Contents

1 Introduction

The EpiDISH package provides tools to infer the fractions of a priori known cell subtypes present in a DNA methylation (DNAm) sample representing a mixture of such cell-types. Inference proceeds via one of 3 methods (Robust Partial Correlations-RPC(A. E. Teschendorff et al. 2017), Cibersort-CBS(Newman et al. 2015), Constrained Projection-CP(Houseman et al. 2012)), as determined by the user. Besides, we also provide a function - CellDMC which allows the identification of differentially methylated cell-types in Epigenome-Wide Association Studies(EWAS)(Zheng, Breeze, et al. 2018). For now, the package contains 7 DNAm reference matrices, three of which are designed for adult whole blood (A. E. Teschendorff et al. 2017) and (Luo et al. 2023), and one which is designed for blood tissue of any age, including cord-blood and blood from infants, children, adolescents and adults.

  1. centDHSbloodDMC.m: This DNAm reference matrix for blood will estimate fractions for 7 immune cell types (B-cells, NK-cells, CD4T and CD8T-cells, Monocytes, Neutrophils and Eosinophils).
  2. cent12CT.m: This DNAm reference matrix for blood and EPIC-arrays will estimate fractions for 12 immune-cell types (naive and mature B-cells, naive and mature CD4T-cells, naive and mature B-cells, T-regulatory cells, NK-cells, Neutrophils, Monocytes, Eosinophils, Basophils).
  3. cent12CT450k.m: This DNAm reference matrix for blood and Illumina 450k-arrays will estimate fractions for 12 immune-cell types (naive and mature B-cells, naive and mature CD4T-cells, naive and mature B-cells, T-regulatory cells, NK-cells, Neutrophils, Monocytes, Eosinophils, Basophils).
  4. centUniLIFE.m: This DNAm reference matrix for blood-tissue of any age will estimate fractions for 19 immune-cell types including 7 youthful cord-blood subtypes (B-cells, NK-cells, Granulocytes, Monocytes, nRBCs, CD4T and CD8T-cells) and 12 adult immune cell types (naive and mature B-cells, naive and mature CD4T-cells, naive and mature B-cells, T-regulatory cells, NK-cells, Neutrophils, Monocytes, Eosinophils, Basophils).

The other 3 DNAm reference matrices are designed for solid tissue-types (Zheng, Webster, et al. 2018):

  1. centEpiFibIC.m: This DNAm reference matrix is designed for a generic solid tissue that is dominated by an epithelial, stromal and immune-cell component. It will estimate fractions for 3 broad cell-types: a generic epithelial, fibroblast and immune-cell type.
  2. centBloodSub.m: This DNAm reference matrix is designed for a solid tissue-type and will estimate immune cell infiltration for 7 immune cell subtypes. This DNAm reference matrix is meant to be applied after centEpiFibIC.m to yield proportions for 7 immune cell subtypes alongside the total epithelial and total fibroblast fractions.
  3. centEpiFibFatIC.m: This DNAm reference matrix is a more specialised version for breast tissue and will estimate total epithelial, fibroblast, immune-cell and fat fractions.

2 How to estimate cell-type fractions in blood

We show an example of using our package to estimate 7 immune cell-type fractions in adult whole blood. We use a subset beta value matrix of GSE42861 (detailed description in manual page of LiuDataSub.m). First, we read in the required objects:

library(EpiDISH)
data(centDHSbloodDMC.m)
data(LiuDataSub.m)
BloodFrac.m <- epidish(beta.m = LiuDataSub.m, ref.m = centDHSbloodDMC.m, method = "RPC")$estF

We can easily check the inferred fractions with boxplots. From the boxplots, we observe that just as we expected, the major cell-type in whole blood is neutrophil.

boxplot(BloodFrac.m)

If we wanted to infer fractions at a higher resolution of 12 immune cell subtypes, we would replace centDHSbloodDMC.m in the above with cent12CT450k.m because this is a 450k DNAm dataset. For an EPIC whole blood dataset, you would use cent12CT.m.

