Introduction

The purpose of this analysis is to apply different enrichment analysis approaches, and see which approach is most parsimonious with gene expressoin differences.

We will specifically be testing the GMEA approach versus existing approaches, namely GOseq and mitch.

This study (GSE158433) is a super-series contains matched RNA-seq (GSE158420) and Infinium EPIC array (GSE158422) data for matched control and cancer tissues.

In this script, we will be processing the EPIC DNA methylation data and performing the statistical analysis in limma and saving the results for downstream enrichment analysis.

suppressPackageStartupMessages({
  library("plyr")
  library("R.utils")
  library("missMethyl")
  library("limma")
  library("DMRcate")
  library("DMRcatedata")
  library("topconfects")
  library("minfi")
  library("IlluminaHumanMethylation450kmanifest")
  library("RColorBrewer")
  library("IlluminaHumanMethylation450kanno.ilmn12.hg19")
  library("GEOquery")
  library("eulerr")
  library("plyr")
  library("gplots")
  library("reshape2")
  library("forestplot")
  library("beeswarm")
  library("RCircos")
  library("qqman")
  library("ENmix")
  library("tictoc")
  library("mitch")
  library("kableExtra")
})

source("../meth_functions.R")
CORES=detectCores()/2

Obtaining array annotations

anno <- getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
myann <- data.frame(anno[,c("UCSC_RefGene_Name","Regulatory_Feature_Group","Islands_Name","Relation_to_Island")])
promoters <- grep("Prom",myann$Regulatory_Feature_Group)

Load array result

GSE158422 is the accession number for the array data.

#GSE158422 <- readRDS("GSE158422.rds")
dm <- read.table("GSE158422_limma.tsv",header=TRUE,sep="\t")

head(dm,50) %>%
  kbl(caption = "Top significant probes with limma") %>%
  kable_paper("hover", full_width = F)
Top significant probes with limma
Row.names UCSC_RefGene_Name Regulatory_Feature_Group Islands_Name Relation_to_Island logFC AveExpr t P.Value adj.P.Val B
5777 cg00159780 REXO1L2P;REXO1L1 chr8:86573421-86575248 Island -1.6798198 1.1155755 -13.44709 0 0 38.71809
61184 cg01772014 C20orf160 Unclassified_Cell_type_specific chr20:30606492-30607174 N_Shore -1.2582446 -2.1149387 -13.39356 0 0 38.51305
468211 cg14205001 NOTCH1 chr9:139405089-139405292 S_Shore 1.0618317 1.0243915 13.36967 0 0 38.42139
221392 cg06569947 DCUN1D2 OpenSea 1.6867415 1.6161405 13.19198 0 0 37.73740
577564 cg17655624 ZNF385A;ZNF385A Unclassified_Cell_type_specific chr12:54784900-54785238 S_Shore -1.5945158 -2.4378214 -13.16667 0 0 37.63961
444856 cg13531667 MCC chr5:112823256-112824304 S_Shore -1.9644152 -2.4559055 -13.15160 0 0 37.58135
669100 cg20979986 SERINC5;SERINC5;SERINC5;SERINC5;SERINC5 Unclassified_Cell_type_specific OpenSea -1.9595499 -1.5027312 -13.07900 0 0 37.30026
657981 cg20568402 DCUN1D2 OpenSea 1.6754713 1.3733742 13.01946 0 0 37.06922
60887 cg01762663 DOCK9;DOCK9 OpenSea 1.4139138 1.6406340 12.96488 0 0 36.85705
760860 cg24334634 DCUN1D2 OpenSea 1.8972876 1.3586261 12.90262 0 0 36.61448
791527 cg25422938 RPA3;UMAD1;UMAD1;UMAD1 OpenSea 2.0846275 0.9242368 12.86102 0 0 36.45216
102318 cg02973960 CAMKK2;CAMKK2;CAMKK2;CAMKK2;CAMKK2;CAMKK2;CAMKK2;CAMKK2 OpenSea 1.8682224 1.0004605 12.82353 0 0 36.30565
159551 cg04690379 SPTLC2 OpenSea 1.4806479 1.6621046 12.80502 0 0 36.23326
401316 cg12131862 ATP2B4;ATP2B4 OpenSea 1.3163515 1.1557853 12.71756 0 0 35.89058
560139 cg17094249 Unclassified_Cell_type_specific OpenSea -1.7223154 -1.5463465 -12.69147 0 0 35.78817
549143 cg16732616 DMRTA2 chr1:50884228-50891471 Island 3.0296072 -2.2822863 12.68159 0 0 35.74937
440784 cg13417058 TKT;TKT;TKT;TKT chr3:53289533-53290213 N_Shore -1.2341774 -3.1783518 -12.63107 0 0 35.55071
576795 cg17627973 SRPK2;SRPK2 chr7:104885049-104885400 Island 1.2294196 1.4826669 12.56735 0 0 35.29974
91439 cg02655980 GALK2;GALK2;GALK2;MIR4716;GALK2 OpenSea 1.1405593 1.9522492 12.54037 0 0 35.19328
15682 cg00446235 TSTD1;TSTD1;TSTD1 Promoter_Associated chr1:161008377-161008830 Island -1.4783395 -2.5090775 -12.52990 0 0 35.15197
397225 cg12003230 C21orf84 OpenSea 1.2440632 1.2608853 12.48583 0 0 34.97786
667781 cg20934096 C7orf50;C7orf50;C7orf50 chr7:1119980-1120248 N_Shelf 1.8042680 1.0936170 12.47564 0 0 34.93756
736989 cg23480296 DAB2IP OpenSea 1.5157757 2.0651030 12.43746 0 0 34.78647
317907 cg09496762 Promoter_Associated chr20:42285961-42286535 Island -1.4003999 -3.4071121 -12.41849 0 0 34.71133
48818 cg01399319 CCNL2;CCNL2;CCNL2;CCNL2 chr1:1322644-1322924 S_Shelf 1.5162090 1.6885478 12.41777 0 0 34.70846
74972 cg02174232 APEH Promoter_Associated chr3:49710968-49712279 Island -1.2885332 -2.2836215 -12.40930 0 0 34.67489
836007 cg26993251 MACROD1 Promoter_Associated chr11:63932990-63934070 Island -1.8527516 -2.5529067 -12.38306 0 0 34.57087
303960 cg09067993 SUCLG2;SUCLG2 OpenSea 1.6811007 1.0576230 12.37682 0 0 34.54612
689315 cg21722612 TPCN1;TPCN1;TPCN1 OpenSea 1.7680183 1.3809215 12.36454 0 0 34.49738
224178 cg06651376 SMYD4 Unclassified_Cell_type_specific OpenSea 1.7592095 2.1211809 12.34589 0 0 34.42334
536592 cg16350950 ARFGEF2;ARFGEF2 OpenSea 1.5848272 1.7602642 12.33256 0 0 34.37040
141548 cg04147064 TBC1D22A;TBC1D22A;TBC1D22A;TBC1D22A;TBC1D22A chr22:47558272-47558513 S_Shelf 1.6574167 1.1469762 12.31667 0 0 34.30722
281220 cg08365845 GNAI2;GNAI2 Promoter_Associated chr3:50264402-50265101 S_Shore -1.1483956 -3.7645116 -12.30084 0 0 34.24430
811041 cg26134703 FAM49B;FAM49B;FAM49B;FAM49B;FAM49B;FAM49B;FAM49B;FAM49B OpenSea 1.1274026 2.1993105 12.28962 0 0 34.19966
327281 cg09792881 DMRTA2 chr1:50884228-50891471 Island 2.6466221 -2.3966140 12.28366 0 0 34.17594
577091 cg17639795 chr19:2950359-2950962 N_Shore 0.9871701 1.4478340 12.28070 0 0 34.16417
500102 cg15169038 PLEKHG1 OpenSea 1.2318433 1.2810663 12.27029 0 0 34.12275
663755 cg20778786 OpenSea -2.3061328 -1.4168169 -12.25057 0 0 34.04422
515979 cg15698065 DCUN1D2 OpenSea 1.1608426 1.1268656 12.24875 0 0 34.03695
27631 cg00781839 DCUN1D2 OpenSea 1.5828415 2.1405283 12.21779 0 0 33.91358
833500 cg26908825 GMDS OpenSea 1.5079284 0.9476486 12.17557 0 0 33.74512
690514 cg21768042 ARAP3 Unclassified OpenSea 0.9046459 1.1325097 12.16453 0 0 33.70104
66144 cg01915688 PPP1R12A;PPP1R12A;PPP1R12A;PPP1R12A;PPP1R12A OpenSea 1.4961453 2.2191384 12.16293 0 0 33.69464
601050 cg18483766 PPP4R1;PPP4R1;PPP4R1 OpenSea 1.3901415 1.7812862 12.15909 0 0 33.67929
693283 cg21870038 RFFL;RFFL;RFFL Promoter_Associated OpenSea -1.8666911 -3.1553817 -12.13781 0 0 33.59427
631020 cg19586165 STARD3;STARD3;STARD3 OpenSea 1.1964628 1.4571282 12.08808 0 0 33.39533
351767 cg10546600 BRD7;BRD7 OpenSea 1.3866309 1.7766308 12.08519 0 0 33.38374
228200 cg06767326 DCUN1D2 OpenSea 1.7872910 2.0093867 12.07603 0 0 33.34708
54185 cg01558390 PIP4K2A OpenSea 1.6242778 0.6886679 12.07576 0 0 33.34599
167196 cg04916486 TRAPPC10 OpenSea 1.2041742 1.2251040 12.05576 0 0 33.26588 
rownames(dm) <- dm[,1]
dm <- dm[,c("UCSC_RefGene_Name","t")]

