Gene set testing for Illumina HumanMethylation Arrays
Exploring the biases on 450k arrays
Jovana Maksimovic, Alicia Oshlack and Belinda Phipson
July 17, 2020
Last updated: 2020-07-17
Checks: 7 0
Knit directory: methyl-geneset-testing/
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|---|---|---|---|---|
| Rmd | 2be6153 | JovMaksimovic | 2020-07-14 | Updated code for generating manuscript figures. |
| Rmd | cfcac1b | Jovana Maksimovic | 2020-07-09 | Updated paper figures. |
| html | 7520d90 | Jovana Maksimovic | 2020-06-23 | Build site. |
| Rmd | f6708bf | Jovana Maksimovic | 2020-06-23 | wflow_publish(c("analysis/exploreArrayBias450.Rmd")) |
| Rmd | 823b793 | Jovana Maksimovic | 2020-06-16 | UPdated analyses with label translation and new colour palette. |
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| Rmd | 63b0011 | Jovana Maksimovic | 2020-05-19 | wflow_publish(c("analysis/exploreArrayBias450.Rmd", "analysis/exploreArrayBiasEPIC.Rmd", |
| html | becf5e4 | Jovana Maksimovic | 2020-04-03 | Build site. |
| Rmd | 456a386 | Jovana Maksimovic | 2020-04-03 | wflow_publish("analysis/exploreArrayBias450.Rmd") |
| Rmd | 250d8ae | JovMaksimovic | 2020-03-16 | Comitting local changes. |
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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="450k")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$entrezid))
summary(numCpgsPerGene) Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 10.00 15.00 19.38 22.00 1299.00 dat <- data.frame(table(table(flatAnn$entrezid)))
numCpgsPerGene <- as.vector(table(flatAnn$entrezid))
med <- median(numCpgsPerGene)
mod <- getMode(numCpgsPerGene)
p <- ggplot(dat, aes(x=Var1, y=Freq)) +
geom_segment(aes(x=Var1, xend=Var1, y=0, yend=Freq), color="darkgrey") +
geom_point( color="black", size=1) +
geom_vline(xintercept = med, linetype = "dashed", color = "red") +
annotate("text", x = med + 2, label=glue("median = {med}"), y=720,
colour="red", size = 3, hjust="left") +
geom_vline(xintercept = mod - 0.5, linetype = "dashed", color = "blue") +
annotate("text", x = mod + 2, label=glue("mode = {mod}"), y=780,
colour="blue", size = 3, hjust="left") +
scale_x_discrete(breaks = c(1,5,10,20,35,50,60,70,80,90,100,125,152,202,
251,350,1299)) +
theme(axis.text.x=element_text(angle=45, hjust=1)) +
xlab("No. CpGs per gene") +
ylab("Frequency")
p
fig <- here("output/figures/SFig-1A.rds")
saveRDS(p, fig, compress = FALSE)The number of genes a CpG maps to can also vary, although the majority of CpGs only map to one gene.
dat <- data.frame(table(table(flatAnn$cpg)))
dat$Split <- ifelse(dat$Freq > 2000, "A",
ifelse(dat$Freq < 2000 & dat$Freq > 50,"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$Split == "C",], aes(x=Var1, y=Freq)) +
geom_bar(stat = "identity") +
theme(axis.title.x=element_blank(),
axis.title.y = element_blank())
p <- a + b + c + plot_layout(widths = c(1,2,6)) +
plot_annotation(caption = "No. genes mapped to a CpG",
theme = theme(plot.caption = element_text(hjust = 0.5,
size = 12)))
p
fig <- here("output/figures/SFig-1B.rds")
saveRDS(p, fig, compress = FALSE)Explore the distribution of the average number of CpGs per gene, per GO category. First, associate GO categories with the CpG and gene data.
cpgEgGo <- cpgsEgGoFreqs(flatAnn)
head(cpgEgGo) ENTREZID GO ENTREZID. Freq
1 142 GO:0000002 142 18
2 291 GO:0000002 291 11
3 1763 GO:0000002 1763 16
4 1890 GO:0000002 1890 25
5 3980 GO:0000002 3980 17
6 4205 GO:0000002 4205 20Calculate the average number of CpGs per gene, per GO category and plot the density distribution.