3 How to estimate generic cell-type fractions in a solid tissue

To illustrate how this works, we first read in a dummy beta value matrix DummyBeta.m, which contains 2000 CpGs and 10 samples, representing a solid tissue:

data(centEpiFibIC.m)
data(DummyBeta.m)

Notice that centEpiFibIC.m has 3 columns, with names of the columns as EPi, Fib and IC. We go ahead and use epidish function with RPC mode to infer the cell-type fractions.

out.l <- epidish(beta.m = DummyBeta.m, ref.m = centEpiFibIC.m, method = "RPC")

Then, we check the output list. estF is the matrix of estimated cell-type fractions. ref is the reference centroid matrix used, and dataREF is the subset of the input data matrix over the probes defined in the reference matrix.

out.l$estF
##            Epi        Fib           IC
## S1  0.08836819 0.06109607 0.8505357378
## S2  0.07652115 0.57326994 0.3502089007
## S3  0.15417391 0.75663136 0.0891947251
## S4  0.77082647 0.04171941 0.1874541181
## S5  0.03960599 0.31921224 0.6411817742
## S6  0.12751711 0.79642919 0.0760537000
## S7  0.18144315 0.72889883 0.0896580171
## S8  0.20220823 0.40929344 0.3884983293
## S9  0.19398079 0.80540932 0.0006098973
## S10 0.27976647 0.23671333 0.4835201992
dim(out.l$ref)
## [1] 599   3
dim(out.l$dataREF)
## [1] 599  10

Note: As part of the quality control step in DNAm data preprocessing, we might have to remove bad probes; consequently, not all probes in the reference matrix may be available in a given dataset. By checking ref and dataREF, we can extract the probes actually used for estimating cell-type fractions. As shown by us (Zheng, Webster, et al. 2018), if the proportion of missing reference matrix probes is more than a third, then estimated fractions may be unreliable.

4 How to estimate immune cell-type fractions in a solid tissue using HEpiDISH

HEpiDISH is an iterative hierarchical procedure of EpiDISH designed for solid tissues with significant immune-cell infiltration. HEpiDISH uses two distinct DNAm references, a primary reference for the estimation of total epithelial, fibroblast and immune-cell fractions, and a separate secondary non-overlapping DNAm reference for the estimation of underlying immune cell subtype fractions. Fig1. HEpiDISH workflow In this example, the third cell-type in the primary DNAm reference matrix is the total immune cell fraction. We would like to know the fractions of 7 immune cell subtypes, in adddition to the epithelial and fibroblast fractions. So we use a secondary reference, which contains 7 immnue cell subtypes, and let hepidish function know that the third column of primary reference should correspond to the secondary DNAm reference matrix. (We only include 3 cell-types of the centBloodSub.m reference because we mixed those three cell-types to generate the dummy beta value matrix.)

data(centBloodSub.m)
frac.m <- hepidish(beta.m = DummyBeta.m, ref1.m = centEpiFibIC.m, ref2.m = centBloodSub.m[,c(1, 2, 5)], h.CT.idx = 3, method = 'RPC')
frac.m
##            Epi        Fib            B           NK       Mono
## S1  0.08836819 0.06109607 0.6446835622 0.0945693668 0.11128281
## S2  0.07652115 0.57326994 0.0502766152 0.2999322854 0.00000000
## S3  0.15417391 0.75663136 0.0381194625 0.0134501813 0.03762508
## S4  0.77082647 0.04171941 0.1434958145 0.0211681974 0.02279011
## S5  0.03960599 0.31921224 0.0167748647 0.1912747358 0.43313217
## S6  0.12751711 0.79642919 0.0286647024 0.0252778983 0.02211110
## S7  0.18144315 0.72889883 0.0515861314 0.0228453164 0.01522657
## S8  0.20220823 0.40929344 0.1908434542 0.1772700742 0.02038480
## S9  0.19398079 0.80540932 0.0003521377 0.0002577596 0.00000000
## S10 0.27976647 0.23671333 0.2546961632 0.1008399798 0.12798406

5 More info about different methods for cell-type fractions estimation

We compared CP and RPC in (A. E. Teschendorff et al. 2017). And we also published a review article(A. E. Teschendorff and Zheng 2017) which discusses most of algorithms for tackling cell heterogeneity in Epigenome-Wide Association Studies(EWAS). Refer to references section for more details.