GMEA whole gene

hist(dm$t)

tic() ; res <- calc_sc(dm) ; toc() #32 cores 95.5
## 93.328 sec elapsed
res2 <- res[[2]]

head(res2,20) %>%
  kbl(caption = "Top significant genes with GMEA") %>%
  kable_paper("hover", full_width = F)
Top significant genes with GMEA
nprobes mean median p-value(sc) sig fdr(sc)
PTPRN2 1474 -3.816597 -4.398750 0 196.60419 0
MAD1L1 817 -2.058088 -2.254502 0 70.64358 0
TNXB 529 -3.158771 -3.592807 0 69.82759 0
DIP2C 607 -2.488481 -3.103617 0 63.97090 0
CDH4 382 -3.924828 -4.543817 0 54.65757 0
PCDHGA1 439 3.112168 3.772701 0 52.74243 0
SHANK2 491 -2.801796 -3.569806 0 51.46912 0
PCDHGA2 422 3.148747 3.774958 0 51.05571 0
ADARB2 475 -2.513880 -3.024767 0 48.13404 0
PCDHGA3 399 3.167597 3.797595 0 47.96551 0
PCDHGB1 380 3.171329 3.787355 0 45.80664 0
PRDM16 663 -2.083706 -2.804255 0 44.84268 0
PCDHGA4 362 3.232176 3.866314 0 44.36341 0
RASA3 361 -2.616746 -2.770470 0 42.87328 0
PCDHGB2 343 3.244599 3.910852 0 41.67703 0
PCDHGA5 326 3.246916 4.020431 0 39.31595 0
TRAPPC9 420 -2.567884 -3.060383 0 39.27994 0
RPS6KA2 355 -2.837456 -3.503466 0 38.86709 0
LMF1 262 -3.282306 -3.664374 0 38.85037 0
CACNA1C 290 -3.611877 -4.519151 0 37.80882 0 
sig <- subset(res2,`fdr(sc)`<0.05)

es <- sig[order(-abs(sig$median)),]

