In this analysis I will try out an approach that preserved per-sample information. It aggregates methylaiton values for all CpGs into a single median value.
suppressPackageStartupMessages({
library("missMethyl")
library("kableExtra")
library("mitch")
library("eulerr")
library("IlluminaHumanMethylation450kmanifest")
library("IlluminaHumanMethylation450kanno.ilmn12.hg19")
library("tictoc")
library("limma")
})
CORES=12Start with gathering the annotation set.
ann450k <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)
myann <- data.frame(ann450k[,c("UCSC_RefGene_Name","Regulatory_Feature_Group")])
promoters <- grep("Prom",myann$Regulatory_Feature_Group)mx <- readRDS("dm3_mx.Rds")
design <- readRDS("dm3_design.Rds")Reactome pathways were downloaded on the 14th Sept 2023 from MsigDB.
gs_entrez <- gmt_import("c2.cp.reactome.v2023.1.Hs.entrez.gmt")
gs_symbols <- gmt_import("c2.cp.reactome.v2023.1.Hs.symbols.gmt")Determine the median of probe values
gn <- unique(unlist(strsplit( myann$UCSC_RefGene_Name ,";")))
gnl <- strsplit( myann$UCSC_RefGene_Name ,";")
gnl <- mclapply(gnl,unique,mc.cores=CORES)
myann$gnl <- gnl
mymed <- function(g) {
probes <- rownames(myann[grep(g,myann$gnl),])
rows <- which(rownames(mx) %in% probes)
if ( length(rows) > 1 ) {
b <- mx[rows,]
med <- apply(b,2,median)
med <- matrix(med,nrow=1)
colnames(med) <- colnames(b)
rownames(med) <- g
return(med)
}
}
med <- mclapply(gn,mymed,mc.cores=CORES)
med <- med[lapply(med,length)>0]
medf <- do.call(rbind,med)Now aggregate by median for gene sets.
i=1
mymed2 <- function(mx,gsets,i) {
nm <- names(gsets)[i]
gs <- gsets[[i]]
med <- matrix(apply(mx[which(rownames(mx) %in% gs),],2,median),nrow=1)
colnames(med) <- colnames(mx)
rownames(med) <- nm
return(med)
}
med2 <- mclapply(1:length(gs_symbols), function(i) {
mymed2(mx=medf,gsets=gs_symbols,i=i)
},mc.cores=CORES)
medf2 <- do.call(rbind,med2)Then do a limma test. See if it is more robust than other approaches.
fit.reduced <- lmFit(medf,design)
fit.reduced <- eBayes(fit.reduced)
summary(decideTests(fit.reduced))## (Intercept) age sexmale groups
## Down 9708 195 725 0
## NotSig 2681 19514 18535 19714
## Up 7325 5 454 0dm <- topTable(fit.reduced,coef=4, number = Inf)
#write.table(dma3a,file="dma3a.tsv")
head(dm, 30) %>% kbl() %>% kable_paper("hover", full_width = F)| logFC | AveExpr | t | P.Value | adj.P.Val | B | |
|---|---|---|---|---|---|---|
| NHLRC1 | -0.2420055 | -1.7115786 | -4.785634 | 0.0000036 | 0.0715680 | 4.0385303 |
| IGF2 | -0.1012726 | -0.2004777 | -3.973273 | 0.0001038 | 0.3526937 | 1.1059260 |
| FAM153C | -0.2897096 | 0.7535631 | -3.959580 | 0.0001094 | 0.3526937 | 1.0602332 |
