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 6 DNAm reference matrices, three of which are designed for whole blood (A. E. Teschendorff et al. 2017) and (Luo et al. 2023):

  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).

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 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 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] EpiDISH_2.21.1   BiocStyle_2.33.1
## 
## loaded via a namespace (and not attached):
##  [1] Matrix_1.7-0        jsonlite_1.8.8      compiler_4.4.0     
##  [4] BiocManager_1.30.23 highr_0.11          Rcpp_1.0.12        
##  [7] tinytex_0.51        stringr_1.5.1       locfdr_1.1-8       
## [10] magick_2.8.3        parallel_4.4.0      jquerylib_0.1.4    
## [13] splines_4.4.0       yaml_2.3.8          fastmap_1.2.0      
## [16] lattice_0.22-6      R6_2.5.1            knitr_1.47         
## [19] MASS_7.3-61         bookdown_0.39       bslib_0.7.0        
## [22] rlang_1.1.4         cachem_1.1.0        stringi_1.8.4      
## [25] xfun_0.45           quadprog_1.5-8      sass_0.4.9         
## [28] cli_3.6.2           magrittr_2.0.3      class_7.3-22       
## [31] digest_0.6.35       grid_4.4.0          lifecycle_1.0.4    
## [34] vctrs_0.6.5         proxy_0.4-27        evaluate_0.24.0    
## [37] glue_1.7.0          e1071_1.7-14        rmarkdown_2.27     
## [40] matrixStats_1.3.0   tools_4.4.0         htmltools_0.5.8.1

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.