Introduction

Here we are looking at putting together the easiest possible workflow for the package.

Source code: https://github.com/markziemann/schizophrenia

Requirements

Load packages.

Important: ensure that the mitch version used is 1.15.1 or higher.

suppressPackageStartupMessages({
  library("limma")
  library("IlluminaHumanMethylationEPICanno.ilm10b4.hg19")
  library("HGNChelper")
  library("mitch")
  library("kableExtra")
})

if( packageVersion("mitch") < "1.15.1") {
  warning("This workflow requires mitch version 1.15.1 or higher")
}

Load pathways

Gene ontologies were downloaded in GMT format from MSigDB on 15th Jan 2024???,1. The GMT file is read into R using the mitch function gmt_import().

gene_sets <- gmt_import("c5.go.v2023.2.Hs.symbols.gmt")

Curate the annotation

One of the critical parts of this workflow is the establishment of probe-gene relationships. This controls how the probe data is aggregated to make the gene level scores.

As these annotations are several years old, many of the annotated gene names are no longer current. To remedy this, the gene names are screened with the HGNChelper package and any defunct symbols get updated to the newer gene name, so they will be recognised properly in the gene sets.

anno <- getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
myann <- data.frame(anno[,c("UCSC_RefGene_Name","UCSC_RefGene_Group","Islands_Name","Relation_to_Island")])
gp <- myann[,"UCSC_RefGene_Name",drop=FALSE]
gp2 <- strsplit(gp$UCSC_RefGene_Name,";")
names(gp2) <- rownames(gp)
gp2 <- lapply(gp2,unique)
gt1 <- stack(gp2)
colnames(gt1) <- c("gene","probe")
gt1$probe <- as.character(gt1$probe)
dim(gt1)
## [1] 684970      2
str(gt1)
## 'data.frame':    684970 obs. of  2 variables:
##  $ gene : chr  "YTHDF1" "EIF2S3" "PKN3" "CCDC57" ...
##  $ probe: chr  "cg18478105" "cg09835024" "cg14361672" "cg01763666" ...
length(unique(gt1$gene))
## [1] 27364
if (! file.exists("new.hgnc.table.rds")) {
  new.hgnc.table <- getCurrentHumanMap()
  saveRDS(new.hgnc.table, "new.hgnc.table.rds")
}

new.hgnc.table <- readRDS("new.hgnc.table.rds")
fix <- checkGeneSymbols(gt1$gene,map=new.hgnc.table)
## Warning in checkGeneSymbols(gt1$gene, map = new.hgnc.table): Human gene symbols
## should be all upper-case except for the 'orf' in open reading frames. The case
## of some letters was corrected.
## Warning in checkGeneSymbols(gt1$gene, map = new.hgnc.table): x contains
## non-approved gene symbols
fix2 <- fix[which(fix$x != fix$Suggested.Symbol),]
length(unique(fix2$x))
## [1] 3253
gt1$gene <- fix$Suggested.Symbol

head(gt1)
##     gene      probe
## 1 YTHDF1 cg18478105
## 2 EIF2S3 cg09835024
## 3   PKN3 cg14361672
## 4 CCDC57 cg01763666
## 5   INF2 cg12950382
## 6  CDC16 cg02115394

Mitch pipeline

The first part is to import the data into mitch.

This only works properly when mitch’s mapGeneIds function uses mean to aggregate (line 69 of mitch.R).