cpgEgGo %>%
group_by(GO) %>%
summarise(avg = mean(Freq)) -> dat`summarise()` ungrouping output (override with `.groups` argument)med <- round(median(dat$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 /Users/maksimovicjovana/Work/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`summarise()` regrouping output by 'simNo', 'noCpgs' (override with `.groups` argument)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
fig <- here("output/figures/SFig-3A.rds")
saveRDS(p, fig, compress = FALSE)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())
| Version | Author | Date |
|---|---|---|
| 7520d90 | Jovana Maksimovic | 2020-06-23 |
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`summarise()` regrouping output by 'simNo', 'noCpgs', 'method' (override with `.groups` argument)`summarise()` regrouping output by 'simNo', 'noCpgs' (override with `.groups` argument)p <- ggplot(medAvgDat, aes(x=noCpgs, y=medAvgFreq, fill=method)) +
geom_violin() +
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)
p
| Version | Author | Date |
|---|---|---|
| 7520d90 | Jovana Maksimovic | 2020-06-23 |
fig <- here("output/figures/SFig-3B.rds")
saveRDS(p, fig, compress = FALSE)
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] grid stats4 parallel stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] dplyr_1.0.0
[2] tibble_3.0.2
[3] ggplot2_3.3.2
[4] patchwork_1.0.1
[5] GO.db_3.10.0
[6] org.Hs.eg.db_3.10.0
[7] AnnotationDbi_1.48.0
[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_1.28.0
[13] locfit_1.5-9.4
[14] iterators_1.0.12
[15] foreach_1.5.0
[16] Biostrings_2.54.0
[17] XVector_0.26.0
[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_1.1.8
[2] BiocFileCache_1.10.2
[3] plyr_1.8.6
[4] splines_3.6.3
[5] digest_0.6.25
[6] htmltools_0.5.0
[7] magrittr_1.5
[8] memoise_1.1.0
[9] limma_3.42.2
[10] readr_1.3.1
[11] annotate_1.64.0
[12] askpass_1.1
[13] siggenes_1.60.0
[14] prettyunits_1.1.1
[15] colorspace_1.4-1
[16] blob_1.2.1
[17] rappdirs_0.3.1
[18] BiasedUrn_1.07
[19] xfun_0.15
[20] crayon_1.3.4
[21] RCurl_1.98-1.2
[22] genefilter_1.68.0
[23] GEOquery_2.54.1
[24] IlluminaHumanMethylationEPICmanifest_0.3.0
[25] survival_3.2-3
[26] ruv_0.9.7.1
[27] gtable_0.3.0
[28] zlibbioc_1.32.0
[29] Rhdf5lib_1.8.0
[30] HDF5Array_1.14.4
[31] scales_1.1.1
[32] DBI_1.1.0
[33] rngtools_1.5
[34] Rcpp_1.0.5
[35] xtable_1.8-4
[36] progress_1.2.2
[37] bit_1.1-15.2
[38] mclust_5.4.6
[39] preprocessCore_1.48.0
[40] httr_1.4.1
[41] RColorBrewer_1.1-2
[42] ellipsis_0.3.1
[43] farver_2.0.3
[44] pkgconfig_2.0.3
[45] reshape_0.8.8
[46] XML_3.99-0.3
[47] dbplyr_1.4.4
[48] reshape2_1.4.4
[49] labeling_0.3
[50] tidyselect_1.1.0
[51] rlang_0.4.7
[52] later_1.1.0.1
[53] munsell_0.5.0
[54] tools_3.6.3
[55] generics_0.0.2
[56] RSQLite_2.2.0
[57] evaluate_0.14
[58] stringr_1.4.0
[59] yaml_2.2.1
[60] knitr_1.29
[61] bit64_0.9-7
[62] fs_1.4.2
[63] beanplot_1.2
[64] scrime_1.3.5
[65] methylumi_2.32.0
[66] purrr_0.3.4
[67] nlme_3.1-148
[68] doRNG_1.8.2
[69] whisker_0.4
[70] nor1mix_1.3-0
[71] xml2_1.3.2
[72] biomaRt_2.42.1
[73] compiler_3.6.3
[74] rstudioapi_0.11
[75] curl_4.3
[76] statmod_1.4.34
[77] stringi_1.4.6
[78] GenomicFeatures_1.38.2
[79] lattice_0.20-41
[80] Matrix_1.2-18
[81] IlluminaHumanMethylation450kmanifest_0.4.0
[82] multtest_2.42.0
[83] vctrs_0.3.1
[84] pillar_1.4.5
[85] lifecycle_0.2.0
[86] data.table_1.12.8
[87] bitops_1.0-6
[88] httpuv_1.5.4
[89] rtracklayer_1.46.0
[90] R6_2.4.1
[91] promises_1.1.1
[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_1.4.2
[98] rprojroot_1.3-2
[99] withr_2.2.0
[100] GenomicAlignments_1.22.1
[101] Rsamtools_2.2.3
[102] GenomeInfoDbData_1.2.2
[103] hms_0.5.3
[104] quadprog_1.5-8
[105] tidyr_1.1.0
[106] base64_2.0
[107] rmarkdown_2.3
[108] DelayedMatrixStats_1.8.0
[109] illuminaio_0.28.0
[110] git2r_0.27.1