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Knit directory: methyl-geneset-testing/

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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$entrezid))
summary(numCpgsPerGene)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00   10.00   20.00   27.25   33.00 1485.00 
dat <- data.frame(table(table(flatAnn$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, linetype = "dashed", color = "red") +
    annotate("text", x = mod + 2, label=glue("mode = {mod}"), y=780, colour="red", 
             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")

p

Version Author Date
25a3336 Jovana Maksimovic 2020-06-23
22f00e9 Jovana Maksimovic 2020-05-19
358cd52 JovMaksimovic 2020-03-16
fig <- here("output/figures/Fig-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$Freq > 2500, "A", 
                    ifelse(dat$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.y = element_blank(),
          axis.title.x = 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

Version Author Date
8e4056b Jovana Maksimovic 2020-06-23
25a3336 Jovana Maksimovic 2020-06-23
0f4cf3b Jovana Maksimovic 2020-06-16
22f00e9 Jovana Maksimovic 2020-05-19
358cd52 JovMaksimovic 2020-03-16
fig <- here("output/figures/Fig-1D.rds")
saveRDS(p, fig, compress = FALSE)

Explore the distribution of the average number of CpGs per gene, per GO category.

ann450 <- loadAnnotation("450k")
flatAnn450 <- loadFlatAnnotation(ann450)

cpgEgGoEPIC <- cpgsEgGoFreqs(flatAnn)
cpgEgGoEPIC$Array <- "EPIC"

cpgEgGo450 <- cpgsEgGoFreqs(flatAnn450)
cpgEgGo450$Array <- "450k"

cpgEgGoEPIC %>% bind_rows(cpgEgGo450) %>% 
    group_by(Array, GO) %>%
    summarise(avg = mean(Freq)) -> datSum
`summarise()` regrouping output by 'Array' (override with `.groups` argument)
maxEPIC <- max(datSum$avg[datSum$Array == "EPIC"])
max450 <- max(datSum$avg[datSum$Array == "450k"])

bw <- 2
pal <- paletteer::paletteer_d("RColorBrewer::Set1", 2)
platform <- c("EPIC" = pal[1], "450k" = pal[2])
p <- ggplot(datSum, aes(x=avg)) +
    geom_histogram(dat = subset(datSum, Array == "450k"),
                   alpha = 0.5, aes(fill = "450k"), binwidth = bw) +
    geom_histogram(dat = subset(datSum, Array == "EPIC"),
                   alpha = 0.5, aes(fill = "EPIC"), binwidth = bw) +
    geom_vline(xintercept = maxEPIC, linetype="dashed", colour = pal[1]) +
    geom_vline(xintercept = max450, linetype="dashed", colour = pal[2]) +
    labs(x="Mean no. CpGs per gene per GO category", y = "Frequency",
         fill = "Platform") +
    annotate("text", x = maxEPIC - 4, label=glue("Max. EPIC = {maxEPIC}"),
             y = 1500, colour=pal[1], size = 2.5, hjust="right") +
    annotate("text", x = max450 - 4, label=glue("Max. 450k = {max450}"),
             y = 2500, colour=pal[2], size = 2.5, hjust="right") +
    scale_fill_manual(values = platform)
p

Version Author Date
25a3336 Jovana Maksimovic 2020-06-23
0f4cf3b Jovana Maksimovic 2020-06-16
22f00e9 Jovana Maksimovic 2020-05-19
358cd52 JovMaksimovic 2020-03-16
fig <- here("output/figures/Fig-1C.rds")
saveRDS(p, fig, compress = FALSE)

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(size = 0.3) +
    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
25a3336 Jovana Maksimovic 2020-06-23
0f4cf3b Jovana Maksimovic 2020-06-16
22f00e9 Jovana Maksimovic 2020-05-19
fig <- here("output/figures/Fig-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
25a3336 Jovana Maksimovic 2020-06-23
0f4cf3b Jovana Maksimovic 2020-06-16