Data files:

  • EWAS_ageofonset_asrb_epic.txt.gz

  • EWAS_clozapine_asrb_epic.txt.gz

  • EWAS_cognitivestatus_asrb_epic.txt.gz

  • EWAS_gafscore_asrb_epic.txt.gz

  • EWAS_prs_asrb_epic.txt.gz

ageonset <- read.table("ASRB_EWAS/EWAS_ageofonset_asrb_epic.fmt.txt",sep="\t",header=TRUE)
colnames(ageonset) <- c("probe","ageonset")
rownames(ageonset) <- ageonset$probe ; ageonset$probe=NULL
iageonset <- -ageonset

cloz <- read.table("ASRB_EWAS/EWAS_clozapine_asrb_epic.fmt.txt",sep="\t",header=TRUE)
colnames(cloz) <- c("probe","cloz")
rownames(cloz) <- cloz$probe ; cloz$probe=NULL

cognit <- read.table("ASRB_EWAS/EWAS_cognitivestatus_asrb_epic.fmt.txt",sep="\t",header=TRUE)
colnames(cognit) <- c("probe","cognit")
rownames(cognit) <- cognit$probe ; cognit$probe=NULL

gaf <- read.table("ASRB_EWAS/EWAS_gafscore_asrb_epic.fmt.txt",sep="\t",header=TRUE)
colnames(gaf) <- c("probe","gaf")
rownames(gaf) <- gaf$probe ; gaf$probe=NULL
igaf <- -gaf

prs <- read.table("ASRB_EWAS/EWAS_prs_asrb_epic.fmt.txt",sep="\t",header=TRUE)
colnames(prs) <- c("probe","prs")
rownames(prs) <- prs$probe ; prs$probe=NULL

Import data.

mylist2 <- list("iageonset"=iageonset,"cloz"=cloz,"cognit"=cognit,"igaf"=igaf,"prs"=prs)

mm <- mitch_import(x=mylist2, DEtype="prescored",geneTable=gt1)
## Note: Mean no. genes in input = 743553
## Note: no. genes in output = 21942
## Warning in mitch_import(x = mylist2, DEtype = "prescored", geneTable = gt1): Warning: less than half of the input genes are also in the
##         output

Multimitch

mmres <- mitch_calc(x=mm,genesets=gene_sets,minsetsize=5,cores=16,resrows=50,priority="effect")
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
if (! file.exists("multires.html")){
  mitch_report(res=mmres,outfile="multires.html")
}

mmrestop <- mmres$enrichment_result
mmrestop <-  mmrestop[order(mmrestop$pMANOVA),]