| CD177 | -0.1651738 | 0.7673867 | -3.930612 | 0.0001222 | 0.3526937 | 0.9639899 |
| SLC4A8 | -0.1836541 | -0.0749776 | -3.893643 | 0.0001407 | 0.3526937 | 0.8420105 |
| MIR431 | 0.1269209 | 2.5871245 | 3.739056 | 0.0002506 | 0.3526937 | 0.3423244 |
| MIR433 | 0.1297470 | 2.9560124 | 3.738265 | 0.0002514 | 0.3526937 | 0.3398124 |
| PQLC2 | -0.2045636 | -0.7358737 | -3.676370 | 0.0003152 | 0.3526937 | 0.1445278 |
| NKG7 | -0.1914152 | -0.6171371 | -3.660996 | 0.0003333 | 0.3526937 | 0.0964470 |
| LZTS2 | -0.1874573 | -0.0999778 | -3.649719 | 0.0003471 | 0.3526937 | 0.0612867 |
| IL21 | 0.1983379 | -1.6311602 | 3.623768 | 0.0003812 | 0.3526937 | -0.0192739 |
| LINGO3 | -0.1318951 | -0.6057878 | -3.620456 | 0.0003857 | 0.3526937 | -0.0295209 |
| LGALS1 | -0.1400368 | -0.7523987 | -3.601113 | 0.0004134 | 0.3526937 | -0.0892062 |
| GFRA3 | 0.1837116 | 0.1192049 | 3.597819 | 0.0004183 | 0.3526937 | -0.0993409 |
| FER | -0.1223290 | -0.5724057 | -3.594106 | 0.0004239 | 0.3526937 | -0.1107594 |
| RSPH9 | -0.1267647 | -0.0298640 | -3.583882 | 0.0004397 | 0.3526937 | -0.1421451 |
| BCL9L | 0.1456354 | 0.6773651 | 3.562159 | 0.0004750 | 0.3526937 | -0.2085759 |
| ARMC3 | -0.1596330 | -2.5107850 | -3.558377 | 0.0004814 | 0.3526937 | -0.2201077 |
| C16orf58 | -0.1777868 | -1.6120849 | -3.554632 | 0.0004878 | 0.3526937 | -0.2315139 |
| ITM2C | -0.1875109 | -0.6986916 | -3.550803 | 0.0004945 | 0.3526937 | -0.2431673 |
| TNFRSF6B | -0.2576646 | -0.9539851 | -3.545419 | 0.0005040 | 0.3526937 | -0.2595340 |
| CTSB | -0.1642150 | -1.8742349 | -3.544140 | 0.0005063 | 0.3526937 | -0.2634194 |
| SLC35E2 | 0.1040556 | -2.7570462 | 3.534310 | 0.0005242 | 0.3526937 | -0.2932399 |
| SERINC5 | -0.1359664 | 0.0749186 | -3.519834 | 0.0005516 | 0.3526937 | -0.3370233 |
| IL19 | 0.1208098 | 2.0249626 | 3.518365 | 0.0005544 | 0.3526937 | -0.3414558 |
| TNFAIP6 | -0.2394318 | 0.5163111 | -3.516354 | 0.0005584 | 0.3526937 | -0.3475251 |
| ZNF853 | 0.1198794 | 0.0746964 | 3.515093 | 0.0005608 | 0.3526937 | -0.3513302 |
| KIF5C | -0.1367720 | 0.2252379 | -3.511645 | 0.0005677 | 0.3526937 | -0.3617216 |
| MIR620 | 0.1831690 | 1.6362189 | 3.507591 | 0.0005758 | 0.3526937 | -0.3739331 |
| ANXA3 | -0.1495810 | -0.8393991 | -3.496924 | 0.0005977 | 0.3526937 | -0.4060001 |
y <- mitch_import(dm, DEtype="limma")## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 19714## Note: no. genes in output = 19714## Note: estimated proportion of input genes in output = 1res <- mitch_calc(y, genesets=gs_symbols, priority="effect",cores=CORES)## Note: Enrichments with large effect sizes may not be