head(es,20) %>%
  kbl(caption = "Top effect size probes with GMEA") %>%
  kable_paper("hover", full_width = F)
Top effect size probes with GMEA
nprobes mean median p-value(sc) sig fdr(sc)
HOXA5 44 7.614178 7.779254 0.0e+00 12.944290 0.0000000
HOXD9 25 7.671189 7.424262 1.0e-07 7.224720 0.0015808
HOXD10 21 7.136925 7.366142 1.0e-06 6.020600 0.0248928
HOXD4 28 7.471295 7.340769 0.0e+00 8.127810 0.0001989
SFTA3 40 5.838050 7.107199 0.0e+00 9.089862 0.0000218
CCDC140 52 6.789033 6.995904 0.0e+00 9.442748 0.0000097
DLX6AS 66 5.719465 6.863361 0.0e+00 11.222806 0.0000002
OTX2 38 6.284445 6.759601 0.0e+00 10.837080 0.0000004
HOXA3 77 6.535123 6.643946 0.0e+00 13.600021 0.0000000
IRX1 24 6.318627 6.620583 1.0e-07 6.923690 0.0031515
SOX1 25 6.058274 6.532565 1.0e-07 7.224720 0.0015808
SIM1 73 4.350333 6.463233 0.0e+00 9.115628 0.0000206
MUC19 21 -6.207236 -6.458548 1.0e-06 6.020600 0.0248928
DRD4 20 5.673112 6.431521 1.9e-06 5.719570 0.0494747
OR9Q1 32 -5.048917 -6.389021 1.7e-06 5.761153 0.0449884
SIX6 23 6.097581 6.352698 2.0e-07 6.622660 0.0062783
PAX3 58 5.425500 6.180889 0.0e+00 9.883136 0.0000035
UNC13C 22 -5.536161 -6.162016 1.0e-06 6.020600 0.0248928
CNTNAP4 35 -5.366167 -6.158233 0.0e+00 9.389922 0.0000110
GHSR 21 5.450202 6.140068 1.0e-06 6.020600 0.0248928 
gmea_volc(res2)

gmea_boxplot(res)

res2_wg <- res2

Mitch whole gene

Methylation only.

genesets <- gmt_import("../ReactomePathways.gmt")

meth <- res2$sig * res2$median
meth <- as.data.frame(meth)
rownames(meth) <- rownames(res2)

mres <- mitch_calc(x=meth, genesets=genesets,priority="effect")
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
head(mres$enrichment_result,20) %>%
  kbl(caption = "Top differential pathways with GMEA+mitch") %>%
  kable_paper("hover", full_width = F)
Top differential pathways with GMEA+mitch
set setSize pANOVA s.dist p.adjustANOVA
323 Digestion of dietary carbohydrate 10 0.0001376 -0.6960481 0.0032635
291 Defective GALNT3 causes HFTC 18 0.0000005 -0.6854206 0.0000307
390 Endosomal/Vacuolar pathway 12 0.0000909 0.6523082 0.0024320
290 Defective GALNT12 causes CRCS1 18 0.0000030 -0.6357778 0.0001496
281 Dectin-2 family 28 0.0000000 -0.6082149 0.0000024
963 Presynaptic depolarization and calcium channel opening 12 0.0004076 -0.5892153 0.0082164
286 Defective C1GALT1C1 causes TNPS 19 0.0000097 -0.5860937 0.0003950
1404 Termination of O-glycan biosynthesis 25 0.0000007 -0.5734037 0.0000427
1523 cGMP effects 15 0.0002209 -0.5506972 0.0050422
321 Digestion 22 0.0000086 -0.5479529 0.0003588
56 Activation of the TFAP2 (AP-2) family of transcription factors 12 0.0011632 0.5413355 0.0196220
119 Beta defensins 32 0.0000001 -0.5396040 0.0000093
742 Metallothioneins bind metals 11 0.0021355 0.5345796 0.0299373
623 Interaction between L1 and Ankyrins 29 0.0000008 -0.5280599 0.0000506
1157 Response to metal ions 14 0.0006937 0.5234624 0.0125183
304 Defensins 40 0.0000000 -0.5225489 0.0000012
41 Activation of Ca-permeable Kainate Receptor 10 0.0054202 -0.5077611 0.0545502
660 Ionotropic activity of kainate receptors 10 0.0054202 -0.5077611 0.0545502
405 Expression and translocation of olfactory receptors 367 0.0000000 -0.5027613 0.0000000
1449 Transcriptional regulation of testis differentiation 12 0.0025831 0.5022832 0.0331762

Methylation and gene expression.

dge <- read.table("GSE158420_dge.tsv")
dgem <- mitch_import(dge,DEtype="deseq2")
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 16438
## Note: no. genes in output = 16438
## Note: estimated proportion of input genes in output = 1
int <- merge(meth,dgem,by=0)
rownames(int) <- int$Row.names
int[,1]=NULL
names(int) <- c("meth","rna")

plot(int,xlim=c(-100,100))
abline(h=0,lty=2,lwd=2,col="red")
abline(v=0,lty=2,lwd=2,col="red")

uu <- length(which(int$meth>0 & int$rna>0))
ud <- length(which(int$meth>0 & int$rna<0))
dd <- length(which(int$meth<0 & int$rna<0))
du <- length(which(int$meth<0 & int$rna>0))

uu
## [1] 1868
ud
## [1] 1830
dd
## [1] 5408
du
## [1] 6997
uu + dd
## [1] 7276
ud + du
## [1] 8827
int_res <- mitch_calc(int,genesets=genesets,priority="effect")
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
int_res_tbl <- int_res$enrichment_result

