_{2}  SE_{0}  SE_{1}  SE_{2}  Pvalue  

(Intercept, γ_{0})  −0.62  −0.02  0.41  0.52  0.52  0.23 
B Cell  −0.45  0.04  0.30  0.77  0.76  0.55 
Granulocyte  7.51  −0.07  0.50  1.73  1.71  <0.0001 
Monocyte  0.49  0.10  0.50  0.47  0.48  0.31 
NK  −1.43  0.06  0.56  0.37  0.38  0.00017 
T Cell (cd4+)  −9.08  1.32  1.95  1.15  1.39  <0.0001 
T Cell (cd8+)  3.06  −1.46  1.96  0.98  1.27  0.016 
Estimates for ovarian cancer analysis (case vs. control)
Est  Bias_{2}  SE_{0}  SE_{1}  SE_{2}  Pvalue  

(Intercept, γ_{0})  −0.05  −0.05  0.41  0.19  0.20  0.81 
B Cell  −1.36  0.02  0.29  0.22  0.23  <0.0001 
Granulocyte  8.97  −0.04  0.49  1.02  1.00  <0.0001 
Monocyte  0.55  0.06  0.49  0.29  0.30  0.066 
NK  −2.09  0.01  0.55  0.31  0.34  <0.0001 
T Cell (cd4+)  −5.64  0.18  1.93  1.06  1.34  <0.0001 
T Cell (cd8+)  −0.35  −0.17  1.93  0.95  1.19  0.77 
Compared with controls, cases showed significant increases in granulocytes and significant decreases in B cells, NK cells, and CD4+ T cells. Cases also showed marginally significant increases in monocytes. These results are consistent with previous literature, where it has been demonstrated that ovarian cancer patients experience decreases in B and T lymphocytes[29–31], increases in monocytes[29, 30] and (somewhat equivocally) increases in eosinophil granulocytes[30]. Additionally, there were significant systematic decreases in CD4+ T cells with increasing age, with a gradient consistent in direction and somewhat consistent in magnitude with the corresponding effect found in the HNSCC data set. Though most of the CD8+ T cell coefficients for age were not significant, they were all positive, with gradient consistent in direction and somewhat consistent in magnitude with the corresponding effect found in the HNSCC data set. As reported in the Additional file1, no bisulfite conversion coefficient was significant, and all coefficients were of small magnitude (generally less than 1 percentage point per standard deviation).
Down syndrome
The only significant difference between cases and controls was in B cell distribution, with biascorrected estimated decrease of 4.8%, 95% confidence interval (−6.2%, −3.5%). This result is consistent with known immune characteristics of Down Syndrome, including deficiencies in both B and T cells[32, 33]. However, in the comparison between total leukocytes and T cells, all coefficients except B Cell and NK were highly significant, in directions consistent with comparison of a sample of purified T cells to a generic whole blood sample. In fact, an estimate of the cellular composition of the T cell samples can be obtained by a simple linear transformation of Γ estimates (adding intercept terms with the T cell coefficients); this operation produces values that are not significantly distinct from zero for all cell types except CD4+ and CD8+, whose biascorrected estimates were, respectively, 75.9%, 95% confidence interval (67%,85%) and 8.6%, 95% confidence interval (0%,17%), consistent with the known distribution of these T cells. For the analysis of case vs. control within total leukocytes,${R}_{1,0}^{2}$ was estimated at 4.5%, while${R}_{1,1}^{2}$ was estimated at 67.6%. For the analysis of total leukocyte vs. T cell with pooled cases and controls,${R}_{1,0}^{2}$ was estimated at 81.4%, while${R}_{1,1}^{2}$ was estimated at 98.9%. The latter set of coefficients of determination indicate that a substantial portion of variation is explained by composition of leukocytes, which is the expected result for such an analysis.