## statistically significant.sig <- subset(res$enrichment_result,p.adjustANOVA<0.05)
nrow(subset(sig,s.dist>0))## [1] 104nrow(subset(sig,s.dist<0))## [1] 30head(subset(sig,s.dist>0),20) %>%
kbl(caption="hypermethylated pathways") %>%
kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 633 | REACTOME_KSRP_KHSRP_BINDS_AND_DESTABILIZES_MRNA | 17 | 0.0006382 | 0.4783380 | 0.0122905 |
| 1010 | REACTOME_DIGESTION | 17 | 0.0011049 | 0.4569851 | 0.0165218 |
| 1049 | REACTOME_METABOLISM_OF_COFACTORS | 19 | 0.0009538 | 0.4377788 | 0.0146058 |
| 15 | REACTOME_APOPTOTIC_FACTOR_MEDIATED_RESPONSE | 17 | 0.0030633 | 0.4148258 | 0.0319136 |
| 1233 | REACTOME_SARS_COV_1_MODULATES_HOST_TRANSLATION_MACHINERY | 34 | 0.0000339 | 0.4107425 | 0.0012321 |
| 117 | REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION | 91 | 0.0000000 | 0.3728317 | 0.0000001 |
| 1037 | REACTOME_DIGESTION_AND_ABSORPTION | 22 | 0.0030418 | 0.3649659 | 0.0319136 |
| 918 | REACTOME_EUKARYOTIC_TRANSLATION_INITIATION | 115 | 0.0000000 | 0.3626449 | 0.0000000 |
| 213 | REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE | 108 | 0.0000000 | 0.3591715 | 0.0000000 |
| 1121 | REACTOME_NONSENSE_MEDIATED_DECAY_NMD | 109 | 0.0000000 | 0.3584992 | 0.0000000 |
| 1155 | REACTOME_RESPONSE_OF_EIF2AK4_GCN2_TO_AMINO_ACID_DEFICIENCY | 97 | 0.0000000 | 0.3524526 | 0.0000002 |
| 911 | REACTOME_MRNA_CAPPING | 28 | 0.0018545 | 0.3398336 | 0.0237609 |
| 946 | REACTOME_PROCESSING_OF_CAPPED_INTRONLESS_PRE_MRNA | 29 | 0.0019703 | 0.3320084 | 0.0242335 |
| 919 | REACTOME_ACTIVATION_OF_THE_MRNA_UPON_BINDING_OF_THE_CAP_BINDING_COMPLEX_AND_EIFS_AND_SUBSEQUENT_BINDING_TO_43S | 55 | 0.0000555 | 0.3142294 | 0.0018554 |
| 380 | REACTOME_SELENOAMINO_ACID_METABOLISM | 106 | 0.0000000 | 0.3105086 | 0.0000030 |
| 176 | REACTOME_INFLUENZA_INFECTION | 150 | 0.0000000 | 0.3103779 | 0.0000000 |
| 673 | REACTOME_NEGATIVE_EPIGENETIC_REGULATION_OF_RRNA_EXPRESSION | 44 | 0.0004157 | 0.3075530 | 0.0089601 |
| 455 | REACTOME_METABOLISM_OF_POLYAMINES | 55 | 0.0000939 | 0.3044611 | 0.0026595 |
| 1242 | REACTOME_SARS_COV_2_MODULATES_HOST_TRANSLATION_MACHINERY | 46 | 0.0003577 | 0.3041787 | 0.0081186 |
| 890 | REACTOME_STABILIZATION_OF_P53 | 54 | 0.0001289 | 0.3011689 | 0.0035627 |
head(subset(sig,s.dist<0),20) %>%
kbl(caption="hypomethylated pathways") %>%
kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 993 | REACTOME_NEGATIVE_REGULATION_OF_ACTIVITY_OF_TFAP2_AP_2_FAMILY_TRANSCRIPTION_FACTORS | 10 | 0.0014050 | -0.5831811 | 0.0202932 |
| 586 | REACTOME_REGULATION_OF_COMMISSURAL_AXON_PATHFINDING_BY_SLIT_AND_ROBO | 10 | 0.0015598 | -0.5776492 | 0.0218876 |