head(int_res_tbl, 20) %>%
  kbl(caption = "Integrative results with mitch") %>%
  kable_paper("hover", full_width = F)
Integrative results with mitch
set setSize pMANOVA s.meth s.rna p.meth p.rna s.dist SD p.adjustMANOVA
1444 Unwinding of DNA 12 0.0000001 -0.0769576 0.9410577 0.6444090 0.0000000 0.9441991 0.7198455 0.0000013
292 Defective pyroptosis 13 0.0000012 0.1541145 0.8160758 0.3360476 0.0000003 0.8305005 0.4680773 0.0000102
223 Condensation of Prometaphase Chromosomes 11 0.0000356 0.0859794 0.7803137 0.6215088 0.0000074 0.7850363 0.4909685 0.0001907
380 Endosomal/Vacuolar pathway 11 0.0000877 0.6214189 -0.4429725 0.0003583 0.0109627 0.7631422 0.7526384 0.0004173
266 DNA strand elongation 32 0.0000000 0.0388834 0.7372418 0.7035166 0.0000000 0.7382665 0.4938140 0.0000000
57 Activation of the pre-replicative complex 33 0.0000000 0.0792165 0.7311746 0.4310787 0.0000000 0.7354533 0.4610040 0.0000000
36 Activation of ATR in response to replication stress 37 0.0000000 0.0975196 0.7125324 0.3047927 0.0000000 0.7191748 0.4348797 0.0000000
743 Mucopolysaccharidoses 11 0.0002097 -0.0698107 -0.7107534 0.6885175 0.0000446 0.7141736 0.4532150 0.0008935
789 Negative regulation of activity of TFAP2 (AP-2) family transcription factors 10 0.0004486 0.4465239 0.5473317 0.0144850 0.0027254 0.7063679 0.0712819 0.0017389
312 Digestion 11 0.0002812 -0.7022406 0.0718025 0.0000550 0.6801159 0.7059019 0.5473312 0.0011588
1147 SLBP independent Processing of Histone Pre-mRNAs 10 0.0004681 0.3381640 0.6195716 0.0640843 0.0006917 0.7058498 0.1989852 0.0017986
911 Polo-like kinase mediated events 16 0.0000092 -0.0746526 0.6931554 0.6052131 0.0000016 0.6971638 0.5429222 0.0000587
1146 SLBP Dependent Processing of Replication-Dependent Histone Pre-mRNAs 11 0.0003048 0.3035069 0.6220198 0.0813548 0.0003537 0.6921164 0.2252226 0.0012391
304 Deposition of new CENPA-containing nucleosomes at the centromere 25 0.0000000 0.0629783 0.6885415 0.5857863 0.0000000 0.6914157 0.4423400 0.0000002
822 Nucleosome assembly 25 0.0000000 0.0629783 0.6885415 0.5857863 0.0000000 0.6914157 0.4423400 0.0000002
897 Phosphorylation of CD3 and TCR zeta chains 20 0.0000007 -0.3214207 -0.5963931 0.0128377 0.0000039 0.6774925 0.1944349 0.0000066
1118 Response to metal ions 11 0.0007378 0.6604221 -0.0530744 0.0001488 0.7605463 0.6625513 0.5045182 0.0026912
950 Purine ribonucleoside monophosphate biosynthesis 11 0.0007871 0.1079125 0.6458446 0.5354825 0.0002078 0.6547979 0.3803754 0.0028242
465 G1/S-Specific Transcription 29 0.0000000 0.0482919 0.6466199 0.6527067 0.0000000 0.6484207 0.4230817 0.0000002
100 Assembly of the ORC complex at the origin of replication 10 0.0017456 0.1161567 0.6365728 0.5247907 0.0004904 0.6470837 0.3679898 0.0056704 
plot(int_res_tbl$s.meth,int_res_tbl$s.rna,ylab="RNA",xlab="meth")

abline(h=0,lty=2,lwd=2,col="red")
abline(v=0,lty=2,lwd=2,col="red")

uu <- length(which(int_res_tbl$s.meth>0 & int_res_tbl$s.rna>0))
ud <- length(which(int_res_tbl$s.meth>0 & int_res_tbl$s.rna<0))
dd <- length(which(int_res_tbl$s.meth<0 & int_res_tbl$s.rna<0))
du <- length(which(int_res_tbl$s.meth<0 & int_res_tbl$s.rna>0))

uu
## [1] 561
ud
## [1] 387
dd
## [1] 363
du
## [1] 193
uu + dd
## [1] 924
ud + du
## [1] 580
mitch_report(int_res,outfile="int_wholegene.html",overwrite=TRUE)
## Note: overwriting existing report
## Dataset saved as " /tmp/RtmpsoLXD6/int_wholegene.rds ".
## 
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## processing file: mitch.Rmd
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## output file: /home/mdz/projects/gmea/GSE158433/mitch.knit.md
## /home/mdz/anaconda3/bin/pandoc +RTS -K512m -RTS /home/mdz/projects/gmea/GSE158433/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpsoLXD6/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpsoLXD6/rmarkdown-str185c460b3b05b.html
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## Output created: /tmp/RtmpsoLXD6/mitch_report.html
## [1] TRUE

GMEA promoter

dm <- read.table("GSE158422_limma.tsv",header=TRUE,sep="\t")
dm <- dm[grep("Promot",dm$Regulatory_Feature_Group),]
dm <- dm[,c("UCSC_RefGene_Name","t")]

hist(dm$t)

tic() ; res <- calc_sc(dm) ; toc() #32 cores 17.7
## 35.423 sec elapsed
res2 <- res[[2]]

head(res2,20) %>%
  kbl(caption = "Top significant promoters with GMEA") %>%
  kable_paper("hover", full_width = F)
Top significant promoters with GMEA
nprobes mean median p-value(sc) sig fdr(sc)
HLA-L 41 2.4110300 2.3465268 0.0e+00 8.406723 0.0000486
FAM50B 29 -2.5234187 -2.1470286 0.0e+00 8.127810 0.0000924
HSPA1A 28 2.2049376 2.4307218 0.0e+00 8.127810 0.0000924
HSPA1L 28 2.2049376 2.4307218 0.0e+00 8.127810 0.0000924
B3GALT4 47 -1.5467954 -1.1538482 1.0e-07 7.079905 0.0010315
RASA3 35 -3.0456647 -3.4398728 1.0e-07 6.926455 0.0014686
CDKN2A 24 2.3529306 2.8131261 1.0e-07 6.923690 0.0014778
DDAH2 37 1.6635355 1.4353228 2.0e-07 6.781510 0.0020501
PEG10 57 -1.7179144 -1.7382003 5.0e-07 6.289850 0.0063591
ZNF790 24 1.9644393 2.0392949 6.0e-07 6.224720 0.0073874
VIM 26 1.5162716 1.2826511 1.0e-06 6.007236 0.0121882
HLA-F 36 2.1191829 2.1087490 1.3e-06 5.886803 0.0160820
UBXN11 21 -5.2359289 -4.3282585 1.9e-06 5.719570 0.0236340
SGCE 54 -1.6125879 -1.7318336 2.3e-06 5.642350 0.0282307
NAP1L4 38 -2.4800770 -1.4257498 2.3e-06 5.638413 0.0284855
HDAC7 21 2.5663971 2.2103327 2.9e-06 5.543479 0.0354424
ZNF566 21 1.0990052 1.1697486 2.9e-06 5.543479 0.0354424
USP44 19 2.8259406 3.0093105 3.8e-06 5.418540 0.0472488
RAB1B 56 0.7151778 0.8184014 6.1e-06 5.215101 0.0754735
ACTR3C 19 -4.1113572 -3.7711569 7.6e-06 5.117510 0.0944824 
sig <- subset(res2,`fdr(sc)`<0.05)

