GSE158433: GMEA of DNA methylation differences in lung squamous cell carcinoma

Introduction

The purpose of this analysis is to explore different enrichment approaches to the cancer methylation data: Infinium EPIC array (GSE158422) data for matched control and cancer tissues.

We will specifically be testing the GMEA approach versus existing approaches:

1-way Wilcox text
1-way t-test
FGSEA test
roast test

After each of these tests, downstream GSEA can be conducted.

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")
  library("fgsea")
})

source("../meth_functions.R")
CORES=8

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)

Gene probe sets

gp <- myann[,"UCSC_RefGene_Name",drop=FALSE]
gp2 <- strsplit(gp$UCSC_RefGene_Name,";")
names(gp2) <- rownames(gp)
sets <- split(rep(names(gp2), lengths(gp2)), unlist(gp2))

hist(unlist(lapply(sets,length)),xlim=c(0,200),breaks=500,xlab="set size",main="probes per gene")

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	B
5777	cg00159780	REXO1L2P;REXO1L1		chr8:86573421-86575248	Island	-1.6810156	1.1199758	-13.41989	38.61314
61184	cg01772014	C20orf160	Unclassified_Cell_type_specific	chr20:30606492-30607174	N_Shore	-1.2581347	-2.1384838	-13.36031	38.38467
468211	cg14205001	NOTCH1		chr9:139405089-139405292	S_Shore	1.0609528	1.0240980	13.34777	38.33651
221392	cg06569947	DCUN1D2			OpenSea	1.6933468	1.5977978	13.21072	37.80891
577564	cg17655624	ZNF385A;ZNF385A	Unclassified_Cell_type_specific	chr12:54784900-54785238	S_Shore	-1.6062843	-2.4260117	-13.17366	37.66580
444856	cg13531667	MCC		chr5:112823256-112824304	S_Shore	-1.9451786	-2.4725269	-13.13667	37.52278
669100	cg20979986	SERINC5;SERINC5;SERINC5;SERINC5;SERINC5	Unclassified_Cell_type_specific		OpenSea	-1.9389375	-1.5165570	-13.05957	37.22412
657981	cg20568402	DCUN1D2			OpenSea	1.6831260	1.3499641	13.03319	37.12174
60887	cg01762663	DOCK9;DOCK9			OpenSea	1.4190052	1.6213375	12.94365	36.77360
760860	cg24334634	DCUN1D2			OpenSea	1.8952109	1.3472386	12.89559	36.58628
791527	cg25422938	RPA3;UMAD1;UMAD1;UMAD1			OpenSea	2.0931417	0.9054807	12.88130	36.53053
102318	cg02973960	CAMKK2;CAMKK2;CAMKK2;CAMKK2;CAMKK2;CAMKK2;CAMKK2;CAMKK2			OpenSea	1.8653298	0.9885988	12.82480	36.30981
159551	cg04690379	SPTLC2			OpenSea	1.4808359	1.6632219	12.81647	36.27725
401316	cg12131862	ATP2B4;ATP2B4			OpenSea	1.3214447	1.1562250	12.73097	35.94239
549143	cg16732616	DMRTA2		chr1:50884228-50891471	Island	3.0282534	-2.2765252	12.67359	35.71713
560139	cg17094249		Unclassified_Cell_type_specific		OpenSea	-1.6984142	-1.5599415	-12.62766	35.53653
91439	cg02655980	GALK2;GALK2;GALK2;MIR4716;GALK2			OpenSea	1.1562534	1.9223113	12.59459	35.40631
15682	cg00446235	TSTD1;TSTD1;TSTD1	Promoter_Associated	chr1:161008377-161008830	Island	-1.4800380	-2.5291407	-12.57768	35.33968
440784	cg13417058	TKT;TKT;TKT;TKT		chr3:53289533-53290213	N_Shore	-1.2138572	-3.1919784	-12.56018	35.27064