Obesity in African Americans
Obese subjects had an estimated increase of 12 percentage points in granulocytes, biascorrected 95% confidence interval (3.4%, 20%) and an estimated decrease of 4 percentage points in NK cells, biascorrected 95% confidence interval (7.7%,0.9%). No significant differences were found for other blood cell types. Note that the specific immunological differences estimated by the method are consistent with known immunological perturbations associated with type II diabetes[9, 10]. Complete results are provided in the Additional file1.
Additional analyses
We obtained the following unnormalized biascorrected estimates: 69.0% CD4+, 95% CI (54%,84%), and 32.5% CD8+, 95%CI (19%,46%). This is consistent with known proportions of these specific cell types among T lymphocytes.
Results of simulations
Simulation Results (Precise Mixtures, n_{1} = 200)
Strong Alternative I (θ = 0)  

Truth  Est  SD  SE_{0}  SE_{1}  SE_{2}  pow(0.05)  pow(0.01)  
B Cell  0.0  0.07  1.00  0.92  0.97  0.98  0.057  0.018 
Granulocyte  8.0  8.02  0.73  0.39  0.73  0.73  1.000  1.000 
Monocyte  0.0  0.01  0.48  0.43  0.47  0.47  0.055  0.013 
NK  0.0  −0.09  1.08  1.02  1.02  1.05  0.066  0.015 
T Cell (cd4+)  −4.0  −4.06  0.81  0.80  0.78  0.81  0.999  0.989 
T Cell (cd8+)  −2.0  −1.93  0.83  0.81  0.78  0.81  0.653  0.419 
Strong Alternative II ( θ = 0)  
Truth  Est  SD  SE _{ 0 }  SE _{ 1 }  SE _{ 2 }  pow(0.05)  pow(0.01)  
B Cell  0.0  0.00  0.97  0.92  0.97  0.99  0.048  0.016 
Granulocyte  8.0  8.00  0.71  0.39  0.72  0.72  1.000  1.000 
Monocyte  0.0  0.03  0.48  0.42  0.47  0.47  0.063  0.016 
NK  0.0  0.03  1.04  1.02  1.01  1.05  0.052  0.014 
T Cell (cd4+)  −6.0  −5.83  0.76  0.80  0.77  0.80  1.000  1.000 
T Cell (cd8+)  0.0  −0.22  0.81  0.81  0.80  0.81  0.064  0.014 
Mixed Alternative ( θ = 0.5)  
Truth  Est  SD  SE _{ 0 }  SE _{ 1 }  SE _{ 2 }  pow(0.05)  pow(0.01)  
B Cell  0.0  −0.02  1.02  1.10  0.96  0.98  0.065  0.011 
Granulocyte  4.0  3.99  0.75  0.47  0.73  0.73  1.000  0.995 
Monocyte  0.0  0.02  0.49  0.51  0.47  0.47  0.060  0.015 
NK  0.0  0.04  1.05  1.22  1.01  1.04  0.054  0.009 
T Cell (cd4+)  −2.0  −2.07  0.82  0.96  0.79  0.83  0.695  0.471 
T Cell (cd8+)  −1.0  −0.95  0.82  0.96  0.78  0.82  0.203  0.082 
Mixed Null ( θ = 1)  
Truth  Est  SD  SE _{ 0 }  SE _{ 1 }  SE _{ 2 }  pow(0.05)  pow(0.01)  
B Cell  0.0  0.00  1.04  1.58  0.96  1.02  0.066  0.017 
Granulocyte  0.0  0.03  0.73  0.67  0.74  0.74  0.055  0.014 
Monocyte  0.0  −0.01  0.47  0.73  0.47  0.48  0.054  0.013 
NK  0.0  −0.01  1.12  1.76  1.01  1.09  0.063  0.014 
T Cell (cd4+)  0.0  0.01  0.87  1.38  0.80  0.90  0.054  0.013 
T Cell (cd8+)  0.0  −0.02  0.88  1.39  0.79  0.89  0.057  0.015 
Strong Null ( θ = 0)  
Truth  Est  SD  SE _{ 0 }  SE _{ 1 }  SE _{ 2 }  pow(0.05)  pow(0.01)  
B Cell  0.0  −0.01  0.99  0.90  0.96  0.96  0.068  0.014 
Granulocyte  0.0  0.03  0.72  0.38  0.74  0.73  0.052  0.013 
Monocyte  0.0  −0.01  0.47  0.42  0.47  0.47  0.055  0.013 
NK  0.0  −0.01  1.06  1.00  1.01  1.02  0.059  0.020 
T Cell (cd4+)  0.0  0.00  0.81  0.78  0.80  0.82  0.054  0.013 