es <- sig[order(-abs(sig$median)),]

head(es,20) %>%
  kbl(caption = "Top effect size probes with GMEA") %>%
  kable_paper("hover", full_width = F)
Top effect size probes with GMEA
nprobes mean median p-value(sc) sig fdr(sc)
UBXN11 21 -5.235929 -4.328258 1.9e-06 5.719570 0.0236340
RASA3 35 -3.045665 -3.439873 1.0e-07 6.926455 0.0014686
USP44 19 2.825941 3.009311 3.8e-06 5.418540 0.0472488
CDKN2A 24 2.352931 2.813126 1.0e-07 6.923690 0.0014778
HSPA1A 28 2.204938 2.430722 0.0e+00 8.127810 0.0000924
HSPA1L 28 2.204938 2.430722 0.0e+00 8.127810 0.0000924
HLA-L 41 2.411030 2.346527 0.0e+00 8.406723 0.0000486
HDAC7 21 2.566397 2.210333 2.9e-06 5.543479 0.0354424
FAM50B 29 -2.523419 -2.147029 0.0e+00 8.127810 0.0000924
HLA-F 36 2.119183 2.108749 1.3e-06 5.886803 0.0160820
ZNF790 24 1.964439 2.039295 6.0e-07 6.224720 0.0073874
PEG10 57 -1.717914 -1.738200 5.0e-07 6.289850 0.0063591
SGCE 54 -1.612588 -1.731834 2.3e-06 5.642350 0.0282307
DDAH2 37 1.663536 1.435323 2.0e-07 6.781510 0.0020501
NAP1L4 38 -2.480077 -1.425750 2.3e-06 5.638413 0.0284855
VIM 26 1.516272 1.282651 1.0e-06 6.007236 0.0121882
ZNF566 21 1.099005 1.169749 2.9e-06 5.543479 0.0354424
B3GALT4 47 -1.546795 -1.153848 1.0e-07 7.079905 0.0010315 
gmea_volc(res2)

gmea_boxplot(res)

res2_p <- res2

Mitch promoter

Methylation only

genesets <- gmt_import("../ReactomePathways.gmt")

pmeth <- res2$sig * res2$median
pmeth <- as.data.frame(pmeth)
rownames(pmeth) <- rownames(res2)

mres <- mitch_calc(x=pmeth, genesets=genesets,priority="effect")
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
head(mres$enrichment_result,20) %>%
  kbl(caption = "Top differential pathways with GMEA+mitch") %>%
  kable_paper("hover", full_width = F)
Top differential pathways with GMEA+mitch
set setSize pANOVA s.dist p.adjustANOVA
292 Endosomal/Vacuolar pathway 11 0.0006803 0.5910705 0.1197286
955 Sema4D induced cell migration and growth-cone collapse 12 0.0097336 0.4306688 0.4317041
189 Cytosolic iron-sulfur cluster assembly 10 0.0188150 0.4286855 0.4540767
1184 Unwinding of DNA 11 0.0232929 -0.3947341 0.4540767
557 Metabolism of folate and pterines 11 0.0250907 -0.3897603 0.4680982
1202 WNT5A-dependent internalization of FZD4 10 0.0333005 0.3884128 0.5470164
954 Sema4D in semaphorin signaling 15 0.0111563 0.3781886 0.4317041
389 Glutamate Neurotransmitter Release Cycle 12 0.0276025 0.3670002 0.4791187
720 Plasma lipoprotein remodeling 12 0.0311330 0.3590846 0.5254235
124 Caspase-mediated cleavage of cytoskeletal proteins 10 0.0535074 0.3523521 0.6495657
647 Nicotinamide salvaging 11 0.0482568 -0.3436748 0.6363422
789 RHOBTB1 GTPase cycle 19 0.0097303 0.3423730 0.4317041
256 Dopamine Neurotransmitter Release Cycle 12 0.0459637 0.3324927 0.6363422
944 SUMOylation of immune response proteins 11 0.0560471 0.3324652 0.6703888
609 NF-kB activation through FADD/RIP-1 pathway mediated by caspase-8 and -10 12 0.0499067 -0.3266685 0.6363422
781 RHO GTPases Activate ROCKs 13 0.0423100 0.3250202 0.6161131
859 Regulation of IFNG signaling 13 0.0435987 -0.3230148 0.6245774
999 Signaling by FLT3 ITD and TKD mutants 13 0.0496463 0.3142236 0.6363422
196 DCC mediated attractive signaling 11 0.0712132 0.3139269 0.6948960
1085 TNF receptor superfamily (TNFSF) members mediating non-canonical NF-kB pathway 10 0.0881687 -0.3111757 0.6972251

Methylation and gene expression.

#dge <- read.table("GSE158420_dge.tsv")
#dgem <- mitch_import(dge,DEtype="deseq2")

int <- merge(pmeth,dgem,by=0)
rownames(int) <- int$Row.names
int[,1]=NULL
names(int) <- c("meth","rna")

plot(int)
abline(h=0,lty=2,lwd=2,col="red")
abline(v=0,lty=2,lwd=2,col="red")

uu <- length(which(int$meth>0 & int$rna>0))
ud <- length(which(int$meth>0 & int$rna<0))
dd <- length(which(int$meth<0 & int$rna<0))
du <- length(which(int$meth<0 & int$rna>0))

uu
## [1] 1998
ud
## [1] 1971
dd
## [1] 2072
du
## [1] 2846
uu + dd
## [1] 4070
ud + du
## [1] 4817
int_res <- mitch_calc(int,genesets=genesets,priority="effect")
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
int_res_tbl <- int_res$enrichment_result