6 How to identify differentially methylated cell-types in EWAS

After estimating cell-type fractions, we can then identify differentially methylated cell-types and their directions of change using CellDMC (Zheng, Breeze, et al. 2018)function. The workflow of CellDMC is shown below. Fig2. CellDMC workflow

We use a binary phenotype vector here, with half of them representing controls and other half representing cases.

pheno.v <- rep(c(0, 1), each = 5)
celldmc.o <- CellDMC(DummyBeta.m, pheno.v, frac.m)

The DMCTs prediction is given(pls note this is faked data. The sample size is too small to find DMCTs.):

head(celldmc.o$dmct)
##            DMC Epi Fib B NK Mono
## cg17506061   0   0   0 0  0    0
## cg09300980   0   0   0 0  0    0
## cg18886245   0   0   0 0  0    0
## cg17470327   0   0   0 0  0    0
## cg26082174   0   0   0 0  0    0
## cg14737131   0   0   0 0  0    0

The estimated coefficients for each cell-type are given in the celldmc.o$coe. Pls refer to help page of CellDMC for more info.

7 Sessioninfo

## R version 4.5.0 (2025-04-11 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
##   LAPACK version 3.12.1
## 
## locale:
## [1] LC_COLLATE=C                          
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] EpiDISH_2.25.0   BiocStyle_2.37.0
## 
## loaded via a namespace (and not attached):
##  [1] vctrs_0.6.5         cli_3.6.5           knitr_1.50         
##  [4] magick_2.8.6        rlang_1.1.6         xfun_0.52          
##  [7] stringi_1.8.7       jsonlite_2.0.0      glue_1.8.0         
## [10] e1071_1.7-16        htmltools_0.5.8.1   tinytex_0.57       
## [13] sass_0.4.10         rmarkdown_2.29      quadprog_1.5-8     
## [16] grid_4.5.0          evaluate_1.0.3      jquerylib_0.1.4    
## [19] MASS_7.3-65         fastmap_1.2.0       yaml_2.3.10        
## [22] locfdr_1.1-8        lifecycle_1.0.4     bookdown_0.43      
## [25] stringr_1.5.1       BiocManager_1.30.25 compiler_4.5.0     
## [28] Rcpp_1.0.14         lattice_0.22-7      digest_0.6.37      
## [31] R6_2.6.1            class_7.3-23        parallel_4.5.0     
## [34] splines_4.5.0       magrittr_2.0.3      bslib_0.9.0        
## [37] Matrix_1.7-3        proxy_0.4-27        tools_4.5.0        
## [40] matrixStats_1.5.0   cachem_1.1.0

References

Houseman, Eugene Andres, William P Accomando, Devin C Koestler, Brock C Christensen, Carmen J Marsit, Heather H Nelson, John K Wiencke, and Karl T Kelsey. 2012. “DNA methylation arrays as surrogate measures of cell mixture distribution.” BMC Bioinformatics 13 (1): 86.

Luo, Q, VB Dwaraka, Q Chen, H Tong, T Zhu, K Seale, JM Raffaele, et al. 2023. “A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes.” Genome Med 15 (1): 59.

Newman, Aaron M, Chih Long Liu, Michael R Green, Andrew J Gentles, Weiguo Feng, Yue Xu, Chuong D Hoang, Maximilian Diehn, and Ash A Alizadeh. 2015. “Robust enumeration of cell subsets from tissue expression profiles.” Nature Methods 12 (5): 453–57.

Teschendorff, Andrew E, Charles E Breeze, Shijie C Zheng, and Stephan Beck. 2017. “A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies.” BMC Bioinformatics 18 (1): 105.

Teschendorff, Andrew E, and Shijie C Zheng. 2017. “Cell-type deconvolution in epigenome-wide association studies: a review and recommendations.” Epigenomics 9 (5): 757–68.

Zheng, Shijie C, Charles E Breeze, Stephan Beck, and Andrew E Teschendorff. 2018. “Identification of differentially methylated cell-types in Epigenome-Wide Association Studies.” Nature Methods 15 (12): 1059–66.

Zheng, Shijie C, Amy P Webster, Danyue Dong, Andy Feber, David G Graham, Roisin Sullivan, Sarah Jevons, et al. 2018. “A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix.” Epigenomics 10 (7): 925–40.