head(mmrestop,30) %>%
  kbl(caption = "Top results in multi by p-value") %>%
  kable_paper("hover", full_width = F)
Top results in multi by p-value
set setSize pMANOVA s.iageonset s.cloz s.cognit s.igaf s.prs p.iageonset p.cloz p.cognit p.igaf p.prs s.dist SD p.adjustMANOVA
9566 GOMF_OLFACTORY_RECEPTOR_ACTIVITY 337 0 -0.1643826 -0.3878818 0.1451251 0.6203288 0.0767419 0.0000002 0.0000000 0.0000047 0 0.0154644 0.7676148 0.3782924 0
6854 GOBP_SENSORY_PERCEPTION_OF_SMELL 363 0 -0.1599746 -0.3605992 0.1442609 0.5863283 0.0787174 0.0000002 0.0000000 0.0000023 0 0.0099909 0.7255426 0.3569799 0
1093 GOBP_DETECTION_OF_STIMULUS_INVOLVED_IN_SENSORY_PERCEPTION 451 0 -0.1342604 -0.3057683 0.1387098 0.5193368 0.0422986 0.0000010 0.0000000 0.0000004 0 0.1236194 0.6342400 0.3117320 0
6850 GOBP_SENSORY_PERCEPTION_OF_CHEMICAL_STIMULUS 438 0 -0.1231133 -0.3120787 0.1536195 0.5183220 0.0720320 0.0000099 0.0000000 0.0000000 0 0.0097391 0.6403087 0.3126204 0
1092 GOBP_DETECTION_OF_STIMULUS 571 0 -0.1162970 -0.2374419 0.1448074 0.4412943 0.0239577 0.0000020 0.0000000 0.0000000 0 0.3278078 0.5349649 0.2612697 0
9118 GOMF_G_PROTEIN_COUPLED_RECEPTOR_ACTIVITY 741 0 -0.0938243 -0.1894197 0.1484769 0.3484517 0.0596077 0.0000137 0.0000000 0.0000000 0 0.0057335 0.4378354 0.2102155 0
6848 GOBP_SENSORY_PERCEPTION 862 0 -0.0773733 -0.1685532 0.1150588 0.3055085 0.0191472 0.0001148 0.0000000 0.0000000 0 0.3399189 0.3759487 0.1829116 0
9435 GOMF_MOLECULAR_TRANSDUCER_ACTIVITY 1357 0 -0.0936096 -0.0734071 0.1366308 0.2519580 0.0195108 0.0000000 0.0000057 0.0000000 0 0.2279255 0.3109387 0.1458241 0
1764 GOBP_G_PROTEIN_COUPLED_RECEPTOR_SIGNALING_PATHWAY 1133 0 -0.0947161 -0.0996212 0.1399526 0.2541341 0.0479463 0.0000001 0.0000000 0.0000000 0 0.0064836 0.3246001 0.1525570 0
10106 GOMF_TRANSCRIPTION_REGULATOR_ACTIVITY 1821 0 0.0770863 0.0145107 -0.0355493 -0.2109717 0.0935796 0.0000000 0.3044197 0.0118639 0 0.0000000 0.2463388 0.1224032 0
9948 GOMF_SEQUENCE_SPECIFIC_DNA_BINDING 1562 0 0.0754558 0.0046962 -0.0521996 -0.2273770 0.1058921 0.0000006 0.7567078 0.0005731 0 0.0000000 0.2671214 0.1319130 0
7617 GOCC_CATALYTIC_COMPLEX 1609 0 0.0675418 0.0067616 -0.1425679 -0.2215897 0.0495104 0.0000062 0.6511278 0.0000000 0 0.0009279 0.2765619 0.1274102 0
7665 GOCC_CHROMOSOME 1811 0 0.0782278 0.0258625 -0.0800031 -0.2022736 0.0987343 0.0000000 0.0678656 0.0000000 0 0.0000000 0.2526896 0.1250895 0
8922 GOMF_DNA_BINDING_TRANSCRIPTION_FACTOR_ACTIVITY 1361 0 0.0767887 0.0012863 -0.0258108 -0.2261423 0.1159164 0.0000020 0.9365591 0.1102093 0 0.0000000 0.2667233 0.1327304 0
