Bio Methylation Cell Type Deconvolution

Version Compatibility

Reference examples tested with: EpiDISH 2.18+, minfi 1.48+, FlowSorted.Blood.EPIC 2.0+, FlowSorted.CordBloodCombined.450k 1.20+.

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

The REFERENCE package is the version that matters most. A reference package is platform-, tissue-, and age-specific: FlowSorted.Blood.EPIC ships IDOLOptimizedCpGs (EPIC) and IDOLOptimizedCpGs450klegacy (450K) as distinct libraries, the 12-cell library lives in a separate FlowSorted.BloodExtended.EPIC package, and cord blood needs FlowSorted.CordBloodCombined.450k (it carries nucleated red blood cells). estimateCellCounts2 returns a Neu (neutrophil) column where the older minfi::estimateCellCounts returns Gran - the label changes the downstream column names. Record the reference package and version alongside the array build.

Cell-Type Deconvolution

"How much of my methylation signal is just cell composition?" -> Project the bulk beta matrix onto a purified-cell reference to estimate per-sample fractions, then carry those fractions forward as covariates - because a bulk methylome is a composition-weighted average and a composition difference is a methylation difference.

• R: epidish(beta.m, ref.m = centDHSbloodDMC.m, method = 'RPC')$estF

Scope: estimate cell-type fractions from a clean bulk beta matrix and use them downstream. Clean beta read-in and EPICv2 replicate-probe collapse -> array-preprocessing. The confounder-vs-mediator decision and the EWAS regression itself -> ewas-design. Adjusting a DNAm clock for cell counts (IEAA) -> epigenetic-clocks. Single-cell/sorted atlases for reference building and validation ground truth -> single-cell/preprocessing. Predictive-model training/leakage -> machine-learning/biomarker-discovery.

The Single Most Important Modern Insight -- A Cell-Fraction Estimate Is a Projection, Not a Measurement

A reference-based fraction is not a measurement of a sample's composition; it is a projection of that sample onto cell types someone else purified, on someone else's platform, in someone else's tissue. Three corollaries each common misuse violates:

1. A composition difference IS a methylation difference. Bulk DNAm is the fraction-weighted average of its constituent cell-type methylomes, and most CpG variance is between cell types, not between conditions. If cases and controls differ in composition - which they almost always do (age, sex, infection, smoking shift the neutrophil-to-lymphocyte ratio) - the EWAS reports cell-count differences as if they were disease methylation. This is the #1 EWAS confounder (Jaffe & Irizarry 2014 Genome Biol 15:R31).
2. The reference defines the answer. A cell type present in the sample but absent from the reference is silently redistributed onto the nearest reference types - no error, the fractions still sum to ~1. Cord blood without nRBC, a solid tissue against a blood reference, EPICv2 data against a 450K library: all return confident, wrong proportions.
3. Fractions are compositional. They live on a simplex (sum ~1), so they are not independent: one going up forces others down. Naively co-regressing or correlating all K fractions manufactures spurious negative associations.

Organize the analysis around matching the reference and handling compositionality, not around picking an algorithm. Deconvolution turns an uncontrollable confounder into a measurable covariate - but only as accurately as the reference matches the sample.

Reference-Based: The Houseman Constrained-Projection Foundation

Houseman 2012 (BMC Bioinformatics 13:86) is the origin. From a matrix of FACS/MACS-purified cell-type mean methylation at discriminating CpGs (L-DMRs), solve for each sample a constrained quadratic program: the non-negative fraction vector w (w_i >= 0, sum ~1) minimizing the squared distance between observed beta and reference x w over the L-DMR CpGs. This "CP" (constrained projection) is what every later method is measured against. The L-DMR selection is itself a tuning choice that the IDOL work (Koestler 2016 BMC Bioinformatics 17:120) optimized into a fixed, benchmarked library.

Tool Taxonomy

Decision Tree by Scenario

Estimate Blood Fractions with EpiDISH (RPC)

Goal: Get per-sample fractions of the major immune cell types from a clean beta matrix to use as EWAS covariates.

Approach: Pass the beta matrix and a tissue-matched reference centroid to epidish with method='RPC' (the robust option), then read the sample-by-cell-type matrix from $estF.

library(EpiDISH)
data(centDHSbloodDMC.m)    # 7 immune cell types, adult whole blood

out <- epidish(beta.m = beta_matrix, ref.m = centDHSbloodDMC.m, method = 'RPC')
fractions <- out$estF       # samples x cell types; rows sum to ~1

Estimate Blood Fractions from an RGChannelSet (minfi + IDOL)

Goal: Estimate the modern 6-cell IDOL blood composition straight from raw IDAT-derived data.