| 570 | REACTOME_CALCITONIN_LIKE_LIGAND_RECEPTORS | 10 | 0.0029495 | -0.5428948 | 0.0312406 |
| 456 | REACTOME_APOPTOTIC_CLEAVAGE_OF_CELL_ADHESION_PROTEINS | 11 | 0.0027003 | -0.5223432 | 0.0290850 |
| 414 | REACTOME_POU5F1_OCT4_SOX2_NANOG_REPRESS_GENES_RELATED_TO_DIFFERENTIATION | 10 | 0.0042367 | -0.5222696 | 0.0411057 |
| 952 | REACTOME_PLATELET_ADHESION_TO_EXPOSED_COLLAGEN | 15 | 0.0018202 | -0.4649678 | 0.0237609 |
| 73 | REACTOME_ELEVATION_OF_CYTOSOLIC_CA2_LEVELS | 16 | 0.0017559 | -0.4517432 | 0.0232472 |
| 961 | REACTOME_INSULIN_RECEPTOR_RECYCLING | 29 | 0.0008113 | -0.3592758 | 0.0139151 |
| 531 | REACTOME_EPHA_MEDIATED_GROWTH_CONE_COLLAPSE | 29 | 0.0011745 | -0.3481331 | 0.0173576 |
| 41 | REACTOME_ROS_AND_RNS_PRODUCTION_IN_PHAGOCYTES | 34 | 0.0008063 | -0.3320212 | 0.0139151 |
| 1119 | REACTOME_TRANSFERRIN_ENDOCYTOSIS_AND_RECYCLING | 30 | 0.0033564 | -0.3093934 | 0.0344026 |
| 1050 | REACTOME_TRIGLYCERIDE_METABOLISM | 34 | 0.0034536 | -0.2897746 | 0.0351159 |
| 232 | REACTOME_REGULATION_OF_BETA_CELL_DEVELOPMENT | 41 | 0.0019829 | -0.2791408 | 0.0242335 |
| 1251 | REACTOME_GASTRULATION | 48 | 0.0020676 | -0.2569939 | 0.0247918 |
| 22 | REACTOME_NEUROTRANSMITTER_RELEASE_CYCLE | 49 | 0.0038628 | -0.2385799 | 0.0389653 |
| 467 | REACTOME_CLASS_B_2_SECRETIN_FAMILY_RECEPTORS | 89 | 0.0005397 | -0.2122236 | 0.0108885 |
| 473 | REACTOME_PEPTIDE_LIGAND_BINDING_RECEPTORS | 182 | 0.0000016 | -0.2063796 | 0.0000952 |
| 91 | REACTOME_COLLAGEN_FORMATION | 76 | 0.0018695 | -0.2063655 | 0.0237609 |
| 835 | REACTOME_PROTEIN_PROTEIN_INTERACTIONS_AT_SYNAPSES | 78 | 0.0021545 | -0.2009522 | 0.0255925 |
| 655 | REACTOME_GPCR_LIGAND_BINDING | 415 | 0.0000000 | -0.1909322 | 0.0000000 |
ttenrich <- function(m,genesets,cores=1) {
res <- mclapply( 1:length(genesets), function(i) {
gs <- genesets[i]
name <- names(gs)
n_members <- length(which(rownames(m) %in% gs[[1]]))
if ( n_members > 4 ) {
tstats <- m[which(rownames(m) %in% gs[[1]]),]
myn <- length(tstats)
mymean <- mean(tstats)
mymedian <- median(tstats)
wt <- t.test(tstats)
res <- c(name,myn,mymean,mymedian,wt$p.value)
}
} , mc.cores = cores)
res_df <- do.call(rbind, res)
rownames(res_df) <- res_df[,1]
res_df <- res_df[,-1]
colnames(res_df) <- c("n_genes","t_mean","t_median","p.value")
tmp <- apply(res_df,2,as.numeric)
rownames(tmp) <- rownames(res_df)
res_df <- tmp
res_df <- as.data.frame(res_df)
res_df <- res_df[order(res_df$p.value),]
res_df$logp <- -log10(res_df$p.value )
res_df$fdr <- p.adjust(res_df$p.value,method="fdr")
res_df[order(abs(res_df$p.value)),]
return(res_df)
}
m <- as.data.frame(dm$t)
rownames(m) <- rownames(dm)
colnames(m) <- "t"