T Cell (cd8+)  0.0  −0.01  0.81  0.79  0.79  0.80  0.054  0.015 
Simulation Results (Noisy Mixtures, n_{1} = 200)
Strong Alternative I (θ = 0)  

Truth  Est  SD  SE _{ 0 }  SE _{ 1 }  SE _{ 2 }  pow(0.05)  pow(0.01)  
B Cell  0.0  −0.06  1.39  0.92  1.36  1.34  0.065  0.019 
Granulocyte  8.0  7.87  2.02  0.39  2.00  1.99  0.974  0.897 
Monocyte  0.0  0.05  1.03  0.42  1.04  1.02  0.049  0.012 
NK  0.0  −0.02  1.21  1.02  1.16  1.18  0.061  0.010 
T Cell (cd4+)  −4.0  −4.00  1.23  0.79  1.21  1.22  0.903  0.739 
T Cell (cd8+)  −2.0  −1.97  1.05  0.80  1.02  0.98  0.517  0.298 
Strong Alternative II ( θ = 0)  
Truth  Est  SD  SE _{ 0 }  SE _{ 1 }  SE _{ 2 }  pow(0.05)  pow(0.01)  
B Cell  0.0  −0.08  1.38  0.92  1.36  1.34  0.063  0.017 
Granulocyte  8.0  7.90  2.03  0.39  1.99  1.98  0.973  0.905 
Monocyte  0.0  0.10  1.07  0.42  1.04  1.02  0.054  0.019 
NK  0.0  0.02  1.17  1.02  1.14  1.18  0.053  0.009 
T Cell (cd4+)  −6.0  −5.70  1.19  0.80  1.13  1.16  0.999  0.986 
T Cell (cd8+)  0.0  −0.23  1.08  0.81  1.10  1.04  0.066  0.015 
Mixed Alternative ( θ = 0.5)  
Truth  Est  SD  SE _{ 0 }  SE _{ 1 }  SE _{ 2 }  pow(0.05)  pow(0.01)  
B Cell  0.0  0.05  1.42  1.10  1.34  1.34  0.066  0.016 
Granulocyte  4.0  4.00  2.01  0.47  2.02  2.01  0.500  0.291 
Monocyte  0.0  0.01  1.06  0.51  1.03  1.02  0.072  0.020 
NK  0.0  −0.02  1.24  1.22  1.13  1.16  0.064  0.013 
T Cell (cd4+)  −2.0  −2.11  1.30  0.95  1.26  1.28  0.391  0.191 
T Cell (cd8+)  −1.0  −0.94  1.08  0.96  1.05  1.02  0.163  0.052 
Mixed Null ( θ = 1)  
Truth  Est  SD  SE _{ 0 }  SE _{ 1 }  SE _{ 2 }  pow(0.05)  pow(0.01)  
B Cell  0.0  0.06  1.41  1.59  1.36  1.37  0.062  0.016 
Granulocyte  0.0  0.04  2.08  0.67  2.06  2.05  0.056  0.008 
Monocyte  0.0  −0.02  1.05  0.73  1.03  1.03  0.058  0.020 
NK  0.0  0.01  1.26  1.76  1.14  1.22  0.066  0.011 
T Cell (cd4+)  0.0  −0.01  1.42  1.38  1.31  1.36  0.067  0.016 
T Cell (cd8+)  0.0  0.00  1.19  1.39  1.08  1.10  0.073  0.011 
Strong Null ( θ = 0)  
Truth  Est  SD  SE _{ 0 }  SE _{ 1 }  SE _{ 2 }  pow(0.05)  pow(0.01)  
B Cell  0.0  0.06  1.37  0.91  1.36  1.32  0.065  0.017 
Granulocyte  0.0  0.03  2.07  0.38  2.06  2.05  0.055  0.009 
Monocyte  0.0  −0.02  1.04  0.42  1.03  1.02  0.057  0.021 
NK  0.0  0.01  1.19  1.01  1.14  1.16  0.053  0.018 
T Cell (cd4+)  0.0  −0.04  1.38  0.79  1.31  1.31  0.069  0.015 
T Cell (cd8+)  0.0  0.01  1.11  0.79  1.08  1.03  0.065  0.016 
Discussion
In this paper, we employ the concept of DMRs as markers of immune cell identity using a high density methylation platform, and propose a set of analytical tools for estimating the proportions of immune cells in unfractionated whole blood. The backbone of the approach is the DNA methylation signature of each of the principal immune components of whole blood (B cells, granulocytes, monocytes, NK cells, and T cells subsets). The examples we have provided above serve to illustrate that our proposed methodology produces parameter estimates consistent with the literature, thus validating its utility.