8122 GOCC_NUCLEAR_PROTEIN_CONTAINING_COMPLEX 1135 0 0.0836151 -0.0022899 -0.1641531 -0.2265251 0.0631030 0.0000020 0.8964763 0.0000000 0 0.0003357 0.2987283 0.1388441 0
3451 GOBP_NERVOUS_SYSTEM_PROCESS 1381 0 -0.0489777 -0.0938900 0.1042962 0.1968257 0.0140583 0.0022745 0.0000000 0.0000000 0 0.3810854 0.2470422 0.1173579 0
8818 GOMF_CIS_REGULATORY_REGION_SEQUENCE_SPECIFIC_DNA_BINDING 1138 0 0.0794211 0.0165518 -0.0333207 -0.2192489 0.1082235 0.0000062 0.3463680 0.0579970 0 0.0000000 0.2597582 0.1294279 0
8259 GOCC_PROTEIN_DNA_COMPLEX 1325 0 0.0806791 0.0422068 -0.0411003 -0.1902320 0.1077949 0.0000008 0.0098947 0.0120099 0 0.0000000 0.2403908 0.1201953 0
4641 GOBP_POSITIVE_REGULATION_OF_RNA_METABOLIC_PROCESS 1805 0 0.0395091 0.0435197 -0.0341398 -0.1735436 0.0666594 0.0053481 0.0021544 0.0160970 0 0.0000026 0.1979427 0.0981181 0
6748 GOBP_RIBONUCLEOPROTEIN_COMPLEX_BIOGENESIS 449 0 0.0681096 -0.0511778 -0.2823454 -0.3041848 0.0441756 0.0133592 0.0630340 0.0000000 0 0.1085838 0.4259776 0.1776535 0
8049 GOCC_MITOCHONDRION 1569 0 0.0192178 0.0181879 -0.1225811 -0.1797461 0.0365386 0.2039351 0.2292332 0.0000000 0 0.0157110 0.2221935 0.0986638 0
9564 GOMF_ODORANT_BINDING 100 0 -0.0547084 -0.3824311 0.1267649 0.6251891 0.0264188 0.3444697 0.0000000 0.0284794 0 0.6480171 0.7462407 0.3652353 0
7954 GOCC_INTRACELLULAR_PROTEIN_CONTAINING_COMPLEX 851 0 0.0634053 0.0333860 -0.1396067 -0.2326162 0.0531955 0.0016830 0.0981638 0.0000000 0 0.0084078 0.2855957 0.1338723 0
3196 GOBP_NEGATIVE_REGULATION_OF_NUCLEOBASE_CONTAINING_COMPOUND_METABOLIC_PROCESS 1430 0 0.0486472 0.0428747 -0.0494451 -0.1738332 0.0785230 0.0020639 0.0066228 0.0017396 0 0.0000007 0.2074451 0.1030372 0
6790 GOBP_RNA_PROCESSING 1248 0 0.0200382 -0.0633376 -0.1728327 -0.1434562 0.0352884 0.2337812 0.0001672 0.0000000 0 0.0360013 0.2368740 0.0936419 0
7820 GOCC_ENVELOPE 1183 0 0.0200343 0.0204073 -0.1269450 -0.1914674 0.0281919 0.2457043 0.2370233 0.0000000 0 0.1023540 0.2332111 0.1023591 0
3310 GOBP_NEGATIVE_REGULATION_OF_RNA_BIOSYNTHETIC_PROCESS 1211 0 0.0658893 0.0511804 -0.0449344 -0.1748868 0.0877487 0.0001131 0.0027116 0.0084721 0 0.0000003 0.2174055 0.1086510 0
6770 GOBP_RIBOSOME_BIOGENESIS 303 0 0.0589775 -0.0554461 -0.3007690 -0.3292038 0.0421914 0.0774307 0.0969034 0.0000000 0 0.2065198 0.4551591 0.1863466 0
732 GOBP_CELL_CYCLE 1732 0 0.0532042 0.0173066 -0.0790776 -0.1553399 0.0459968 0.0002323 0.2312198 0.0000000 0 0.0014614 0.1887582 0.0906216 0
4720 GOBP_POSITIVE_REGULATION_OF_TRANSCRIPTION_BY_RNA_POLYMERASE_II 1191 0 0.0331296 0.0517734 -0.0231155 -0.1774395 0.0763082 0.0541314 0.0026150 0.1790621 0 0.0000091 0.2040100 0.1016249 0 
mmrestop <- subset(mmrestop,p.adjustMANOVA<0.05)