576795	cg17627973	SRPK2;SRPK2		chr7:104885049-104885400	Island	1.2313437	1.4827501	12.55575	35.25318
397225	cg12003230	C21orf84			OpenSea	1.2476557	1.2625415	12.51287	35.08392
736989	cg23480296	DAB2IP			OpenSea	1.5228320	2.0541757	12.49776	35.02422
667781	cg20934096	C7orf50;C7orf50;C7orf50		chr7:1119980-1120248	N_Shelf	1.8073593	1.0858697	12.48786	34.98510
74972	cg02174232	APEH	Promoter_Associated	chr3:49710968-49712279	Island	-1.3008066	-2.2997937	-12.47090	34.91804
224178	cg06651376	SMYD4	Unclassified_Cell_type_specific		OpenSea	1.7518820	2.1327280	12.39961	34.63569
48818	cg01399319	CCNL2;CCNL2;CCNL2;CCNL2		chr1:1322644-1322924	S_Shelf	1.5152093	1.6887531	12.39857	34.63157
836007	cg26993251	MACROD1	Promoter_Associated	chr11:63932990-63934070	Island	-1.8619005	-2.5408778	-12.38902	34.59373
317907	cg09496762		Promoter_Associated	chr20:42285961-42286535	Island	-1.3804253	-3.4226436	-12.38487	34.57725
141548	cg04147064	TBC1D22A;TBC1D22A;TBC1D22A;TBC1D22A;TBC1D22A		chr22:47558272-47558513	S_Shelf	1.6643797	1.1404515	12.38173	34.56481
689315	cg21722612	TPCN1;TPCN1;TPCN1			OpenSea	1.7694572	1.3631921	12.37873	34.55291
303960	cg09067993	SUCLG2;SUCLG2			OpenSea	1.6770912	1.0487424	12.34700	34.42698
327281	cg09792881	DMRTA2		chr1:50884228-50891471	Island	2.6518400	-2.3846188	12.30475	34.25906
811041	cg26134703	FAM49B;FAM49B;FAM49B;FAM49B;FAM49B;FAM49B;FAM49B;FAM49B			OpenSea	1.1456692	2.1751297	12.30127	34.24524
536592	cg16350950	ARFGEF2;ARFGEF2			OpenSea	1.5891250	1.7433091	12.28112	34.16508
577091	cg17639795			chr19:2950359-2950962	N_Shore	0.9854179	1.4310298	12.25750	34.07103
500102	cg15169038	PLEKHG1			OpenSea	1.2350751	1.2815770	12.25413	34.05762
833500	cg26908825	GMDS			OpenSea	1.5139160	0.9404246	12.24443	34.01897
515979	cg15698065	DCUN1D2			OpenSea	1.1600645	1.1265920	12.24075	34.00432
281220	cg08365845	GNAI2;GNAI2	Promoter_Associated	chr3:50264402-50265101	S_Shore	-1.1317946	-3.7762019	-12.22984	33.96084
27631	cg00781839	DCUN1D2			OpenSea	1.5928682	2.1251831	12.19970	33.84066
663755	cg20778786				OpenSea	-2.2891897	-1.4266297	-12.19677	33.82896
601050	cg18483766	PPP4R1;PPP4R1;PPP4R1			OpenSea	1.3919045	1.7771650	12.17365	33.73668
690514	cg21768042	ARAP3	Unclassified		OpenSea	0.9038892	1.1291209	12.17317	33.73475
66144	cg01915688	PPP1R12A;PPP1R12A;PPP1R12A;PPP1R12A;PPP1R12A			OpenSea	1.5092716	2.2025612	12.13652	33.58836
134752	cg03946762	ZNF385A;ZNF385A;ZNF385A;ZNF385A	Unclassified_Cell_type_specific	chr12:54784900-54785238	Island	-1.7206885	-2.0731893	-12.13557	33.58454
228200	cg06767326	DCUN1D2			OpenSea	1.8092946	1.9834216	12.13503	33.58240
693283	cg21870038	RFFL;RFFL;RFFL	Promoter_Associated		OpenSea	-1.8490646	-3.1666138	-12.11240	33.49190
351767	cg10546600	BRD7;BRD7			OpenSea	1.4049959	1.7498277	12.11131	33.48755
524497	cg15970145	LARGE;LARGE			OpenSea	-1.8418429	-0.8440018	-12.09043	33.40397
54185	cg01558390	PIP4K2A			OpenSea	1.6277425	0.6732985	12.08821	33.39509

rownames(dm) <- dm[,1]
dm <- dm[,c("UCSC_RefGene_Name","t")]