Our proposed method resembles regression calibration, where we consider a methylation signature to be a highdimensional multivariate surrogate for the distribution of white blood cells. In turn, this distribution is of interest for predicting or modeling disease states. As a surrogate, the DNA methylation signature is assumed to be a highly correlated, yet imperfect, measure of leukocyte distribution, and thus fits into the framework of measurement error models, where the use of a noisy surrogate marker to investigate an association with a disease outcome of interest results in biased estimates, unless internal or external validation data can be obtained to “calibrate” the model and correct the bias[12]. However, in this case, the problem is complicated by the extremely high dimension of the surrogate. Measurement error problems are typically formulated as a set of relationships between z, the disease outcome (e.g. case/control status), ω, the gold standard (e.g. leukocyte distribution), and Y, the surrogate (e.g. DNA methylation). Of interest is E(z  ω), which may be difficult to estimate due to the cost or logistical complications involved in obtaining ω in a large number of samples. Typically, it is possible to collect sufficient data for modeling E(z  Y), which provides information about E(z  ω) through the (often imperfect) association E(Y  ω), which is inferred from an external validation sample[12, 34]. Unfortunately, the highdimensional nature of Y renders E(z  Y) difficult to formulate. While multivariate methods of measurement error correction exist, even in a highdimensional context[35], they require an explicit specification of E(zY), requiring a large number of parameters even for a main effects regression model, and many more in order to account for interactions. This becomes unwieldy when each component of Y contributes a small amount of information about z, and both dimensionreduction strategies and constrained regression strategies entail substantial loss of information and may be extremely computationally intensive. Existing measurement error formulations[34, 35] would have required us to specify a logistic regression model for case/control status, conditional on DNA methylation signature, a computationally difficult task that would have extreme vulnerability to model misspecification. On the other hand, our method requires specification of E(Y  z), which is natural and straightforward. Note that in some treatments of regression calibration, E(ω  Y) is used as a surrogate for ω in regression models for z[12]; our treatment essentially assumes a linear form for E(Y  ω) and effectively obtains E(ω  Y) by projecting Y onto the column space of resulting matrix. We note that it is possible using existing methods to qualitatively describe immune response contributions to DNA methylation. This is typically done by conducting a pathway analysis along the lines of one of the methods described in[36], the best option of which is Gene Set Enrichment Analysis (GSEA)[37]. For example, Teschendorff et al. (2009)[22] use GSEA to qualitatively motivate an immunological explanation. However, these methods do not directly quantify the immunological contribution.