head(int_res_tbl,20) %>%
  kbl(caption = "Integrative results with mitch") %>%
  kable_paper("hover", full_width = F)
Integrative results with mitch
set setSize pMANOVA s.meth s.rna p.meth p.rna s.dist SD p.adjustMANOVA
1180 Unwinding of DNA 11 0.0000001 -0.3850519 0.9462476 0.0269942 0.0000001 1.0215917 0.9413709 0.0000008
220 Defective pyroptosis 12 0.0000013 0.1509927 0.8366708 0.3651099 0.0000005 0.8501863 0.4848476 0.0000099
166 Condensation of Prometaphase Chromosomes 10 0.0000375 -0.2887334 0.7948519 0.1138347 0.0000134 0.8456693 0.7662105 0.0002056
41 Activation of the pre-replicative complex 31 0.0000000 0.0468830 0.8044307 0.6516040 0.0000000 0.8057958 0.5356671 0.0000000
205 DNA strand elongation 30 0.0000000 -0.0299284 0.7516274 0.7767403 0.0000000 0.7522230 0.5526434 0.0000000
291 Endosomal/Vacuolar pathway 10 0.0004821 0.5559755 -0.4952196 0.0023252 0.0067039 0.7445477 0.7433072 0.0020701
595 Mucopolysaccharidoses 11 0.0000830 -0.0981250 -0.7366617 0.5730745 0.0000233 0.7431682 0.4515137 0.0004263
28 Activation of ATR in response to replication stress 36 0.0000000 -0.0967727 0.7348992 0.3153079 0.0000000 0.7412434 0.5880808 0.0000000
725 Polo-like kinase mediated events 15 0.0000118 -0.2307409 0.6897701 0.1218162 0.0000037 0.7273404 0.6508996 0.0000725
230 Deposition of new CENPA-containing nucleosomes at the centromere 22 0.0000000 -0.0093066 0.7200115 0.9397825 0.0000000 0.7200717 0.5157058 0.0000004
663 Nucleosome assembly 22 0.0000000 -0.0093066 0.7200115 0.9397825 0.0000000 0.7200717 0.5157058 0.0000004
357 G1/S-Specific Transcription 28 0.0000000 -0.1106843 0.6882273 0.3109191 0.0000000 0.6970709 0.5649158 0.0000000
146 Chk1/Chk2(Cds1) mediated inactivation of Cyclin B:Cdk1 complex 13 0.0001954 -0.1026165 0.6606016 0.5217834 0.0000372 0.6685242 0.5396767 0.0009123
167 Condensation of Prophase Chromosomes 13 0.0002400 -0.1507101 0.6473501 0.3467860 0.0000532 0.6646621 0.5643138 0.0010956
67 Assembly of the ORC complex at the origin of replication 10 0.0014213 -0.0351277 0.6608989 0.8474622 0.0002960 0.6618318 0.4921651 0.0052258
515 Lagging Strand Synthesis 19 0.0000020 0.1759049 0.6371698 0.1844451 0.0000015 0.6610052 0.3261635 0.0000144
749 Processive synthesis on the lagging strand 14 0.0000573 0.2377084 0.6156317 0.1235737 0.0000666 0.6599300 0.2672321 0.0003020
759 Purine ribonucleoside monophosphate biosynthesis 10 0.0015505 0.0040654 0.6539326 0.9822391 0.0003428 0.6539452 0.4595255 0.0056498
952 Sema4D induced cell migration and growth-cone collapse 12 0.0009661 0.4285775 -0.4861565 0.0101395 0.0035522 0.6480948 0.6468146 0.0036730
734 Postmitotic nuclear pore complex (NPC) reformation 26 0.0000001 -0.1175647 0.6370433 0.2996239 0.0000000 0.6478006 0.5335884 0.0000012 
int_res_tbl <- int_res_tbl[order(-abs(int_res_tbl$s.dist)),]

head(int_res_tbl,20) %>%
  kbl(caption = "Integrative results with mitch") %>%
  kable_paper("hover", full_width = F)
Integrative results with mitch
set setSize pMANOVA s.meth s.rna p.meth p.rna s.dist SD p.adjustMANOVA
1180 Unwinding of DNA 11 0.0000001 -0.3850519 0.9462476 0.0269942 0.0000001 1.0215917 0.9413709 0.0000008
220 Defective pyroptosis 12 0.0000013 0.1509927 0.8366708 0.3651099 0.0000005 0.8501863 0.4848476 0.0000099
166 Condensation of Prometaphase Chromosomes 10 0.0000375 -0.2887334 0.7948519 0.1138347 0.0000134 0.8456693 0.7662105 0.0002056
41 Activation of the pre-replicative complex 31 0.0000000 0.0468830 0.8044307 0.6516040 0.0000000 0.8057958 0.5356671 0.0000000
205 DNA strand elongation 30 0.0000000 -0.0299284 0.7516274 0.7767403 0.0000000 0.7522230 0.5526434 0.0000000
291 Endosomal/Vacuolar pathway 10 0.0004821 0.5559755 -0.4952196 0.0023252 0.0067039 0.7445477 0.7433072 0.0020701
595 Mucopolysaccharidoses 11 0.0000830 -0.0981250 -0.7366617 0.5730745 0.0000233 0.7431682 0.4515137 0.0004263
28 Activation of ATR in response to replication stress 36 0.0000000 -0.0967727 0.7348992 0.3153079 0.0000000 0.7412434 0.5880808 0.0000000
725 Polo-like kinase mediated events 15 0.0000118 -0.2307409 0.6897701 0.1218162 0.0000037 0.7273404 0.6508996 0.0000725
230 Deposition of new CENPA-containing nucleosomes at the centromere 22 0.0000000 -0.0093066 0.7200115 0.9397825 0.0000000 0.7200717 0.5157058 0.0000004
663 Nucleosome assembly 22 0.0000000 -0.0093066 0.7200115 0.9397825 0.0000000 0.7200717 0.5157058 0.0000004
357 G1/S-Specific Transcription 28 0.0000000 -0.1106843 0.6882273 0.3109191 0.0000000 0.6970709 0.5649158 0.0000000
146 Chk1/Chk2(Cds1) mediated inactivation of Cyclin B:Cdk1 complex 13 0.0001954 -0.1026165 0.6606016 0.5217834 0.0000372 0.6685242 0.5396767 0.0009123
167 Condensation of Prophase Chromosomes 13 0.0002400 -0.1507101 0.6473501 0.3467860 0.0000532 0.6646621 0.5643138 0.0010956
67 Assembly of the ORC complex at the origin of replication 10 0.0014213 -0.0351277 0.6608989 0.8474622 0.0002960 0.6618318 0.4921651 0.0052258
515 Lagging Strand Synthesis 19 0.0000020 0.1759049 0.6371698 0.1844451 0.0000015 0.6610052 0.3261635 0.0000144
749 Processive synthesis on the lagging strand 14 0.0000573 0.2377084 0.6156317 0.1235737 0.0000666 0.6599300 0.2672321 0.0003020
759 Purine ribonucleoside monophosphate biosynthesis 10 0.0015505 0.0040654 0.6539326 0.9822391 0.0003428 0.6539452 0.4595255 0.0056498
952 Sema4D induced cell migration and growth-cone collapse 12 0.0009661 0.4285775 -0.4861565 0.0101395 0.0035522 0.6480948 0.6468146 0.0036730
734 Postmitotic nuclear pore complex (NPC) reformation 26 0.0000001 -0.1175647 0.6370433 0.2996239 0.0000000 0.6478006 0.5335884 0.0000012 
plot(int_res_tbl$s.meth,int_res_tbl$s.rna,ylab="RNA",xlab="meth")