GMEA whole gene - wilcox test

hist(dm$t)

tic() ; res <- calc_sc(dm) ; toc() #32 cores 95.5

## 116.156 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	sig
PTPRN2	1474	-3.813234	-4.397006	196.48861
MAD1L1	817	-2.042400	-2.277810	70.11979
TNXB	529	-3.156354	-3.605676	69.75063
DIP2C	607	-2.479070	-3.095174	63.78267
CDH4	382	-3.919010	-4.543389	54.63858
PCDHGA1	439	3.122674	3.776895	52.91420
SHANK2	491	-2.794116	-3.589533	51.36588
PCDHGA2	422	3.159710	3.783492	51.21125
PCDHGA3	399	3.179048	3.797327	48.12942
ADARB2	475	-2.507251	-3.021943	48.00564
PCDHGB1	380	3.182828	3.785979	45.95262
PRDM16	663	-2.082507	-2.808744	44.79882
PCDHGA4	362	3.243741	3.881572	44.50243
RASA3	361	-2.612493	-2.761520	42.79716
PCDHGB2	343	3.255906	3.949639	41.81006
PCDHGA5	326	3.258105	4.026341	39.45533
TRAPPC9	420	-2.548773	-3.071687	38.99707
LMF1	262	-3.279107	-3.705261	38.85506
RPS6KA2	355	-2.828566	-3.489451	38.78985
CACNA1C	290	-3.605957	-4.538620	37.76518

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	7.623040	7.780885	0.0e+00	12.944290	0.0000000
HOXD10	21	7.142220	7.426734	1.0e-06	6.020600	0.0249023
HOXD9	25	7.665227	7.412387	1.0e-07	7.224720	0.0015815
HOXD4	28	7.471039	7.349427	0.0e+00	8.127810	0.0001990
SFTA3	40	5.835427	7.108292	0.0e+00	9.089862	0.0000218
CCDC140	52	6.792560	6.995631	0.0e+00	9.442748	0.0000097
DLX6AS	66	5.720625	6.862673	0.0e+00	11.222806	0.0000002
OTX2	38	6.288114	6.766082	0.0e+00	10.837080	0.0000004
HOXA3	77	6.546720	6.671353	0.0e+00	13.600021	0.0000000
IRX1	24	6.319978	6.616094	1.0e-07	6.923690	0.0031527
SOX1	25	6.058103	6.516175	1.0e-07	7.224720	0.0015815
SIM1	73	4.353270	6.467176	0.0e+00	9.145769	0.0000192
DRD4	20	5.670473	6.440472	1.9e-06	5.719570	0.0495014
MUC19	21	-6.200077	-6.435404	1.0e-06	6.020600	0.0249023
OR9Q1	32	-5.049954	-6.398015	1.7e-06	5.761153	0.0450092
SIX6	23	6.101058	6.375368	2.0e-07	6.622660	0.0062802
PAX3	58	5.432323	6.178637	0.0e+00	9.927403	0.0000032
UNC13C	22	-5.520481	-6.155292	1.0e-06	6.020600	0.0249023
CNTNAP4	35	-5.354329	-6.137429	0.0e+00	9.389922	0.0000110
GHSR	21	5.456063	6.134691	1.0e-06	6.020600	0.0249023

gmea_volc(res2)

gmea_boxplot(res)

res2_wg <- res2

Investigate how probe number influences significance.

plot(res2$nprobes,res2$sig,log="x",ylim=c(0,50),pch=19,cex=0.6)
points(sig$nprobes,sig$sig,col="red",pch=19,cex=0.62)
MIN = min(sig$nprobes)
LEFT = nrow(subset(res2,nprobes<MIN))
RIGHT = nrow(subset(res2,nprobes>MIN))
SIG = nrow(sig)
TOT = nrow(res2)
HEADER <- paste(TOT, "genes in total.", SIG, "with FDR<0.05.",
  RIGHT, "well covered and", LEFT, "poorly covered")
mtext(HEADER)
abline(v=MIN,lty=2)