An important consideration in the measurement error literature is that of transportability of model parameters[38]. In our setting, an important consideration is whether the methylation profiles obtained from the purified blood cells used to assemble S_{0} would be representative of the white blood cells measured within S_{1}. Because of the biological assumptions inherent in the DMR literature and underlying current understanding of hematopoeisis and lineage commitment, this assumption is reasonable, provided our method is used to detect abnormal mixtures of normal white blood cells. However, methylation abnormalities in the white blood cells themselves constitute a form of noncell mediated alteration (in the sense of the term we have been using), and contribute to bias in our methods, as described briefly above and in detail in the Additional file1.
Note that our formulation respects the study design (DNA methylation assay data collected after sampling from phenotype groups). An alternative strategy outside the measurement error literature but within the larger missingdata literature might have been the use of an ExpectationMaximization (EM) algorithm to integrate over the missing data ω[39]. However, by design, the distribution of ω varied substantially between the data sets S_{0} and S_{1}, severely complicating the approach; notably, an would be the introduction of feedback from S_{1} to S_{0}, contaminating the goldstandard status of S_{0}. An alternative, might be the use of an empirical Bayes procedure, reminiscent of existing mixturemodel approaches[40]. However, difficulty in specifying the distribution of “remainder terms” (denoted as ξ above) render this approach untenable, and in simulations (not presented), attempts to impute ω among S_{1} samples using parameters obtained from S_{0} samples resulted in extremely biased estimates of ω.
The most significant aspect of the current study is our development of a method for inferring changes in the distribution of white blood cell types between different human populations (e.g. cases and controls) using DNA methylation signatures; an approach guided by an external validation set consisting of methylation profiles from purified white blood cell components. DNA methylation in peripheral blood is a potentially powerful new biomarker for clinical and epidemiological investigation. By example, numerous studies have now attempted to distinguish cancer cases from controls using whole peripheral blood assayed via DNA methylation arrays, including ovarian[22], bladder[41], and pancreatic[42] cancers. While these studies have demonstrated good to excellent discrimination of cases from controls, sound evidence for a biological mechanism has been elusive. Presumably, disease associated alterations in blood methylation have several etiological components driven by inherent genetic, environmental and disease specific factors. Given the known developmental associated differences in DNA methylation among specific blood cell types, changes in the distributions of blood cell types alone could account for disease associated DNA methylation. While numerous authors provide a qualitative discussion that includes the possibility of immunerelated DNA methylation differences (e.g.[22]), none to date has specifically quantified the contribution from immune response. On the other hand, the many diverse types of immune cells in blood make this issue highly complex and problematic to tackle using single cell type assays. Therefore, it is crucial to the development of this new avenue of biomarker research to delineate effects due to the immune cell distribution itself from other “non cell type” alterations in DNA methylation. We term the differences among human populations attributed to cell distributions to be “immunologically mediated”. Our solution to partition this component of variation in methylation from other determinants are multivariate analytic tools including regression coefficients and associated inference, as well as coefficients of determination measures. Taken together these provide a means for evaluating whether the observed DNA methylation differences are due to an immunologically mediated response.
In our Additional file1 we provide a detailed analysis of potential sources of bias in our analysis. One obvious biological source of bias is age of the subjects contributing cells for validation. At certain CpG loci, DNA methylation is known to change with age[43], especially in T cells[44]. In the Additional file1 we demonstrate that any agerelated associations with DNA methylation in our top 100 CpGs were too weak to be detected with the current validation sample, and thus unlikely to bias the results of our analyses (notably age coefficients provided for the HNSCC example). However, we remark that with larger sample sizes, adjustments for age can be incorporated with an appropriate additional term in the linear model (1) for Y_{0h}.