Approach: Run estimateCellCounts2 on the RGChannelSet with the IDOL probe selection and the platform-matched reference; for 450K data switch the reference library so the same cell types are estimated cross-platform.

library(FlowSorted.Blood.EPIC)

counts <- estimateCellCounts2(
  rgSet,
  compositeCellType = 'Blood',
  processMethod = 'preprocessNoob',
  probeSelect = 'IDOL',
  cellTypes = c('CD8T', 'CD4T', 'NK', 'Bcell', 'Mono', 'Neu'),   # Neu, not Gran
  referencePlatform = 'IlluminaHumanMethylationEPIC'
)$counts

Solid Tissue: Hierarchical Deconvolution

Goal: Deconvolve a solid tissue (epithelial + fibroblast + infiltrating immune) rather than forcing a blood reference onto it.

Approach: Use hepidish to first split Epithelial/Fibroblast/total-Immune, then deconvolve the immune fraction into subtypes and multiply through. For tissues with no sorted reference at all, EpiSCORE builds an imputed DNAm reference from a single-cell RNA atlas.

library(EpiDISH)
data(centEpiFibIC.m)       # Epithelial / Fibroblast / Immune-Cell
data(centBloodSub.m)       # immune subtypes for the second level

frac <- hepidish(beta.m = beta_matrix, ref1.m = centEpiFibIC.m,
                 ref2.m = centBloodSub.m, h.CT.idx = 3, method = 'RPC')
# h.CT.idx = 3 = the Immune column in ref1 to expand with ref2

Reference-Free Correction

Goal: Capture composition structure when no matched reference exists, accepting unlabeled components.

Approach: ReFACTor selects the most composition-informative CpGs and runs sparse-PCA; use the top components as EWAS covariates. RefFreeEWAS decomposes the matrix into a latent cell-mixture term. Both correct without naming the cell types, so check that genuine top hits survive (they can absorb real signal).

library(TCA)
ref <- refactor(beta_matrix, k = 6)   # k = expected number of cell types
covariates <- ref$scores              # top sparse-PC components as EWAS covariates

Using the Fractions: Covariate vs Cell-Type-Resolved

There are two distinct moves once fractions exist, and they answer different questions.

As covariates (the standard EWAS defense). Include the fractions in the per-CpG design matrix so composition is regressed out. Because fractions are compositional (sum ~1), do NOT enter all K - drop one reference cell type (or use a compositional transform) to avoid perfect collinearity. The confounder-vs-mediator decision (regress out, or treat composition as the mechanism) belongs to ewas-design; execution belongs to differential-cpg-testing.

Cell-type-resolved EWAS (which cell type drives the signal). Instead of regressing composition away, model a phenotype x cell-fraction INTERACTION per CpG to ask which cell type carries the differential methylation and in which direction. CellDMC (Zheng 2018 Nat Methods 15:1059) is the simplest member; a family generalizes it:

library(EpiDISH)
res <- CellDMC(beta.m = beta_matrix, pheno.v = phenotype, frac.m = fractions)
# res$dmct: per-CpG, which cell type is differentially methylated (-1/0/1)

A cell-type-resolved call is an ill-posed inverse problem regularized by an assumed reference: rare cell types (2-5% of the mixture) are badly underpowered, fraction collinearity destabilizes the interactions, and deconvolution error propagates straight into the attribution (HIRE's argument for estimating composition jointly). Validation is hard without sorted/single-cell ground truth - method papers lean on simulations and reconstructed mixtures, which are circular. Treat an in-silico cell-type-specific hit as a HYPOTHESIS about what to sort next, not a finding; confirm load-bearing attributions in sorted or single-cell DNAm from independent samples (Walker 2025 Brief Bioinform 26:bbaf427).

The IEAA Link to Clocks

Intrinsic epigenetic age acceleration (IEAA) is DNAm age residualized on chronological age AND estimated blood cell counts - so deconvolution is the prerequisite step: estimate fractions here, then hand them to epigenetic-clocks as the cell-count covariates that distinguish cell-intrinsic aging from a composition shift. Do not teach the clock here; compute the fractions and route the IEAA adjustment to epigenetic-clocks.