mmrestop <- mmrestop[order(-abs(mmrestop$s.dist)),]

head(mmrestop,30) %>%
  kbl(caption = "Top results in multi by effect size") %>%
  kable_paper("hover", full_width = F)
Top results in multi by effect size
set setSize pMANOVA s.iageonset s.cloz s.cognit s.igaf s.prs p.iageonset p.cloz p.cognit p.igaf p.prs s.dist SD p.adjustMANOVA
1151 GOBP_DISRUPTION_OF_PLASMA_MEMBRANE_INTEGRITY_IN_ANOTHER_ORGANISM 6 0.0000008 -0.9794858 -0.6334792 -0.0481856 -0.7322210 0.1622599 0.0000324 0.0072027 0.8380450 0.0018945 0.4912759 1.3876188 0.4821603 0.0000368
8594 GOMF_ALKANE_1_MONOOXYGENASE_ACTIVITY 5 0.0039303 -0.3329990 0.3559375 0.8767197 0.7099695 -0.2642750 0.1972230 0.1681002 0.0006853 0.0059692 0.3061330 1.2570263 0.5518425 0.0450382
8782 GOMF_CCR6_CHEMOKINE_RECEPTOR_BINDING 5 0.0032941 -0.3699959 0.4948443 0.7827050 0.7358253 0.0446096 0.1519217 0.0553299 0.0024358 0.0043772 0.8628537 1.2400903 0.4919256 0.0393329
866 GOBP_CHORIONIC_TROPHOBLAST_CELL_PROLIFERATION 5 0.0007438 0.5523909 -0.4499157 0.3075626 -0.1435474 0.8932580 0.0324238 0.0814598 0.2336534 0.5783040 0.0005413 1.1919187 0.5365787 0.0121005
2185 GOBP_LOBAR_BRONCHUS_EPITHELIUM_DEVELOPMENT 5 0.0028943 0.0191184 0.7279847 0.3647445 0.6327483 -0.5603592 0.9409845 0.0048137 0.1578204 0.0142708 0.0300063 1.1737711 0.5237498 0.0357021
9743 GOMF_PIRNA_BINDING 5 0.0037048 -0.7239003 0.2479920 0.8274149 0.2976068 -0.0731641 0.0050563 0.3368968 0.0013535 0.2491376 0.7769354 1.1679340 0.5695889 0.0430323
9209 GOMF_IMMUNOGLOBULIN_RECEPTOR_ACTIVITY 6 0.0014187 0.1935783 0.8071207 0.7198061 0.2932166 -0.0492949 0.4115720 0.0006168 0.0022610 0.2135755 0.8343700 1.1381754 0.3618179 0.0205050
4879 GOBP_PROTECTION_FROM_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY 6 0.0000027 0.1970581 -0.8755774 0.5543703 -0.3853331 0.0499027 0.4032160 0.0002035 0.0186885 0.1021407 0.8323578 1.1241735 0.5526127 0.0001039
5066 GOBP_PROXIMAL_DISTAL_PATTERN_FORMATION_INVOLVED_IN_NEPHRON_DEVELOPMENT 5 0.0033800 -0.0219264 0.2042485 0.0876054 -0.8415645 0.6820714 0.9323360 0.4289897 0.7344314 0.0011173 0.0082562 1.1060403 0.5524685 0.0401266
9659 GOMF_PEPTIDOGLYCAN_IMMUNE_RECEPTOR_ACTIVITY 5 0.0023858 -0.5156129 0.3079272 -0.0815700 -0.4397411 -0.7892875 0.0458567 0.2330995 0.7521047 0.0885915 0.0022379 1.0879690 0.4250409 0.0309423
2184 GOBP_LOBAR_BRONCHUS_DEVELOPMENT 6 0.0040715 -0.0119742 0.7074216 0.4115609 0.4253586 -0.5130683 0.9594909 0.0026904 0.0808409 0.0711747 0.0295209 1.0555284 0.4760111 0.0462933
5448 GOBP_REGULATION_OF_CORTICOTROPIN_SECRETION 5 0.0016571 0.9146647 0.2604094 0.3746456 -0.2450928 -0.0952455 0.0003965 0.3132623 0.1468403 0.3425760 0.7122607 1.0554270 0.4531571 0.0233895
9719 GOMF_PHOSPHOLIPASE_A2_INHIBITOR_ACTIVITY 6 0.0006784 0.5298444 0.5846402 -0.3664600 0.1671073 -0.5569840 0.0245996 0.0131341 0.1200652 0.4784218 0.0181387 1.0464168 0.5170431 0.0112513
9011 GOMF_FATTY_ACYL_COA_SYNTHASE_ACTIVITY 6 0.0015094 0.3447757 0.8486658 0.1328714 0.3791940 -0.3058139 0.1436010 0.0003177 0.5730139 0.1077214 0.1945514 1.0459775 0.4189991 0.0215682