abline(h=0,lty=2,lwd=2,col="red")
abline(v=0,lty=2,lwd=2,col="red")

uu <- length(which(int_res_tbl$s.meth>0 & int_res_tbl$s.rna>0))
ud <- length(which(int_res_tbl$s.meth>0 & int_res_tbl$s.rna<0))
dd <- length(which(int_res_tbl$s.meth<0 & int_res_tbl$s.rna<0))
du <- length(which(int_res_tbl$s.meth<0 & int_res_tbl$s.rna>0))

uu
## [1] 269
ud
## [1] 342
dd
## [1] 229
du
## [1] 388
uu + dd
## [1] 498
ud + du
## [1] 730
mitch_report(int_res,outfile="int_promoter.html",overwrite=TRUE)
## Note: overwriting existing report
## Dataset saved as " /tmp/RtmpsoLXD6/int_promoter.rds ".
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##  $ echo      : logi FALSE
##  $ fig.height: num 7
##  $ fig.width : num 7
##  $ fig.show  : chr "all"
##  $ message   : logi FALSE
## 
## 
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  |.................................................                     |  71%
##   ordinary text without R code
## 
## 
  |                                                                            
  |...................................................                   |  74%
## label: results_table (with options) 
## List of 2
##  $ results: chr "asis"
##  $ echo   : logi FALSE
## 
## 
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  |......................................................                |  76%
##   ordinary text without R code
## 
## 
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  |........................................................              |  79%
## label: results_table_complete (with options) 
## List of 2
##  $ results: chr "asis"
##  $ echo   : logi FALSE
## 
## 
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  |..........................................................            |  82%
##   ordinary text without R code
## 
## 
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  |............................................................          |  85%
## label: detailed_geneset_reports1d (with options) 
## List of 7
##  $ results   : chr "asis"
##  $ echo      : logi FALSE
##  $ fig.height: num 6
##  $ fig.width : num 6
##  $ out.width : chr "80%"
##  $ comment   : logi NA
##  $ message   : logi FALSE
## 
## 
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  |..............................................................        |  88%
##   ordinary text without R code
## 
## 
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## label: detailed_geneset_reports2d (with options) 
## List of 7
##  $ results   : chr "asis"
##  $ echo      : logi FALSE
##  $ fig.height: num 5
##  $ fig.width : num 6
##  $ out.width : chr "80%"
##  $ comment   : logi NA
##  $ message   : logi FALSE
## 
  |                                                                            
  |..................................................................    |  94%
##   ordinary text without R code
## 
## 
  |                                                                            
  |....................................................................  |  97%
## label: session_info (with options) 
## List of 3
##  $ include: logi TRUE
##  $ echo   : logi TRUE
##  $ results: chr "markup"
## 
## 
  |                                                                            
  |......................................................................| 100%
##   ordinary text without R code
## output file: /home/mdz/projects/gmea/GSE158433/mitch.knit.md
## /home/mdz/anaconda3/bin/pandoc +RTS -K512m -RTS /home/mdz/projects/gmea/GSE158433/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpsoLXD6/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpsoLXD6/rmarkdown-str185c43ab60649.html
## 
## Output created: /tmp/RtmpsoLXD6/mitch_report.html
## [1] TRUE