tres <- ttenrich(m=m,genesets=gs_symbols,cores=CORES)
sig <- subset(tres,fdr<0.05)
nrow(sig)## [1] 102nrow(subset(sig,t_median>0))## [1] 60head(sig[order(-sig$t_median),],20) %>%
kbl(caption="hypermethylated pathways") %>%
kable_paper("hover", full_width = F)| n_genes | t_mean | t_median | p.value | logp | fdr | |
|---|---|---|---|---|---|---|
| REACTOME_METABOLISM_OF_COFACTORS | 19 | 0.6505516 | 0.7611475 | 0.0012627 | 2.898688 | 0.0280396 |
| REACTOME_SARS_COV_1_MODULATES_HOST_TRANSLATION_MACHINERY | 34 | 0.6574048 | 0.7539045 | 0.0000213 | 4.671469 | 0.0015700 |
| REACTOME_KSRP_KHSRP_BINDS_AND_DESTABILIZES_MRNA | 17 | 0.7154416 | 0.7384284 | 0.0020849 | 2.680920 | 0.0371382 |
| REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION | 91 | 0.5591009 | 0.5982604 | 0.0000000 | 9.689857 | 0.0000000 |
| REACTOME_DIGESTION | 17 | 0.7231936 | 0.5774700 | 0.0005352 | 3.271517 | 0.0159104 |
| REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE | 108 | 0.5362825 | 0.5762411 | 0.0000000 | 10.787962 | 0.0000000 |
| REACTOME_NONSENSE_MEDIATED_DECAY_NMD | 109 | 0.5277171 | 0.5542217 | 0.0000000 | 10.549551 | 0.0000000 |
| REACTOME_EUKARYOTIC_TRANSLATION_INITIATION | 115 | 0.5405320 | 0.5362167 | 0.0000000 | 11.467541 | 0.0000000 |
| REACTOME_NEGATIVE_REGULATION_OF_NOTCH4_SIGNALING | 52 | 0.3940041 | 0.5302456 | 0.0015034 | 2.822915 | 0.0301701 |
| REACTOME_REGULATION_OF_MRNA_STABILITY_BY_PROTEINS_THAT_BIND_AU_RICH_ELEMENTS | 85 | 0.4160772 | 0.5120525 | 0.0000105 | 4.978480 | 0.0009463 |
| REACTOME_FCERI_MEDIATED_NF_KB_ACTIVATION | 75 | 0.3737093 | 0.5109577 | 0.0012472 | 2.904063 | 0.0280396 |
| REACTOME_DIGESTION_AND_ABSORPTION | 22 | 0.5725403 | 0.4981110 | 0.0014630 | 2.834750 | 0.0301701 |
| REACTOME_METABOLISM_OF_POLYAMINES | 55 | 0.4408615 | 0.4899451 | 0.0003635 | 3.439460 | 0.0128644 |
| REACTOME_PROCESSING_OF_CAPPED_INTRONLESS_PRE_MRNA | 29 | 0.5083895 | 0.4863145 | 0.0025190 | 2.598766 | 0.0443843 |
| REACTOME_RESPONSE_OF_EIF2AK4_GCN2_TO_AMINO_ACID_DEFICIENCY | 97 | 0.5389763 | 0.4861120 | 0.0000000 | 9.483704 | 0.0000001 |
| REACTOME_STABILIZATION_OF_P53 | 54 | 0.4452783 | 0.4749731 | 0.0001552 | 3.808997 | 0.0063583 |
| REACTOME_REGULATION_OF_EXPRESSION_OF_SLITS_AND_ROBOS | 161 | 0.4210261 | 0.4749654 | 0.0000000 | 8.794701 | 0.0000003 |
| REACTOME_NUCLEAR_EVENTS_MEDIATED_BY_NFE2L2 | 76 | 0.3186231 | 0.4485395 | 0.0028669 | 2.542580 | 0.0474217 |
| REACTOME_DOWNSTREAM_SIGNALING_EVENTS_OF_B_CELL_RECEPTOR_BCR | 78 | 0.3875343 | 0.4473950 | 0.0001894 | 3.722714 | 0.0073084 |
| REACTOME_ACTIVATION_OF_THE_MRNA_UPON_BINDING_OF_THE_CAP_BINDING_COMPLEX_AND_EIFS_AND_SUBSEQUENT_BINDING_TO_43S | 55 | 0.4820461 | 0.4420311 | 0.0001007 | 3.996811 | 0.0046656 |