1558 GOBP_FOREBRAIN_NEURON_FATE_COMMITMENT 7 0.0009358 0.4141392 0.1984630 0.0218633 -0.7339282 0.5602332 0.0577631 0.3632006 0.9202105 0.0007710 0.0102598 1.0314497 0.5053283 0.0147319
7033 GOBP_STEM_CELL_FATE_SPECIFICATION 6 0.0004211 -0.0106066 0.4984348 0.2463986 -0.8612479 0.0459367 0.9641142 0.0344830 0.2959346 0.0002584 0.8455064 1.0262164 0.5127878 0.0078344
5201 GOBP_REGULATION_OF_ACTIVATION_OF_JANUS_KINASE_ACTIVITY 7 0.0026975 -0.6087401 0.1997265 0.7201602 0.3113810 -0.1296884 0.0052832 0.3601537 0.0009672 0.1536804 0.5523914 1.0212067 0.4985692 0.0339074
1108 GOBP_DETOXIFICATION_OF_NITROGEN_COMPOUND 5 0.0038889 -0.2288280 -0.0876966 0.7547522 -0.2456945 0.5482883 0.3755617 0.7341654 0.0034676 0.3413923 0.0337323 0.9953340 0.4692884 0.0447847
8583 GOMF_ALCOHOL_DEHYDROGENASE_ACTIVITY_ZINC_DEPENDENT 7 0.0029236 0.0002019 -0.3680419 0.0602234 0.6997102 -0.5839135 0.9992620 0.0917439 0.7826105 0.0013452 0.0074625 0.9846985 0.4904773 0.0359144
3913 GOBP_PLATELET_DEGRANULATION 7 0.0037807 -0.1565079 0.5400176 0.5400176 0.5306653 0.2822951 0.4733383 0.0133494 0.0133494 0.0150385 0.1958785 0.9843926 0.3024694 0.0437147
7692 GOCC_CLATHRIN_COMPLEX 6 0.0041767 -0.2092299 0.7058868 -0.1026167 -0.0266533 -0.6301058 0.3747973 0.0027485 0.6633574 0.9099842 0.0075174 0.9748473 0.4838706 0.0471222
81 GOBP_ADENYLATE_CYCLASE_INHIBITING_SEROTONIN_RECEPTOR_SIGNALING_PATHWAY 7 0.0006664 0.3181413 0.1661988 0.6442216 -0.2602690 0.5798496 0.1449436 0.4463830 0.0031588 0.2330796 0.0078876 0.9735619 0.3634555 0.0111058
4246 GOBP_POSITIVE_REGULATION_OF_GASTRO_INTESTINAL_SYSTEM_SMOOTH_MUSCLE_CONTRACTION 5 0.0016958 0.4310252 0.2564161 0.2620869 -0.6183252 -0.4467247 0.0950934 0.3207408 0.3101551 0.0166426 0.0836445 0.9497950 0.4741944 0.0237067
5939 GOBP_REGULATION_OF_NATURAL_KILLER_CELL_CHEMOTAXIS 7 0.0004717 -0.1658732 -0.3813149 -0.0394868 0.7945098 0.2756130 0.4472744 0.0806223 0.8564366 0.0002719 0.2066729 0.9389795 0.4568747 0.0084825
2532 GOBP_MITOCHONDRIAL_RESPIRASOME_ASSEMBLY 10 0.0005639 0.2452398 -0.3018512 -0.4284242 -0.7333850 0.0488601 0.1793112 0.0983538 0.0189733 0.0000591 0.7890520 0.9354383 0.3877727 0.0097306
9130 GOMF_HEMOGLOBIN_BINDING 9 0.0042981 -0.3303850 0.1349615 0.5378552 0.6346348 -0.2068066 0.0861001 0.4832401 0.0052013 0.0009765 0.2826743 0.9285404 0.4311401 0.0483094
557 GOBP_CELLULAR_GLUCURONIDATION 18 0.0000015 0.1433031 0.0778751 0.5487188 0.7202052 -0.0766436 0.2925336 0.5673237 0.0000555 0.0000001 0.5734678 0.9231806 0.3364109 0.0000651
4404 GOBP_POSITIVE_REGULATION_OF_MEMORY_T_CELL_DIFFERENTIATION 9 0.0002866 -0.3367174 0.6413319 0.2923702 0.3800210 0.3065953 0.0802500 0.0008622 0.1288023 0.0483571 0.1112175 0.9211852 0.3602277 0.0056735
8014 GOCC_MHC_CLASS_I_PROTEIN_COMPLEX 10 0.0000079 -0.4498997 -0.4661864 0.2070399 -0.4797465 -0.2955590 0.0137522 0.0106839 0.2569212 0.0086106 0.1055699 0.8832422 0.2913146 0.0002671
5377 GOBP_REGULATION_OF_CELL_CHEMOTAXIS_TO_FIBROBLAST_GROWTH_FACTOR 9 0.0019812 -0.3921286 -0.4235171 -0.4746121 0.0345547 -0.4506907 0.0416378 0.0277924 0.0136753 0.8575415 0.0192121 0.8733294 0.2123372 0.0266967