Per-Method Failure Modes

Missing cell type silently redistributed

Trigger: a sample contains a cell type absent from the reference (cord-blood nRBC, a rare infiltrate, a granulocyte subtype collapsed to Gran). Mechanism: the constrained projection has no column for it, so its signal lands on the nearest present types. Symptom: plausible-looking fractions that sum to ~1 with no warning. Fix: match the reference to tissue+age (FlowSorted.CordBloodCombined.450k for newborns; hepidish/EpiSCORE for solid tissue).

Platform-mismatched reference library

Trigger: 450K data with the EPIC IDOL library, or EPICv2 with either. Mechanism: reference CpGs are partly absent on the other platform, shrinking the L-DMR set used for the projection. Symptom: biased fractions, no error. Fix: IDOLOptimizedCpGs450klegacy / cent*450k.m for 450K; collapse EPICv2 replicate probes first (-> array-preprocessing).

Collinear cell-fraction covariates

Trigger: entering all K fractions (sum ~1) into a design matrix. Mechanism: the simplex constraint makes the K-th fraction a linear function of the others. Symptom: rank-deficient design, dropped coefficient, or spurious negative fraction-fraction correlations. Fix: drop one reference cell type or use a compositional (CLR/ILR) transform.

Reference-free over-correction

Trigger: including too many ReFACTor/RefFreeEWAS components, or using them when a reference exists. Mechanism: unlabeled latent components can absorb true biological signal alongside composition. Symptom: top EWAS hits vanish; false negatives. Fix: prefer reference-based when a reference exists; use reference-free as a fallback/sensitivity check and confirm hits survive.

Cell-type attribution from rare cells

Trigger: reading a CellDMC/TCA call for a 2-5% cell type. Mechanism: a rare cell contributes a fraction-attenuated slice of bulk variance, so its interaction estimate is dominated by deconvolution noise. Symptom: confident-looking csDM in basophils/eosinophils; nulls misread as "no effect." Fix: report each cell type's mean fraction; distrust specific calls for low-abundance types; never infer absence of effect from an underpowered null.

Quantitative Thresholds

Common Errors

References

• Houseman EA, Accomando WP, Koestler DC, et al. 2012. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13:86.
• Jaffe AE, Irizarry RA. 2014. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol 15:R31.
• Koestler DC, Jones MJ, Usset J, et al. 2016. Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL). BMC Bioinformatics 17:120.
• Salas LA, Koestler DC, Butler RA, et al. 2018. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol 19:64.
• Salas LA, Zhang Z, Koestler DC, et al. 2022. Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling. Nat Commun 13:761.
• Teschendorff AE, Breeze CE, Zheng SC, Beck S. 2017. A comparison of reference-based algorithms for correcting cell-type heterogeneity in epigenome-wide association studies. BMC Bioinformatics 18:105.
• Teschendorff AE, Zhu T, Breeze CE, Beck S. 2020. EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data. Genome Biol 21:221.
• Houseman EA, Molitor J, Marsit CJ. 2014. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 30:1431-1439.
• Rahmani E, Zaitlen N, Baran Y, et al. 2016. Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies. Nat Methods 13:443-445.
• Zheng SC, Breeze CE, Beck S, Teschendorff AE. 2018. Identification of differentially methylated cell types in epigenome-wide association studies. Nat Methods 15:1059-1066.
• Rahmani E, Schweiger R, Rhead B, et al. 2019. Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology. Nat Commun 10:3417.
• Li Z, Wu H. 2019. TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis. Genome Biol 20:190.
• Takeuchi F, Kato N. 2021. Nonlinear ridge regression improves cell-type-specific differential expression analysis. BMC Bioinformatics 22:141.
• Luo X, Yang C, Wei Y. 2019. Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies. Nat Commun 10:3113.
• Walker EM, Dempster EL, Franklin A, et al. 2025. Guidance for the design and analysis of cell-type-specific DNA methylation epidemiology studies. Brief Bioinform 26:bbaf427.

Related Skills

• array-preprocessing - Provides the clean beta matrix deconvolution consumes
• ewas-design - Cell-fraction covariate strategy (confounder vs mediator)
• epigenetic-clocks - IEAA: adjust the clock for estimated cell composition
• differential-cpg-testing - Uses cell fractions as design-matrix covariates
• single-cell/preprocessing - scRNA atlases for reference building (EpiSCORE) and ground truth
• machine-learning/biomarker-discovery - Predictive-model boundary
• workflows/methylation-pipeline - End-to-end pipeline