Session Information

sessionInfo()
## R version 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## 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       
## 
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] pkgload_1.2.4                                      
##  [2] GGally_2.1.2                                       
##  [3] ggplot2_3.3.6                                      
##  [4] gtools_3.9.2                                       
##  [5] tibble_3.1.7                                       
##  [6] dplyr_1.0.9                                        
##  [7] echarts4r_0.4.3                                    
##  [8] kableExtra_1.3.4                                   
##  [9] mitch_1.8.0                                        
## [10] tictoc_1.0.1                                       
## [11] ENmix_1.32.0                                       
## [12] doParallel_1.0.17                                  
## [13] qqman_0.1.8                                        
## [14] RCircos_1.2.2                                      
## [15] beeswarm_0.4.0                                     
## [16] forestplot_2.0.1                                   
## [17] checkmate_2.1.0                                    
## [18] magrittr_2.0.3                                     
## [19] reshape2_1.4.4                                     
## [20] gplots_3.1.3                                       
## [21] eulerr_6.1.1                                       
## [22] GEOquery_2.64.2                                    
## [23] RColorBrewer_1.1-3                                 
## [24] IlluminaHumanMethylation450kmanifest_0.4.0         
## [25] topconfects_1.12.0                                 
## [26] DMRcatedata_2.14.0                                 
## [27] ExperimentHub_2.4.0                                
## [28] AnnotationHub_3.4.0                                
## [29] BiocFileCache_2.4.0                                
## [30] dbplyr_2.1.1                                       
## [31] DMRcate_2.10.0                                     
## [32] limma_3.52.1                                       
## [33] missMethyl_1.30.0                                  
## [34] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [35] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1 
## [36] minfi_1.42.0                                       
## [37] bumphunter_1.38.0                                  
## [38] locfit_1.5-9.5                                     
## [39] iterators_1.0.14                                   
## [40] foreach_1.5.2                                      
## [41] Biostrings_2.64.0                                  
## [42] XVector_0.36.0                                     
## [43] SummarizedExperiment_1.26.1                        
## [44] Biobase_2.56.0                                     
## [45] MatrixGenerics_1.8.0                               
## [46] matrixStats_0.62.0                                 
## [47] GenomicRanges_1.48.0                               
## [48] GenomeInfoDb_1.32.2                                
## [49] IRanges_2.30.0                                     
## [50] S4Vectors_0.34.0                                   
## [51] BiocGenerics_0.42.0                                
## [52] R.utils_2.11.0                                     
## [53] R.oo_1.24.0                                        
## [54] R.methodsS3_1.8.1                                  
## [55] plyr_1.8.7                                         
## 
## loaded via a namespace (and not attached):
##   [1] rappdirs_0.3.3                rtracklayer_1.56.0           
##   [3] tidyr_1.2.0                   bit64_4.0.5                  
##   [5] knitr_1.39                    DelayedArray_0.22.0          
##   [7] data.table_1.14.2             rpart_4.1.16                 
##   [9] KEGGREST_1.36.0               RCurl_1.98-1.6               
##  [11] AnnotationFilter_1.20.0       generics_0.1.2               
##  [13] GenomicFeatures_1.48.1        preprocessCore_1.58.0        
##  [15] RSQLite_2.2.14                bit_4.0.4                    
##  [17] tzdb_0.3.0                    webshot_0.5.3                
##  [19] xml2_1.3.3                    httpuv_1.6.5                 
##  [21] assertthat_0.2.1              xfun_0.31                    
##  [23] jquerylib_0.1.4               hms_1.1.1                    
##  [25] evaluate_0.15                 promises_1.2.0.1             
##  [27] fansi_1.0.3                   restfulr_0.0.13              
##  [29] scrime_1.3.5                  progress_1.2.2               
##  [31] caTools_1.18.2                readxl_1.4.0                 
##  [33] DBI_1.1.2                     geneplotter_1.74.0           
##  [35] htmlwidgets_1.5.4             reshape_0.8.9                
##  [37] purrr_0.3.4                   ellipsis_0.3.2               
##  [39] backports_1.4.1               permute_0.9-7                
##  [41] calibrate_1.7.7               annotate_1.74.0              
##  [43] biomaRt_2.52.0                sparseMatrixStats_1.8.0      
##  [45] vctrs_0.4.1                   ensembldb_2.20.1             
##  [47] withr_2.5.0                   cachem_1.0.6                 
##  [49] Gviz_1.40.1                   BSgenome_1.64.0              
##  [51] GenomicAlignments_1.32.0      prettyunits_1.1.1            
##  [53] mclust_5.4.9                  svglite_2.1.0                
##  [55] cluster_2.1.3                 RPMM_1.25                    
##  [57] lazyeval_0.2.2                crayon_1.5.1                 
##  [59] genefilter_1.78.0             labeling_0.4.2               
##  [61] edgeR_3.38.1                  pkgconfig_2.0.3              
##  [63] nlme_3.1-157                  ProtGenerics_1.28.0          
##  [65] nnet_7.3-17                   rlang_1.0.2                  
##  [67] lifecycle_1.0.1               filelock_1.0.2               
##  [69] dichromat_2.0-0.1             rprojroot_2.0.3              
##  [71] cellranger_1.1.0              rngtools_1.5.2               
##  [73] base64_2.0                    Matrix_1.4-1                 
##  [75] Rhdf5lib_1.18.2               base64enc_0.1-3              
##  [77] viridisLite_0.4.0             png_0.1-7                    
##  [79] rjson_0.2.21                  bitops_1.0-7                 
##  [81] KernSmooth_2.23-20            rhdf5filters_1.8.0           
##  [83] blob_1.2.3                    DelayedMatrixStats_1.18.0    
##  [85] doRNG_1.8.2                   stringr_1.4.0                
##  [87] nor1mix_1.3-0                 readr_2.1.2                  
##  [89] jpeg_0.1-9                    scales_1.2.0                 
##  [91] memoise_2.0.1                 zlibbioc_1.42.0              
##  [93] compiler_4.2.0                BiocIO_1.6.0                 
##  [95] illuminaio_0.38.0             Rsamtools_2.12.0             
##  [97] cli_3.3.0                     DSS_2.44.0                   
##  [99] htmlTable_2.4.0               Formula_1.2-4                
## [101] MASS_7.3-57                   tidyselect_1.1.2             
## [103] stringi_1.7.6                 highr_0.9                    
## [105] yaml_2.3.5                    askpass_1.1                  
## [107] latticeExtra_0.6-29           sass_0.4.1                   
## [109] VariantAnnotation_1.42.1      tools_4.2.0                  
## [111] rstudioapi_0.13               foreign_0.8-82               
## [113] bsseq_1.32.0                  gridExtra_2.3                
## [115] farver_2.1.0                  digest_0.6.29                
## [117] BiocManager_1.30.17           shiny_1.7.1                  
## [119] quadprog_1.5-8                Rcpp_1.0.8.3                 
## [121] siggenes_1.70.0               BiocVersion_3.15.2           
## [123] later_1.3.0                   org.Hs.eg.db_3.15.0          
## [125] httr_1.4.3                    AnnotationDbi_1.58.0         
## [127] biovizBase_1.44.0             colorspace_2.0-3             
## [129] brio_1.1.3                    rvest_1.0.2                  
## [131] XML_3.99-0.9                  splines_4.2.0                
## [133] statmod_1.4.36                multtest_2.52.0              
## [135] systemfonts_1.0.4             xtable_1.8-4                 
## [137] jsonlite_1.8.0                dynamicTreeCut_1.63-1        
## [139] testthat_3.1.4                R6_2.5.1                     
## [141] Hmisc_4.7-0                   pillar_1.7.0                 
## [143] htmltools_0.5.2               mime_0.12                    
## [145] glue_1.6.2                    fastmap_1.1.0                
## [147] BiocParallel_1.30.2           interactiveDisplayBase_1.34.0
## [149] beanplot_1.3.1                codetools_0.2-18             
## [151] utf8_1.2.2                    bslib_0.3.1                  
## [153] lattice_0.20-45               curl_4.3.2                   
## [155] openssl_2.0.1                 survival_3.3-1               
## [157] rmarkdown_2.14                desc_1.4.1                   
## [159] munsell_0.5.0                 rhdf5_2.40.0                 
## [161] GenomeInfoDbData_1.2.8        HDF5Array_1.24.0             
## [163] impute_1.70.0                 gtable_0.3.0