GMEA whole gene - t.test

dm <- read.table("GSE158422_limma.tsv",header=TRUE,sep="\t")
rownames(dm) <- dm[,1]
dm <- dm[,c("UCSC_RefGene_Name","t")]

hist(dm$t)

tic()
res <- calc_sc2(dm)
toc()

## 254.451 sec elapsed

res2 <- res[[2]]

head(res2,20) %>%
  kbl(caption = "Top significant genes with GMEA-t") %>%
  kable_paper("hover", full_width = F)

Top significant genes with GMEA-t
	nprobes	mean	median	sig
PTPRN2	1474	-3.813234	-4.397006	Inf
TNXB	529	-3.156354	-3.605676	113.00665
CDH4	382	-3.919010	-4.543389	99.12338
DIP2C	607	-2.479070	-3.095174	86.68677
MAD1L1	817	-2.042400	-2.277810	81.22935
PCDHGA1	439	3.122674	3.776895	75.90437
PCDHGA2	422	3.159710	3.783492	73.92040
SHANK2	491	-2.794116	-3.589533	73.17547
SNORD115-15	81	-6.138454	-6.052379	70.09110
SNORD115-21	81	-6.138454	-6.052379	70.09110
PCDHGA3	399	3.179048	3.797327	69.19488
LMF1	262	-3.279107	-3.705261	67.83114
PCDHGB1	380	3.182828	3.785979	66.11569
PCDHGA4	362	3.243741	3.881572	64.49388
MYT1L	245	-3.956712	-4.423715	64.37781
ADARB2	475	-2.507251	-3.021943	62.24055
PCDHGB2	343	3.255906	3.949639	60.22098
RASA3	361	-2.612493	-2.761520	59.68705
PCDHGA5	326	3.258105	4.026341	56.53669
CACNA1C	290	-3.605957	-4.538620	56.17918

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)
MIR10B	13	7.587783	8.134873	0.0e+00	8.007009	0.0002488
HOXD12	14	7.862712	7.922158	0.0e+00	12.765511	0.0000000
HOXA5	44	7.623040	7.780885	0.0e+00	40.923039	0.0000000
LINC01246	7	-7.478710	-7.729335	1.0e-07	6.973414	0.0026483
PFN3	10	7.723714	7.630209	0.0e+00	7.650953	0.0005621
MIR196A1	7	7.437659	7.478137	7.0e-07	6.160796	0.0169168
C17orf112	6	-7.509326	-7.471408	1.5e-06	5.816728	0.0370409
MIR205	7	-6.909420	-7.466330	2.0e-06	5.704269	0.0478546
HOXD10	21	7.142220	7.426734	0.0e+00	12.079764	0.0000000
HOXD9	25	7.665227	7.412387	0.0e+00	22.120455	0.0000000
HOXD4	28	7.471039	7.349427	0.0e+00	25.596279	0.0000000
MIR503	12	6.473450	7.288326	0.0e+00	7.337316	0.0011527
HOXB1	16	6.521135	7.287845	9.0e-07	6.062606	0.0211564
TAS2R1	6	-6.937028	-7.169484	1.8e-06	5.750589	0.0430630
MIR411	11	-6.635723	-7.159973	1.0e-07	7.229529	0.0014751
SFTA3	40	5.835427	7.108292	0.0e+00	13.314641	0.0000000
LOC101929681	9	-6.576090	-7.075011	9.0e-07	6.052007	0.0216720
MNDA	10	-6.561520	-7.040268	0.0e+00	7.404846	0.0009876
CCDC140	52	6.792560	6.995631	0.0e+00	39.740933	0.0000000
KRTAP7-1	6	-6.916205	-6.989707	0.0e+00	7.365598	0.0010804

gmea_volc(res2)

gmea_boxplot(res)

res2_t <- res2

plot(res2$nprobes,res2$sig,log="x",ylim=c(0,50),pch=19,cex=0.6)
points(sig$nprobes,sig$sig,col="red",pch=19,cex=0.62)
MIN = min(sig$nprobes)
LEFT = nrow(subset(res2,nprobes<MIN))
RIGHT = nrow(subset(res2,nprobes>MIN))
SIG = nrow(sig)
TOT = nrow(res2)
HEADER <- paste(TOT, "genes in total.", SIG, "with FDR<0.05.",
  RIGHT, "well covered and", LEFT, "poorly covered")
mtext(HEADER)
abline(v=MIN,lty=2)

