Gene set testing for Illumina HumanMethylation Arrays
Exploring the biases on EPIC arrays
Jovana Maksimovic, Alicia Oshlack and Belinda Phipson
June 16, 2020
Last updated: 2020-06-16
Checks: 7 0
Knit directory: methyl-geneset-testing/
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| Rmd | 136a9f0 | Jovana Maksimovic | 2020-06-16 | wflow_publish(“analysis/exploreArrayBiasEPIC.Rmd”) |
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library(here)
library(glue)
library(minfi)
library(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
library(missMethyl)
library(org.Hs.eg.db)
library(GO.db)
library(patchwork)
library(grid)
library(ggplot2)
library(tibble)
library(dplyr)
source(here("code/utility.R"))Array properties
Get the array annotation data.
ann <- loadAnnotation(arrayType="EPIC")Associate CpGs to genes (ENTREZ ID) using the Illumina annotation information.
flatAnn <- loadFlatAnnotation(ann)The number of CpGs annotated to a gene is highly variable.
numCpgsPerGene <- as.vector(table(flatAnn$entrezid))
summary(numCpgsPerGene) Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 10.00 20.00 27.25 33.00 1485.00 Figure 1A
View
dat <- data.frame(table(table(flatAnn$entrezid)))
med <- median(numCpgsPerGene)
mod <- getMode(numCpgsPerGene)
ggplot(dat, aes(x=Var1, y=Freq)) +
geom_segment(aes(x=Var1, xend=Var1, y=0, yend=Freq), color="darkgrey") +
geom_point( color="black", size=1) +
geom_vline(xintercept = med, linetype = "dashed", color = "red") +
annotate("text", x = med + 2, label=glue("median = {med}"), y=700, colour="red",
size = 3, hjust="left") +
geom_vline(xintercept = mod, linetype = "dashed", color = "blue") +
annotate("text", x = mod + 2, label=glue("mode = {mod}"), y=760, colour="blue",
size = 3, hjust="left") +
scale_x_discrete(breaks = c(1,10,20,30,45,55,70,80,90,100,120,135,150,160,180,
200,215,250,330,429,1485)) +
theme(axis.text.x=element_text(angle=45, hjust=1)) +
xlab("No. CpGs per gene") +
ylab("Frequency")
Download
file <- here("output/Fig-1A.pdf")
pdf(file = file)
dat <- data.frame(table(table(flatAnn$entrezid)))
med <- median(numCpgsPerGene)
mod <- getMode(numCpgsPerGene)
ggplot(dat, aes(x=Var1, y=Freq)) +
geom_segment(aes(x=Var1, xend=Var1, y=0, yend=Freq), color="darkgrey") +
geom_point( color="black", size=1) +
geom_vline(xintercept = med, linetype = "dashed", color = "red") +
annotate("text", x = med + 2, label=glue("median = {med}"), y=700, colour="red",
size = 3, hjust="left") +
geom_vline(xintercept = mod, linetype = "dashed", color = "blue") +
annotate("text", x = mod + 2, label=glue("mode = {mod}"), y=760, colour="blue",
size = 3, hjust="left") +
scale_x_discrete(breaks = c(1,10,20,30,45,55,70,80,90,100,120,135,150,160,180,
200,215,250,330,429,1485)) +
theme(axis.text.x=element_text(angle=45, hjust=1)) +
xlab("No. CpGs per gene") +
ylab("Frequency")
dev.off()png
2 Figure 1D
View
The number of genes a CpG maps to can also vary, although the majority of CpGs only map to one gene.
dat <- data.frame(table(table(flatAnn$cpg)))
dat$Split <- ifelse(dat$Freq > 2500, "A",
ifelse(dat$Freq < 2500 & dat$Freq > 65,"B","C"))
a <- ggplot(dat[dat$Split == "A",], aes(x=Var1, y=Freq)) +
geom_bar(stat = "identity") +
theme(axis.title.x = element_blank(),
axis.text.y=element_text(angle=90, hjust=0.5)) +
ylab("Frequency")
b <- ggplot(dat[dat$Split == "B",], aes(x=Var1, y=Freq)) +
geom_bar(stat = "identity") +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
c <- ggplot(dat[dat$Split == "C",], aes(x=Var1, y=Freq)) +
geom_bar(stat = "identity") +
theme(axis.title.x=element_blank(),
axis.title.y = element_blank())
a + b + c + plot_layout(widths = c(1,2,6)) +
plot_annotation(caption = "No. genes mapped to a CpG",
theme = theme(plot.caption = element_text(hjust = 0.5,
size = 12)))
Download
file <- here("output/Fig-1D.pdf")
pdf(file = file)
dat <- data.frame(table(table(flatAnn$cpg)))
dat$Split <- ifelse(dat$Freq > 2500, "A",
ifelse(dat$Freq < 2500 & dat$Freq > 65,"B","C"))
a <- ggplot(dat[dat$Split == "A",], aes(x=Var1, y=Freq)) +
geom_bar(stat = "identity") +
theme(axis.title.x = element_blank(),
axis.text.y=element_text(angle=90, hjust=0.5)) +
ylab("Frequency")
b <- ggplot(dat[dat$Split == "B",], aes(x=Var1, y=Freq)) +
geom_bar(stat = "identity") +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
c <- ggplot(dat[dat$Split == "C",], aes(x=Var1, y=Freq)) +
geom_bar(stat = "identity") +
theme(axis.title.x=element_blank(),
axis.title.y = element_blank())
a + b + c + plot_layout(widths = c(1,2,6)) +
plot_annotation(caption = "No. genes mapped to a CpG",
theme = theme(plot.caption = element_text(hjust = 0.5,
size = 12)))
dev.off()png
2 Explore the distribution of the average number of CpGs per gene, per GO category. First, associate GO categories with the CpG and gene data.