Single mitch for iageonset, cloz, cognit, igaf and prs.

# age of onset
iageonset_g <- mitch_import(x=iageonset,DEtype="prescored",geneTable=gt1)
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 743553
## Note: no. genes in output = 21942
## Warning in mitch_import(x = iageonset, DEtype = "prescored", geneTable = gt1): Warning: less than half of the input genes are also in the
##         output
m_iageonset <- mitch_calc(iageonset_g,genesets=gene_sets,cores=16, minsetsize=5, resrows=50, priority="effect")
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
head(m_iageonset$enrichment_result,10) %>% kbl(caption = "Top 10 effect size") %>% kable_paper("hover", full_width = F)
Top 10 effect size
set setSize pANOVA s.dist p.adjustANOVA
1151 GOBP_DISRUPTION_OF_PLASMA_MEMBRANE_INTEGRITY_IN_ANOTHER_ORGANISM 6 0.0000324 -0.9794858 0.0114471
5448 GOBP_REGULATION_OF_CORTICOTROPIN_SECRETION 5 0.0003965 0.9146647 0.0634015
9743 GOMF_PIRNA_BINDING 5 0.0050563 -0.7239003 0.2776762
4841 GOBP_PREVENTION_OF_POLYSPERMY 7 0.0012244 -0.7055977 0.1287036
3183 GOBP_NEGATIVE_REGULATION_OF_NEUTROPHIL_ACTIVATION 5 0.0082941 -0.6816702 0.3529942
6843 GOBP_SEMICIRCULAR_CANAL_MORPHOGENESIS 7 0.0032497 0.6423068 0.2187788
9563 GOMF_N_N_DIMETHYLANILINE_MONOOXYGENASE_ACTIVITY 5 0.0144199 -0.6317819 0.4539507
928 GOBP_COMPARTMENT_PATTERN_SPECIFICATION 5 0.0149714 0.6282810 0.4610949
1922 GOBP_INHIBITION_OF_NEUROEPITHELIAL_CELL_DIFFERENTIATION 5 0.0153284 0.6260747 0.4610949
1235 GOBP_EGG_ACTIVATION 11 0.0003951 -0.6169225 0.0634015 
top <- m_iageonset$enrichment_result
top <- top[order(top$pANOVA),]
head(top,10) %>% kbl(caption = "Top 10 significance") %>% kable_paper("hover", full_width = F)
Top 10 significance
set setSize pANOVA s.dist p.adjustANOVA
8335 GOCC_SECRETORY_VESICLE 1019 0e+00 -0.1094220 0.0000350
9435 GOMF_MOLECULAR_TRANSDUCER_ACTIVITY 1357 0e+00 -0.0936096 0.0000367
8333 GOCC_SECRETORY_GRANULE 855 0e+00 -0.1115274 0.0000845
7665 GOCC_CHROMOSOME 1811 0e+00 0.0782278 0.0000845
10106 GOMF_TRANSCRIPTION_REGULATOR_ACTIVITY 1821 0e+00 0.0770863 0.0000988
1764 GOBP_G_PROTEIN_COUPLED_RECEPTOR_SIGNALING_PATHWAY 1133 1e-07 -0.0947161 0.0001277
6854 GOBP_SENSORY_PERCEPTION_OF_SMELL 363 2e-07 -0.1599746 0.0002390
9566 GOMF_OLFACTORY_RECEPTOR_ACTIVITY 337 2e-07 -0.1643826 0.0002723