nrow(subset(sig,t_median<0))## [1] 42head(sig[order(sig$t_median),],20) %>%
kbl(caption="hypomethylated pathways") %>%
kable_paper("hover", full_width = F)| n_genes | t_mean | t_median | p.value | logp | fdr | |
|---|---|---|---|---|---|---|
| REACTOME_VITAMINS | 6 | -1.4153951 | -1.4319918 | 0.0020622 | 2.685669 | 0.0371382 |
| REACTOME_NEGATIVE_REGULATION_OF_ACTIVITY_OF_TFAP2_AP_2_FAMILY_TRANSCRIPTION_FACTORS | 10 | -1.1117713 | -1.1434539 | 0.0014321 | 2.844039 | 0.0301701 |
| REACTOME_APOPTOTIC_CLEAVAGE_OF_CELL_ADHESION_PROTEINS | 11 | -0.9459299 | -1.0111542 | 0.0028201 | 2.549729 | 0.0471283 |
| REACTOME_CALCITONIN_LIKE_LIGAND_RECEPTORS | 10 | -1.0093147 | -1.0037701 | 0.0018083 | 2.742720 | 0.0341725 |
| REACTOME_SULFIDE_OXIDATION_TO_SULFATE | 5 | -0.9406360 | -0.9141661 | 0.0006724 | 3.172368 | 0.0187926 |
| REACTOME_REGULATION_OF_COMMISSURAL_AXON_PATHFINDING_BY_SLIT_AND_ROBO | 10 | -1.0886355 | -0.8843506 | 0.0015066 | 2.821997 | 0.0301701 |
| REACTOME_INSULIN_RECEPTOR_RECYCLING | 29 | -0.7003122 | -0.7624464 | 0.0001529 | 3.815732 | 0.0063583 |
| REACTOME_ROS_AND_RNS_PRODUCTION_IN_PHAGOCYTES | 34 | -0.7562956 | -0.7475170 | 0.0005465 | 3.262436 | 0.0159104 |
| REACTOME_TRANSFERRIN_ENDOCYTOSIS_AND_RECYCLING | 30 | -0.5801228 | -0.7451496 | 0.0005264 | 3.278700 | 0.0159104 |
| REACTOME_TRANSCRIPTIONAL_REGULATION_OF_TESTIS_DIFFERENTIATION | 12 | -0.8318114 | -0.6761622 | 0.0025704 | 2.589992 | 0.0448031 |
| REACTOME_REGULATION_OF_BETA_CELL_DEVELOPMENT | 41 | -0.5715274 | -0.6398835 | 0.0006502 | 3.186929 | 0.0184918 |
| REACTOME_PEPTIDE_LIGAND_BINDING_RECEPTORS | 182 | -0.4434256 | -0.6155905 | 0.0000010 | 5.983200 | 0.0001204 |
| REACTOME_GASTRULATION | 48 | -0.5344044 | -0.5966044 | 0.0004897 | 3.310051 | 0.0158768 |
| REACTOME_CLASS_A_1_RHODOPSIN_LIKE_RECEPTORS | 295 | -0.4174122 | -0.5782931 | 0.0000000 | 7.947299 | 0.0000015 |
| REACTOME_PROTEIN_PROTEIN_INTERACTIONS_AT_SYNAPSES | 78 | -0.4330859 | -0.5399548 | 0.0009486 | 3.022938 | 0.0226118 |
| REACTOME_HEPARAN_SULFATE_HEPARIN_HS_GAG_METABOLISM | 50 | -0.4096542 | -0.5146453 | 0.0019333 | 2.713710 | 0.0360209 |
| REACTOME_AMINO_ACIDS_REGULATE_MTORC1 | 41 | -0.4301327 | -0.5074996 | 0.0030359 | 2.517718 | 0.0490412 |
| REACTOME_GPCR_LIGAND_BINDING | 415 | -0.4072606 | -0.4695270 | 0.0000000 | 11.280495 | 0.0000000 |
| REACTOME_CLASS_B_2_SECRETIN_FAMILY_RECEPTORS | 89 | -0.4242197 | -0.4695270 | 0.0004673 | 3.330406 | 0.0157810 |
| REACTOME_TRIGLYCERIDE_METABOLISM | 34 | -0.5609747 | -0.4661456 | 0.0008489 | 3.071147 | 0.0210430 |
Compare mitch and t-test approaches.