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(cpgEgGoEPIC[,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(size = 0.3) + 
    stat_summary(geom="point", size = 0.5, 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
25a3336 Jovana Maksimovic 2020-06-23
fig <- here("output/figures/Fig-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] paletteer_1.2.0                           
 [10] limma_3.42.2                              
 [11] readr_1.3.1                               
 [12] annotate_1.64.0                           
 [13] askpass_1.1                               
 [14] siggenes_1.60.0                           
 [15] prettyunits_1.1.1                         
 [16] colorspace_1.4-1                          
 [17] blob_1.2.1                                
 [18] rappdirs_0.3.1                            
 [19] BiasedUrn_1.07                            
 [20] xfun_0.15                                 
 [21] prismatic_0.2.0                           
 [22] crayon_1.3.4                              
 [23] RCurl_1.98-1.2                            
 [24] genefilter_1.68.0                         
 [25] GEOquery_2.54.1                           
 [26] IlluminaHumanMethylationEPICmanifest_0.3.0
 [27] survival_3.2-3                            
 [28] ruv_0.9.7.1                               
 [29] gtable_0.3.0                              
 [30] zlibbioc_1.32.0                           
 [31] Rhdf5lib_1.8.0                            
 [32] HDF5Array_1.14.4                          
 [33] scales_1.1.1                              
 [34] DBI_1.1.0                                 
 [35] rngtools_1.5                              
 [36] Rcpp_1.0.5                                
 [37] xtable_1.8-4                              
 [38] progress_1.2.2                            
 [39] bit_1.1-15.2                              
 [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_3.99-0.3                              
 [49] dbplyr_1.4.4                              
 [50] reshape2_1.4.4                            
 [51] labeling_0.3                              
 [52] tidyselect_1.1.0                          
 [53] rlang_0.4.7                               
 [54] later_1.1.0.1                             
 [55] munsell_0.5.0                             
 [56] tools_3.6.3                               
 [57] generics_0.0.2                            
 [58] RSQLite_2.2.0                             
 [59] evaluate_0.14                             
 [60] stringr_1.4.0                             
 [61] yaml_2.2.1                                
 [62] rematch2_2.1.2                            
 [63] knitr_1.29                                
 [64] bit64_0.9-7                               
 [65] fs_1.4.2                                  
 [66] beanplot_1.2                              
 [67] scrime_1.3.5                              
 [68] methylumi_2.32.0                          
 [69] purrr_0.3.4                               
 [70] nlme_3.1-148                              
 [71] doRNG_1.8.2                               
 [72] whisker_0.4                               
 [73] nor1mix_1.3-0                             
 [74] xml2_1.3.2                                
 [75] biomaRt_2.42.1                            
 [76] compiler_3.6.3                            
 [77] rstudioapi_0.11                           
 [78] curl_4.3                                  
 [79] statmod_1.4.34                            
 [80] stringi_1.4.6                             
 [81] GenomicFeatures_1.38.2                    
 [82] lattice_0.20-41                           
 [83] Matrix_1.2-18                             
 [84] IlluminaHumanMethylation450kmanifest_0.4.0
 [85] multtest_2.42.0                           
 [86] vctrs_0.3.1                               
 [87] pillar_1.4.5                              
 [88] lifecycle_0.2.0                           
 [89] data.table_1.12.8                         
 [90] bitops_1.0-6                              
 [91] httpuv_1.5.4                              
 [92] rtracklayer_1.46.0                        
 [93] R6_2.4.1                                  
 [94] promises_1.1.1                            
 [95] gridExtra_2.3                             
 [96] codetools_0.2-16                          
 [97] MASS_7.3-51.6                             
 [98] assertthat_0.2.1                          
 [99] rhdf5_2.30.1                              
[100] openssl_1.4.2                             
[101] rprojroot_1.3-2                           
[102] withr_2.2.0                               
[103] GenomicAlignments_1.22.1                  
[104] Rsamtools_2.2.3                           
[105] GenomeInfoDbData_1.2.2                    
[106] hms_0.5.3                                 
[107] quadprog_1.5-8                            
[108] tidyr_1.1.0                               
[109] base64_2.0                                
[110] rmarkdown_2.3                             
[111] DelayedMatrixStats_1.8.0                  
[112] illuminaio_0.28.0                         
[113] git2r_0.27.1