The methylCC user’s guide
29 October 2019
Abstract
Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups.Package
methylCC 1.0.0
1 Introduction
There are several approaches available to adjust for differents in the relative proportion of cell types in whole blood measured from DNA methylation (DNAm). For example, reference-based approaches require the use of reference data sets made up of purified cell types to identify cell type-specific DNAm signatures. These cell type-specific DNAm signatures are used to estimate the relative proportions of cell types directly, but these reference data sets are laborious and expensive to collect. Furthermore, these reference data sets will need to be continuously collected over time as new platform technologies emerge measuring DNAm because the observed methylation levels for the same CpGs in the same sample vary depending the platform technology.
In contrast, there are reference-free approaches, which are based on methods related to surrogate variable analysis or linear mixed models. These approaches do not provide estimates of the relative proportions of cell types, but rather these methods just remove the variability induced from the differences in relative cell type proportions in whole blood samples.
Here, we present a statistical model that estimates the cell composition of whole blood samples measured from DNAm. The method can be applied to microarray or sequencing data (for example whole-genome bisulfite sequencing data, WGBS, reduced representation bisulfite sequencing data, RRBS). Our method is based on the idea of identifying informative genomic regions that are clearly methylated or unmethylated for each cell type, which permits estimation in multiple platform technologies as cell types preserve their methylation state in regions independent of platform despite observed measurements being platform dependent.
2 Getting Started
Load the methylCC R package and other packages that we’ll need
later on.
library(FlowSorted.Blood.450k)
library(methylCC)
library(minfi)
library(tidyr)
library(dplyr)
library(ggplot2)3 Data
3.1 Whole Blood Illumina 450k Microarray Data Example
# Phenotypic information about samples
head(pData(FlowSorted.Blood.450k))## DataFrame with 6 rows and 8 columns
## Sample_Name Slide Array Basename
## <character> <numeric> <character> <character>
## WB_105 WB_105 5684819001 R01C01 idat/5684819001_R01C01
## WB_218 WB_218 5684819001 R02C01 idat/5684819001_R02C01
## WB_261 WB_261 5684819001 R03C01 idat/5684819001_R03C01
## PBMC_105 PBMC_105 5684819001 R04C01 idat/5684819001_R04C01
## PBMC_218 PBMC_218 5684819001 R05C01 idat/5684819001_R05C01
## PBMC_261 PBMC_261 5684819001 R06C01 idat/5684819001_R06C01
## SampleID CellTypeLong CellType Sex
## <character> <character> <character> <character>
## WB_105 105 Whole blood WBC M
## WB_218 218 Whole blood WBC M
## WB_261 261 Whole blood WBC M
## PBMC_105 105 PBMC PBMC M
## PBMC_218 218 PBMC PBMC M
## PBMC_261 261 PBMC PBMC M# RGChannelSet
rgset <- FlowSorted.Blood.450k[,
pData(FlowSorted.Blood.450k)$CellTypeLong %in% "Whole blood"]4 Using the estimatecc() function
4.1 Input for estimatecc()
The estimatecc() function must have
one object as input:
- an
objectsuch as anRGChannelSetfrom the R packageminfior aBSseqobject from the R packagebsseq. This object should contain observed DNAm levels at CpGs (rows) in a set of \(N\) whole blood samples (columns).
4.2 Running estimatecc()
In this example, we are interested in estimating the cell
composition of the whole blood samples listed in the
FlowSorted.Blood.450k R/Bioconductor package.
To run the methylcC::estimatecc() function,
just provide the RGChannelSet. This will
create an estimatecc object. We
will call the object est.
set.seed(12345)
est <- estimatecc(object = rgset)
est## estimatecc: Estimate Cell Composition of Whole Blood
## Samples using DNA methylation
## Input object class: RGChannelSet
## Reference cell types: Gran CD4T CD8T Bcell Mono NK
## Number of Whole Blood Samples: 6
## Name of Whole Blood Samples: WB_105 WB_218 WB_261 WB_043 WB_160 WB_149To see the cell composition estimates, use the
cell_counts() function.
cell_counts(est)## Gran CD4T CD8T Bcell Mono NK
## WB_105 0.4242292 0.16915420 0.09506568 0.04187765 0.08357502 0.18609822
## WB_218 0.4906710 0.15471447 0.00000000 0.04979116 0.14346117 0.16136217
## WB_261 0.5476117 0.11895815 0.14007846 0.01725995 0.08869797 0.08739378
## WB_043 0.5038143 0.12420228 0.08031593 0.06515287 0.07218653 0.15432807
## WB_160 0.6803254 0.07139726 0.04965732 0.00000000 0.09526148 0.10335854
## WB_149 0.5375962 0.14902349 0.10814235 0.03227085 0.06111685 0.111850254.3 Compare to minfi::estimateCellCounts()
We can also use the estimateCellCounts() from R/Bioconductor package
minfi
to estimate the cell composition for each of the whole blood samples.
sampleNames(rgset) <- paste0("Sample", 1:6)
est_minfi <- minfi::estimateCellCounts(rgset)
est_minfi## CD8T CD4T NK Bcell Mono Gran
## Sample1 0.13967126 0.1581874 0.137528672 0.07040633 0.06383445 0.4835306
## Sample2 0.05797617 0.1751543 0.072686689 0.09859270 0.12429750 0.5228217
## Sample3 0.12091718 0.1531062 0.029632651 0.05447982 0.06775822 0.6064806
## Sample4 0.10438514 0.1709784 0.024322195 0.11447040 0.05233508 0.5700027
## Sample5 0.03775465 0.1465998 0.003996696 0.04767462 0.07452444 0.7069746
## Sample6 0.06568804 0.1873355 0.054344189 0.07039282 0.05196750 0.5932074Then, we can compare the estimates to methylCC::estimatecc().