GMEA using FGSEA test

head(dm)

##            UCSC_RefGene_Name         t
## cg00159780  REXO1L2P;REXO1L1 -13.41989
## cg01772014         C20orf160 -13.36031
## cg14205001            NOTCH1  13.34777
## cg06569947           DCUN1D2  13.21072
## cg17655624   ZNF385A;ZNF385A -13.17366
## cg13531667               MCC -13.13667

tstats <- dm$t
names(tstats) <- rownames(dm)

tic()
fgseaRes <- fgsea(pathways = sets,
                  stats    = tstats,
                  minSize  = 5,
                  nproc = 16)

## Warning in fgseaMultilevel(...): There were 24 pathways for which P-values were
## not calculated properly due to unbalanced (positive and negative) gene-level
## statistic values. For such pathways pval, padj, NES, log2err are set to NA. You
## can try to increase the value of the argument nPermSimple (for example set it
## nPermSimple = 10000)

## Warning in fgseaMultilevel(...): For some of the pathways the P-values were
## likely overestimated. For such pathways log2err is set to NA.

## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are less
## than 1e-50. You can set the `eps` argument to zero for better estimation.

toc()

## 49.495 sec elapsed

fgseaRes <- as.data.frame(fgseaRes)[,1:7]
fgseaRes <- fgseaRes[order(fgseaRes$pval),]

head(fgseaRes,20) %>%
  kbl(caption = "Top significant genes with GMEA-FGSEA") %>%
  kable_paper("hover", full_width = F)

Top significant genes with GMEA-FGSEA
	pathway	log2err	ES	NES	size
8626	HOXA5	NA	0.9751158	4.264259	44
15480	PAX6	NA	0.8355458	4.540388	131
15576	PCDHGA8	NA	0.7066343	4.257646	234
15582	PCDHGB5	NA	0.6960881	4.129469	215
16969	PTPRN2	NA	-0.5493220	-2.371055	1474
15577	PCDHGA9	1.825919	0.6740625	3.896916	198
3333	CCDC140	1.762439	0.9413319	4.313300	52
22866	ZIC4	1.750650	0.8765661	4.361757	77
15583	PCDHGB6	1.714797	0.6586990	3.867038	187
8624	HOXA3	1.690472	0.8627231	4.292875	77
16008	PITX2	1.671997	0.8576116	4.235153	74
5408	DLX6AS	1.640742	0.8782157	4.255537	66
20520	TBX5	1.621700	0.8292883	4.115278	81
15540	PCDHA5	1.602431	0.5833933	3.407617	221
3724	CDH4	1.589456	-0.5596017	-2.353428	382
15477	PAX3	1.582928	0.8881333	4.117349	58
19451	SNORD115-15	1.582928	-0.8243442	-3.073420	81
19457	SNORD115-21	1.582928	-0.8243442	-3.073420	81
8647	HOXC4	1.569792	0.6886474	3.830130	137
15567	PCDHGA10	1.563182	0.6308482	3.688264	172

sig <- subset(fgseaRes,padj<0.05)
SIG = nrow(sig)

plot(fgseaRes$ES,-log10(fgseaRes$pval))
points(sig$ES,-log10(sig$pval),col="red")

plot(fgseaRes$size,-log10(fgseaRes$pval),log="x")
points(sig$size,-log10(sig$pval),col="red")

gmea_volc(res2)

gmea_boxplot(res)