For reproducibility.
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## 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_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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: Australia/Melbourne
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] ENmix_1.36.03
## [2] doParallel_1.0.17
## [3] qqman_0.1.9
## [4] RCircos_1.2.2
## [5] beeswarm_0.4.0
## [6] forestplot_3.1.3
## [7] abind_1.4-5
## [8] checkmate_2.2.0
## [9] reshape2_1.4.4
## [10] gplots_3.1.3
## [11] GEOquery_2.68.0
## [12] RColorBrewer_1.1-3
## [13] topconfects_1.16.0
## [14] DMRcatedata_2.18.0
## [15] ExperimentHub_2.8.1
## [16] AnnotationHub_3.8.0
## [17] BiocFileCache_2.8.0
## [18] dbplyr_2.3.3
## [19] DMRcate_2.14.1
## [20] R.utils_2.12.2
## [21] R.oo_1.25.0
## [22] R.methodsS3_1.8.2
## [23] plyr_1.8.8
## [24] dplyr_1.1.3
## [25] limma_3.56.2
## [26] tictoc_1.2
## [27] IlluminaHumanMethylation450kmanifest_0.4.0
## [28] eulerr_7.0.0
## [29] mitch_1.12.0
## [30] kableExtra_1.3.4
## [31] missMethyl_1.34.0
## [32] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [33] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
## [34] minfi_1.46.0
## [35] bumphunter_1.42.0
## [36] locfit_1.5-9.8
## [37] iterators_1.0.14
## [38] foreach_1.5.2
## [39] Biostrings_2.68.1
## [40] XVector_0.40.0
## [41] SummarizedExperiment_1.30.2
## [42] Biobase_2.60.0
## [43] MatrixGenerics_1.12.3
## [44] matrixStats_1.0.0
## [45] GenomicRanges_1.52.0
## [46] GenomeInfoDb_1.36.3
## [47] IRanges_2.34.1
## [48] S4Vectors_0.38.1
## [49] BiocGenerics_0.46.0
##
## loaded via a namespace (and not attached):
## [1] DSS_2.48.0 ProtGenerics_1.32.0
## [3] bitops_1.0-7 httr_1.4.7
## [5] webshot_0.5.5 dynamicTreeCut_1.63-1
## [7] tools_4.3.1 doRNG_1.8.6
## [9] backports_1.4.1 utf8_1.2.3
## [11] R6_2.5.1 HDF5Array_1.28.1
## [13] lazyeval_0.2.2 Gviz_1.44.1
## [15] permute_0.9-7 rhdf5filters_1.12.1
## [17] prettyunits_1.1.1 GGally_2.1.2
## [19] gridExtra_2.3 base64_2.0.1
## [21] preprocessCore_1.62.1 cli_3.6.1
## [23] sass_0.4.7 readr_2.1.4
## [25] genefilter_1.82.1 askpass_1.2.0
## [27] Rsamtools_2.16.0 systemfonts_1.0.4
## [29] foreign_0.8-85 siggenes_1.74.0
## [31] illuminaio_0.42.0 svglite_2.1.1
## [33] dichromat_2.0-0.1 scrime_1.3.5
## [35] BSgenome_1.68.0 readxl_1.4.3
## [37] impute_1.74.1 rstudioapi_0.15.0
## [39] RSQLite_2.3.1 generics_0.1.3
## [41] BiocIO_1.10.0 