cpgEgGo <- cpgsEgGoFreqs(flatAnn)
head(cpgEgGo) ENTREZID GO ENTREZID. Freq
1 142 GO:0000002 142 40
2 291 GO:0000002 291 17
3 1763 GO:0000002 1763 28
4 1890 GO:0000002 1890 24
5 3980 GO:0000002 3980 21
6 4205 GO:0000002 4205 42Calculate the average number of CpGs per gene, per GO category and plot the density distribution.
cpgEgGo %>%
group_by(GO) %>%
summarise(avg = mean(Freq)) -> dat
med <- round(median(dat$avg), 2)
p <- ggplot(dat, aes(x=avg)) +
geom_density() +
geom_vline(xintercept = med, linetype="dashed", colour = "red") +
labs(x="Mean no. CpGs per gene per GO category", y = "Density") +
annotate("text", x = med + 4, label=glue("median = {med}"),
y = 0.035, colour="red", size = 3, hjust="left")
p
Null simulations
Null simulation strategy: randomly select 50, 100, 500, 1000, 5000 and 10000 sets of CpGs and perform GO testing on each set 100 times, with and without adjusting for the various biases on the array.
The code used for the simulation can be found in /oshlack_lab/jovana.maksimovic/research/methyl-geneset-testing/code/randCpgSim.R.
The following boxplots show what proportion of the 100 simulations, at each level of CpGs sampled, had a raw p-value less than 0.05. This gives us an idea of the false discovery rate with and without adjustment for the number of CpGs annotated to a gene.
dat <- readRDS(here("output/random-cpg-sims/EPIC.rds"))
dat %>% filter(method %in% names(dict)) %>%
mutate(method = unname(dict[method])) %>%
group_by(simNo, noCpgs, method) %>%
summarise(pSig = sum(P.DE < 0.05)/length(P.DE)) -> sigDat
p <- ggplot(sigDat, aes(x=noCpgs, y=pSig, fill=method)) +
geom_violin() +
geom_hline(yintercept=0.05, linetype="dashed", color = "red") +
labs(y="Prop. GO cat. with p-value < 0.05", x="No. randomly sampled CpGs",
fill="Method") +
scale_fill_manual(values = methodCols)
p
| Version | Author | Date |
|---|---|---|
| 22f00e9 | Jovana Maksimovic | 2020-05-19 |
QQ plots of randomly selected simulations at each level of CpGs sampled.
set.seed(42)
s <- sample(1:100, 3)
dat %>% filter(simNo %in% s) %>%
filter(method %in% names(dict)) %>%
mutate(method = unname(dict[method])) %>%
arrange(simNo, noCpgs, method, P.DE) %>%
group_by(simNo, noCpgs, method) %>%
mutate(exp = 1:n()/n()) -> subDat
p <- ggplot(subDat, aes(x=-log10(exp), y=-log10(P.DE), color=method)) +
geom_point(shape = 1, size = 0.5) +
facet_grid(noCpgs ~ simNo, scales = "free_y") +
scale_color_manual(values = methodCols)
p + geom_line(aes(x=-log10(exp), y=-log10(exp)),
linetype="dashed", color = "black") +
labs(y=expression(Observed~~-log[10](italic(p))),
x=expression(Expected~~-log[10](italic(p))),
color="Method") +
theme(legend.position="bottom", strip.text.x = element_blank())
Explore the relationship between the median, average number of CpGs, per gene, per GO category and the various sources of bias on the array.