res2_wg <- res2

GMEA using ROAST test

TODO

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	0.0000238	-0.7715770	0.0006371
405	Expression and translocation of olfactory receptors	351	0.0000000	-0.6913602	0.0000000
861	Olfactory Signaling Pathway	359	0.0000000	-0.6848450	0.0000000
291	Defective GALNT3 causes HFTC	18	0.0000007	-0.6768037	0.0000311
390	Endosomal/Vacuolar pathway	12	0.0000529	0.6738191	0.0012828
119	Beta defensins	31	0.0000000	-0.6641412	0.0000000
290	Defective GALNT12 causes CRCS1	18	0.0000054	-0.6193648	0.0001978
304	Defensins	39	0.0000000	-0.6163304	0.0000000
281	Dectin-2 family	28	0.0000000	-0.5959173	0.0000032
286	Defective C1GALT1C1 causes TNPS	19	0.0000137	-0.5760934	0.0004434
321	Digestion	22	0.0000038	-0.5690575	0.0001514
1404	Termination of O-glycan biosynthesis	25	0.0000021	-0.5481980	0.0000947
87	Antimicrobial peptides	83	0.0000000	-0.5470219	0.0000000
380	ERKs are inactivated	13	0.0007751	0.5383799	0.0096231
742	Metallothioneins bind metals	11	0.0020694	0.5362633	0.0187082
1157	Response to metal ions	14	0.0006043	0.5293178	0.0081553
56	Activation of the TFAP2 (AP-2) family of transcription factors	12	0.0015324	0.5281737	0.0151485
963	Presynaptic depolarization and calcium channel opening	12	0.0016093	-0.5257980	0.0154173
1449	Transcriptional regulation of testis differentiation	12	0.0017594	0.5214446	0.0166501
1220	Sensory Perception	575	0.0000000	-0.5209378	0.0000000

Session Information

sessionInfo()

## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
## 
## 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] fgsea_1.22.0                                       
##  [2] kableExtra_1.3.4                                   
##  [3] mitch_1.8.0                                        
##  [4] tictoc_1.1                                         
##  [5] ENmix_1.32.0                                       
##  [6] doParallel_1.0.17                                  
##  [7] qqman_0.1.8                                        
##  [8] RCircos_1.2.2                                      
##  [9] beeswarm_0.4.0                                     
## [10] forestplot_2.0.1                                   
## [11] checkmate_2.1.0                                    
## [12] magrittr_2.0.3                                     
## [13] reshape2_1.4.4                                     
## [14] gplots_3.1.3                                       
## [15] eulerr_6.1.1                                       
## [16] GEOquery_2.64.2                                    
## [17] RColorBrewer_1.1-3                                 
## [18] IlluminaHumanMethylation450kmanifest_0.4.0         
## [19] topconfects_1.12.0                                 
## [20] DMRcatedata_2.14.0                                 
## [21] ExperimentHub_2.4.0                                
## [22] AnnotationHub_3.4.0                                
## [23] BiocFileCache_2.4.0                                
## [24] dbplyr_2.2.1                                       
## [25] DMRcate_2.10.0                                     
## [26] limma_3.52.1                                       
## [27] missMethyl_1.30.0                                  
## [28] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [29] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1 
## [30] minfi_1.42.0                                       
## [31] bumphunter_1.38.0                                  
## [32] locfit_1.5-9.5                                     
## [33] iterators_1.0.14                                   
## [34] foreach_1.5.2                                      
## [35] Biostrings_2.64.0                                  
## [36] XVector_0.36.0                                     
## [37] SummarizedExperiment_1.26.1                        
## [38] Biobase_2.56.0                                     
## [39] MatrixGenerics_1.8.0                               
## [40] matrixStats_0.62.0                                 
## [41] GenomicRanges_1.48.0                               
## [42] GenomeInfoDb_1.32.2                                
## [43] IRanges_2.30.0                                     
## [44] S4Vectors_0.34.0                                   
## [45] BiocGenerics_0.42.0                                
## [46] R.utils_2.12.0                                     
## [47] R.oo_1.25.0                                        
## [48] R.methodsS3_1.8.2                                  
## [49] plyr_1.8.7                                         
## 
## loaded via a namespace (and not attached):
##   [1] rappdirs_0.3.3                rtracklayer_1.56.0           
##   [3] GGally_2.1.2                  tidyr_1.2.0                  
##   [5] ggplot2_3.3.6                 bit64_4.0.5                  
##   [7] knitr_1.39                    DelayedArray_0.22.0          
##   [9] data.table_1.14.2             rpart_4.1.16                 
##  [11] KEGGREST_1.36.2               RCurl_1.98-1.7               
##  [13] AnnotationFilter_1.20.0       generics_0.1.2               
##  [15] GenomicFeatures_1.48.3        preprocessCore_1.58.0        
##  [17] RSQLite_2.2.14                bit_4.0.4                    
##  [19] tzdb_0.3.0                    webshot_0.5.3                
##  [21] xml2_1.3.3                    httpuv_1.6.5                 
##  [23] assertthat_0.2.1              xfun_0.31                    
##  [25] hms_1.1.1                     jquerylib_0.1.4              
##  [27] evaluate_0.15                 promises_1.2.0.1             
##  [29] fansi_1.0.3                   restfulr_0.0.15              
##  [31] scrime_1.3.5                  progress_1.2.2               
##  [33] caTools_1.18.2                readxl_1.4.0                 
##  [35] DBI_1.1.3                     geneplotter_1.74.0           
##  [37] htmlwidgets_1.5.4             reshape_0.8.9                
##  [39] purrr_0.3.4                   ellipsis_0.3.2               
##  [41] dplyr_1.0.9                   backports_1.4.1              
##  [43] permute_0.9-7                 calibrate_1.7.7              
##  [45] annotate_1.74.0               biomaRt_2.52.0               
##  [47] sparseMatrixStats_1.8.0       vctrs_0.4.1                  
##  [49] ensembldb_2.20.2              cachem_1.0.6                 
##  [51] Gviz_1.40.1                   BSgenome_1.64.0              
##  [53] GenomicAlignments_1.32.0      prettyunits_1.1.1            
##  [55] mclust_5.4.10                 svglite_2.1.0                
##  [57] cluster_2.1.3                 RPMM_1.25                    
##  [59] lazyeval_0.2.2                crayon_1.5.1                 
##  [61] genefilter_1.78.0             edgeR_3.38.1                 
##  [63] pkgconfig_2.0.3               nlme_3.1-159                 
##  [65] ProtGenerics_1.28.0           nnet_7.3-17                  
##  [67] rlang_1.0.3                   lifecycle_1.0.1              
##  [69] filelock_1.0.2                dichromat_2.0-0.1            
##  [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_4.2.1                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_7.3-58                   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      fastmatch_1.1-3              
## [111] tools_4.2.1                   rstudioapi_0.13              
## [113] foreign_0.8-82                bsseq_1.32.0                 
## [115] gridExtra_2.3                 digest_0.6.29                
## [117] BiocManager_1.30.18           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] rvest_1.0.2                   XML_3.99-0.10                
## [131] splines_4.2.1                 statmod_1.4.36               
## [133] multtest_2.52.0               systemfonts_1.0.4            
## [135] xtable_1.8-4                  jsonlite_1.8.0               
## [137] dynamicTreeCut_1.63-1         R6_2.5.1                     
## [139] echarts4r_0.4.4               Hmisc_4.7-0                  
## [141] pillar_1.7.0                  htmltools_0.5.2              
## [143] mime_0.12                     glue_1.6.2                   
## [145] fastmap_1.1.0                 BiocParallel_1.30.3          
## [147] interactiveDisplayBase_1.34.0 beanplot_1.3.1               
## [149] codetools_0.2-18              utf8_1.2.2                   
## [151] lattice_0.20-45               bslib_0.3.1                  
## [153] tibble_3.1.7                  curl_4.3.2                   
## [155] gtools_3.9.2.2                openssl_2.0.2                
## [157] survival_3.4-0                rmarkdown_2.14               
## [159] munsell_0.5.0                 rhdf5_2.40.0                 
## [161] GenomeInfoDbData_1.2.8        HDF5Array_1.24.1             
## [163] impute_1.70.0                 gtable_0.3.0