gtools_3.9.4
## [43] interp_1.1-4 Matrix_1.6-1
## [45] fansi_1.0.4 lifecycle_1.0.3
## [47] edgeR_3.42.4 yaml_2.3.7
## [49] rhdf5_2.44.0 blob_1.2.4
## [51] promises_1.2.1 crayon_1.5.2
## [53] lattice_0.21-8 echarts4r_0.4.5
## [55] GenomicFeatures_1.52.2 annotate_1.78.0
## [57] KEGGREST_1.40.0 pillar_1.9.0
## [59] knitr_1.44 beanplot_1.3.1
## [61] rjson_0.2.21 codetools_0.2-19
## [63] glue_1.6.2 data.table_1.14.8
## [65] vctrs_0.6.3 png_0.1-8
## [67] cellranger_1.1.0 gtable_0.3.4
## [69] assertthat_0.2.1 cachem_1.0.8
## [71] xfun_0.40 S4Arrays_1.0.6
## [73] mime_0.12 survival_3.5-7
## [75] statmod_1.5.0 interactiveDisplayBase_1.38.0
## [77] ellipsis_0.3.2 nlme_3.1-163
## [79] bit64_4.0.5 bsseq_1.36.0
## [81] progress_1.2.2 filelock_1.0.2
## [83] bslib_0.5.1 nor1mix_1.3-0
## [85] KernSmooth_2.23-22 rpart_4.1.19
## [87] colorspace_2.1-0 DBI_1.1.3
## [89] Hmisc_5.1-1 nnet_7.3-19
## [91] tidyselect_1.2.0 bit_4.0.5
## [93] compiler_4.3.1 curl_5.0.2
## [95] rvest_1.0.3 htmlTable_2.4.1
## [97] xml2_1.3.5 RPMM_1.25
## [99] DelayedArray_0.26.7 rtracklayer_1.60.1
## [101] scales_1.2.1 caTools_1.18.2
## [103] quadprog_1.5-8 rappdirs_0.3.3
## [105] stringr_1.5.0 digest_0.6.33
## [107] rmarkdown_2.24 htmltools_0.5.6
## [109] pkgconfig_2.0.3 jpeg_0.1-10
## [111] base64enc_0.1-3 sparseMatrixStats_1.12.2
## [113] highr_0.10 fastmap_1.1.1
## [115] ensembldb_2.24.0 rlang_1.1.1
## [117] htmlwidgets_1.6.2 shiny_1.7.5
## [119] DelayedMatrixStats_1.22.6 jquerylib_0.1.4
## [121] jsonlite_1.8.7 BiocParallel_1.34.2
## [123] mclust_6.0.0 VariantAnnotation_1.46.0
## [125] RCurl_1.98-1.12 magrittr_2.0.3
## [127] Formula_1.2-5 GenomeInfoDbData_1.2.10
## [129] Rhdf5lib_1.22.1 munsell_0.5.0
## [131] Rcpp_1.0.11 stringi_1.7.12
## [133] zlibbioc_1.46.0 MASS_7.3-60
## [135] org.Hs.eg.db_3.17.0 deldir_1.0-9
## [137] splines_4.3.1 multtest_2.56.0
## [139] hms_1.1.3 rngtools_1.5.2
## [141] geneplotter_1.78.0 biomaRt_2.56.1
## [143] BiocVersion_3.17.1 XML_3.99-0.14
## [145] evaluate_0.21 calibrate_1.7.7
## [147] latticeExtra_0.6-30 biovizBase_1.48.0
## [149] BiocManager_1.30.22 tzdb_0.4.0
## [151] httpuv_1.6.11 tidyr_1.3.0
## [153] openssl_2.1.0 purrr_1.0.2
## [155] reshape_0.8.9 ggplot2_3.4.3
## [157] xtable_1.8-4 restfulr_0.0.15
## [159] AnnotationFilter_1.24.0 later_1.3.1
## [161] viridisLite_0.4.2 tibble_3.2.1
## [163] memoise_2.0.1 AnnotationDbi_1.62.2
## [165] GenomicAlignments_1.36.0 cluster_2.1.4