goFreq <- as_tibble(unique(cpgEgGo[,c("GO","Freq")]))
dat %>% filter(method %in% names(dict)) %>%
mutate(method = unname(dict[method])) %>%
filter(P.DE < 0.05) %>%
inner_join(goFreq, by=c("GO" = "GO")) %>%
group_by(simNo, noCpgs, method, GO) %>%
summarise(avgFreq=mean(Freq)) %>%
group_by(simNo, noCpgs, method) %>%
summarise(medAvgFreq=median(avgFreq)) -> medAvgDat
p <- ggplot(medAvgDat, aes(x=noCpgs, y=medAvgFreq, fill=method)) +
geom_violin()
p + stat_summary(geom="point", size=1, color="white", position = position_dodge(0.9),
show.legend = FALSE, fun = median) +
labs(y="Median avg. no. CpGs/gene/GO cat.",
x="No. randomly sampled CpGs",
fill="Method") +
scale_fill_manual(values = methodCols)
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /config/RStudio/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /config/RStudio/R/3.6.1/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats4 parallel stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] dplyr_0.8.5
[2] tibble_3.0.1
[3] ggplot2_3.3.0
[4] patchwork_1.0.0
[5] GO.db_3.8.2
[6] org.Hs.eg.db_3.8.2
[7] AnnotationDbi_1.46.1
[8] missMethyl_1.20.4
[9] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
[10] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
[11] minfi_1.32.0
[12] bumphunter_1.26.0
[13] locfit_1.5-9.1
[14] iterators_1.0.12
[15] foreach_1.5.0
[16] Biostrings_2.54.0
[17] XVector_0.24.0
[18] SummarizedExperiment_1.16.1
[19] DelayedArray_0.12.3
[20] BiocParallel_1.20.1
[21] matrixStats_0.56.0
[22] Biobase_2.46.0
[23] GenomicRanges_1.38.0
[24] GenomeInfoDb_1.22.1
[25] IRanges_2.20.2
[26] S4Vectors_0.24.4
[27] BiocGenerics_0.32.0
[28] glue_1.4.1
[29] here_0.1
[30] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.1.7
[2] BiocFileCache_1.10.2
[3] plyr_1.8.6
[4] splines_3.6.1
[5] digest_0.6.25
[6] htmltools_0.4.0
[7] magrittr_1.5
[8] memoise_1.1.0
[9] limma_3.42.2
[10] readr_1.3.1
[11] annotate_1.62.0
[12] askpass_1.1
[13] siggenes_1.60.0
[14] prettyunits_1.1.1
[15] colorspace_1.4-1
[16] blob_1.2.0
[17] rappdirs_0.3.1
[18] BiasedUrn_1.07
[19] xfun_0.14
[20] crayon_1.3.4
[21] RCurl_1.95-4.12
[22] genefilter_1.68.0
[23] GEOquery_2.54.1
[24] IlluminaHumanMethylationEPICmanifest_0.3.0
[25] survival_2.44-1.1
[26] ruv_0.9.7.1
[27] gtable_0.3.0
[28] registry_0.5-1
[29] zlibbioc_1.30.0
[30] Rhdf5lib_1.6.1
[31] HDF5Array_1.14.4
[32] scales_1.1.1
[33] DBI_1.0.0
[34] rngtools_1.4
[35] bibtex_0.4.2
[36] Rcpp_1.0.4.6
[37] xtable_1.8-4
[38] progress_1.2.2
[39] bit_1.1-14
[40] mclust_5.4.6
[41] preprocessCore_1.48.0
[42] httr_1.4.1
[43] RColorBrewer_1.1-2
[44] ellipsis_0.3.1
[45] farver_2.0.3
[46] pkgconfig_2.0.3
[47] reshape_0.8.8
[48] XML_3.98-1.20
[49] dbplyr_1.4.2
[50] reshape2_1.4.3
[51] labeling_0.3
[52] tidyselect_1.1.0
[53] rlang_0.4.6
[54] later_1.0.0
[55] munsell_0.5.0
[56] tools_3.6.1
[57] RSQLite_2.1.2
[58] evaluate_0.14
[59] stringr_1.4.0
[60] yaml_2.2.1
[61] knitr_1.28
[62] bit64_0.9-7
[63] fs_1.4.1
[64] beanplot_1.2
[65] scrime_1.3.5
[66] methylumi_2.30.0
[67] purrr_0.3.4
[68] nlme_3.1-147
[69] doRNG_1.7.1
[70] whisker_0.4
[71] nor1mix_1.3-0
[72] xml2_1.3.2
[73] biomaRt_2.42.1
[74] compiler_3.6.1
[75] curl_4.3
[76] statmod_1.4.32
[77] stringi_1.4.6
[78] GenomicFeatures_1.36.4
[79] lattice_0.20-41
[80] Matrix_1.2-18
[81] IlluminaHumanMethylation450kmanifest_0.4.0
[82] multtest_2.40.0
[83] vctrs_0.3.0
[84] pillar_1.4.4
[85] lifecycle_0.2.0
[86] data.table_1.12.8
[87] bitops_1.0-6
[88] httpuv_1.5.2
[89] rtracklayer_1.44.4
[90] R6_2.4.1
[91] promises_1.1.0
[92] gridExtra_2.3
[93] codetools_0.2-16
[94] MASS_7.3-51.6
[95] assertthat_0.2.1
[96] rhdf5_2.30.1
[97] openssl_1.4.1
[98] pkgmaker_0.27
[99] rprojroot_1.3-2
[100] withr_2.2.0
[101] GenomicAlignments_1.20.1
[102] Rsamtools_2.0.1
[103] GenomeInfoDbData_1.2.1
[104] hms_0.5.3
[105] quadprog_1.5-8
[106] tidyr_1.1.0
[107] base64_2.0
[108] rmarkdown_2.1
[109] DelayedMatrixStats_1.8.0
[110] illuminaio_0.28.0
[111] git2r_0.27.1