df_minfi = gather(cbind("samples" = rownames(cell_counts(est)),
as.data.frame(est_minfi)),
celltype, est, -samples)
df_methylCC = gather(cbind("samples" = rownames(cell_counts(est)),
cell_counts(est)),
celltype, est, -samples)
dfcombined <- full_join(df_minfi, df_methylCC,
by = c("samples", "celltype"))
ggplot(dfcombined, aes(x=est.x, y = est.y, color = celltype)) +
geom_point() + xlim(0,1) + ylim(0,1) +
geom_abline(intercept = 0, slope = 1) +
xlab("Using minfi::estimateCellCounts()") +
ylab("Using methylCC::estimatecc()") +
labs(title = "Comparing cell composition estimates")
We see the estimates closely match for the six cell types.
5 SessionInfo
sessionInfo()## R version 3.6.1 (2019-07-05)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.10-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.10-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
## [2] IlluminaHumanMethylation450kmanifest_0.4.0
## [3] ggplot2_3.2.1
## [4] dplyr_0.8.3
## [5] tidyr_1.0.0
## [6] methylCC_1.0.0
## [7] FlowSorted.Blood.450k_1.23.0
## [8] minfi_1.32.0
## [9] bumphunter_1.28.0
## [10] locfit_1.5-9.1
## [11] iterators_1.0.12
## [12] foreach_1.4.7
## [13] Biostrings_2.54.0
## [14] XVector_0.26.0
## [15] SummarizedExperiment_1.16.0
## [16] DelayedArray_0.12.0
## [17] BiocParallel_1.20.0
## [18] matrixStats_0.55.0
## [19] Biobase_2.46.0
## [20] GenomicRanges_1.38.0
## [21] GenomeInfoDb_1.22.0
## [22] IRanges_2.20.0
## [23] S4Vectors_0.24.0
## [24] BiocGenerics_0.32.0
## [25] knitr_1.25
## [26] BiocStyle_2.14.0
##
## loaded via a namespace (and not attached):
## [1] backports_1.1.5 BiocFileCache_1.10.0
## [3] plyr_1.8.4 lazyeval_0.2.2
## [5] splines_3.6.1 digest_0.6.22
## [7] htmltools_0.4.0 magrittr_1.5
## [9] memoise_1.1.0 BSgenome_1.54.0
## [11] limma_3.42.0 readr_1.3.1
## [13] annotate_1.64.0 R.utils_2.9.0
## [15] askpass_1.1 siggenes_1.60.0
## [17] prettyunits_1.0.2 colorspace_1.4-1
## [19] blob_1.2.0 rappdirs_0.3.1
## [21] xfun_0.10 crayon_1.3.4
## [23] RCurl_1.95-4.12 genefilter_1.68.0
## [25] bsseq_1.22.0 GEOquery_2.54.0
## [27] zeallot_0.1.0 survival_2.44-1.1
## [29] glue_1.3.1 gtable_0.3.0
## [31] registry_0.5-1 zlibbioc_1.32.0
## [33] plyranges_1.6.0 Rhdf5lib_1.8.0
## [35] HDF5Array_1.14.0 scales_1.0.0
## [37] DBI_1.0.0 rngtools_1.4
## [39] bibtex_0.4.2 Rcpp_1.0.2
## [41] xtable_1.8-4 progress_1.2.2
## [43] bit_1.1-14 mclust_5.4.5
## [45] preprocessCore_1.48.0 httr_1.4.1
## [47] RColorBrewer_1.1-2 ellipsis_0.3.0
## [49] pkgconfig_2.0.3 reshape_0.8.8
## [51] XML_3.98-1.20 R.methodsS3_1.7.1
## [53] dbplyr_1.4.2 labeling_0.3
## [55] tidyselect_0.2.5 rlang_0.4.1
## [57] AnnotationDbi_1.48.0 munsell_0.5.0
## [59] tools_3.6.1 RSQLite_2.1.2
## [61] evaluate_0.14 stringr_1.4.0
## [63] yaml_2.2.0 bit64_0.9-7
## [65] beanplot_1.2 scrime_1.3.5
## [67] purrr_0.3.3 nlme_3.1-141
## [69] doRNG_1.7.1 nor1mix_1.3-0
## [71] R.oo_1.22.0 xml2_1.2.2
## [73] biomaRt_2.42.0 compiler_3.6.1
## [75] curl_4.2 tibble_2.1.3
## [77] stringi_1.4.3 GenomicFeatures_1.38.0
## [79] lattice_0.20-38 Matrix_1.2-17
## [81] permute_0.9-5 multtest_2.42.0
## [83] vctrs_0.2.0 pillar_1.4.2
## [85] lifecycle_0.1.0 BiocManager_1.30.9
## [87] data.table_1.12.6 bitops_1.0-6
## [89] rtracklayer_1.46.0 R6_2.4.0
## [91] bookdown_0.14 codetools_0.2-16
## [93] MASS_7.3-51.4 gtools_3.8.1
## [95] assertthat_0.2.1 rhdf5_2.30.0
## [97] openssl_1.4.1 pkgmaker_0.27
## [99] withr_2.1.2 GenomicAlignments_1.22.0
## [101] Rsamtools_2.2.0 GenomeInfoDbData_1.2.2
## [103] hms_0.5.1 quadprog_1.5-7
## [105] grid_3.6.1 base64_2.0
## [107] rmarkdown_1.16 DelayedMatrixStats_1.8.0
## [109] illuminaio_0.28.0