Similar methods based on mRNA have been employed[13–15]. The statistical principles described in this article would apply, wholesale, to mRNA expression profiles, but with two cautionary statements. The first is mathematical: mRNA is typically analyzed on a logarithmic scale, yet the assumptions of the proposed methodology involve linearity on an arithmetic scale, since the mixing coefficients are assumed to act linearly on absolute numbers of nucleic acid molecules; thus, the proposed methods would require analysis of untransformed fluorescence intensities, whose skewed distributions would result in numerical instabilities. The second is biological: there is no necessarily linear relationship between cell number and mRNA copies, since proteins may be translated as a consequence of an initial burst of mRNA transcription upon cellular development, after which significant mRNA degradation is possible. In contrast, one would expect the average beta value provided by Illumina beadarray products (and similar quantities) to scale in proportion to the actual fraction of methylated nucleic acids; in addition, an assumption of two DNA molecules per cell seems biologically reasonable. In the Additional file1 we provide an example of an application of our methods using mRNA data.
Going forward there are two issues that require further experimental and analytical refinement. First, although the current studies suggest group level comparisons of blood cell DNA methylation can reveal important immune alterations, it will be important to provide methods for individual level immune cell profiling, since clinical and detailed analytical epidemiologic applications that examine individual risk factor information will be the subject of future studies. As we have demonstrated above, individual immune profiles are theoretically achievable but will require extensive validation, with a wide array of mixture combinations, before gaining widespread acceptance. Secondly, there is intense interest in minor immune cell fractions and their role in disease, though the signal strength of cell types comprising < 5% of the total white cell compartment may be difficult to quantitate. Examples of such cell types include the regulatory Tcell or NK cell fractions, which are implicated in autoimmune and malignant diseases. Optimization of platforms for technical sensitivity to minor subtypes combined with statistical optimization of signature recognition are needed to enhance the approach for testing highly targeted immune hypotheses.
Conclusions
The method we present here has potentially far reaching implications for rapid, simple and complete assessment of the composition of human white blood cell populations, i.e. the immune profile. Currently, assessment of the cellular composition of peripheral blood cannot be accomplished without the use of freshly drawn venous blood that is immediately prepared in a specially equipped laboratory. A complete assessment of the entire immune profile requires extensive flow cytometric measurements based on protein epitopes on leukocyte membranes that distinguishes subtypes of immune cells that are either too rare or too similar in appearance to be distinguished using simple microscopic approaches. In particular, flow cytometry is limited by the following: (i) cells must be separated, requiring large volumes of fresh cells; (ii) detection can be accomplished only by the fluorescent antibody tags available, which require expensive technology to read; (iii) the outer cell membrane must be intact, mandating limited utility in many instances (particularly in research). In contrast, our method requires the application of these laborintensive or expensive steps only in the construction of the validation set S_{0}, which need only be developed once. Once S_{0} is available, subsequent interrogation is based on the chemically stable CpG methylation of DNA; thus our method obviates the need for fresh blood and the preservation of labile protein epitopes. It is also able to simultaneously assess all of the individual components of the peripheral blood using a highly multiplexed molecular platform and is thus very straightforward logistically. Furthermore, the statistical methodology presented here can be implemented easily with the instrumental output of the methylation arrays, which simplifies the interpretation of the immune profile data from the operators point of view. This method can be immediately deployed in a research framework to cost effectively assess human immune profiles (in fresh or archival samples), exploring their potential as biomarkers, and addressing key questions regarding disease pathogenesis. Furthermore, our approach is readily suited for rapid translation to a broad base of clinical applications such as disease monitoring, diagnosis, prognosis, and response to therapy.
Our approach makes research on biobanked specimens possible, now making a vast array of prospective studies that could not otherwise be done, possible. Software and sample data are provided in Additional file2.
Abbreviations
 CTL:

Cytotoxic Tcells
 CpG:

Cytosinephosphateguanine
 DMR:

Differentially methylated region
 HNSCC:

Head and neck squamous cell carcinoma
 NK:

Matural killer.
Declarations
Acknowledgements
This work was funded by NIH grants CA100679, CA126939, CA121147, and CA078609.
Authors’ Affiliations
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