9948 GOMF_SEQUENCE_SPECIFIC_DNA_BINDING 1562 6e-07 0.0754558 0.0007261
8259 GOCC_PROTEIN_DNA_COMPLEX 1325 8e-07 0.0806791 0.0008340 
nrow(subset(top,p.adjustANOVA<0.05))
## [1] 57
# cloz
cloz_g <- mitch_import(x=cloz,DEtype="prescored",geneTable=gt1)
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 743553
## Note: no. genes in output = 21942
## Warning in mitch_import(x = cloz, DEtype = "prescored", geneTable = gt1): Warning: less than half of the input genes are also in the
##         output
m_cloz <- mitch_calc(cloz_g,genesets=gene_sets,cores=16, minsetsize=5, resrows=50, priority="effect")
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
head(m_cloz$enrichment_result,10) %>% kbl(caption = "Top 10 effect size") %>% kable_paper("hover", full_width = F)
Top 10 effect size
set setSize pANOVA s.dist p.adjustANOVA
4879 GOBP_PROTECTION_FROM_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY 6 0.0002035 -0.8755774 0.0228843
9011 GOMF_FATTY_ACYL_COA_SYNTHASE_ACTIVITY 6 0.0003177 0.8486658 0.0292890
9205 GOMF_IGE_BINDING 5 0.0010993 0.8427497 0.0637513
9209 GOMF_IMMUNOGLOBULIN_RECEPTOR_ACTIVITY 6 0.0006168 0.8071207 0.0450837
4612 GOBP_POSITIVE_REGULATION_OF_PYROPTOSIS 5 0.0017780 -0.8069198 0.0837284
2355 GOBP_MEDIUM_CHAIN_FATTY_ACID_CATABOLIC_PROCESS 5 0.0040815 0.7415508 0.1347291
1003 GOBP_CYTOLYSIS_BY_HOST_OF_SYMBIONT_CELLS 6 0.0017867 -0.7362935 0.0837284
2185 GOBP_LOBAR_BRONCHUS_EPITHELIUM_DEVELOPMENT 5 0.0048137 0.7279847 0.1466018
9373 GOMF_MEDIUM_CHAIN_FATTY_ACID_COA_LIGASE_ACTIVITY 8 0.0004795 0.7128659 0.0389407
2184 GOBP_LOBAR_BRONCHUS_DEVELOPMENT 6 0.0026904 0.7074216 0.1057370 
top <- m_cloz$enrichment_result
top <- top[order(top$pANOVA),]
head(top,10) %>% kbl(caption = "Top 10 significance") %>% kable_paper("hover", full_width = F)
Top 10 significance
set setSize pANOVA s.dist p.adjustANOVA
9566 GOMF_OLFACTORY_RECEPTOR_ACTIVITY 337 0 -0.3878818 0.00e+00
6854 GOBP_SENSORY_PERCEPTION_OF_SMELL 363 0 -0.3605992 0.00e+00
6850 GOBP_SENSORY_PERCEPTION_OF_CHEMICAL_STIMULUS 438 0 -0.3120787 0.00e+00
1093 GOBP_DETECTION_OF_STIMULUS_INVOLVED_IN_SENSORY_PERCEPTION 451 0 -0.3057683 0.00e+00
1092 GOBP_DETECTION_OF_STIMULUS 571 0 -0.2374419 0.00e+00
9118 GOMF_G_PROTEIN_COUPLED_RECEPTOR_ACTIVITY 741 0 -0.1894197 0.00e+00
6848 GOBP_SENSORY_PERCEPTION 862 0 -0.1685532 0.00e+00
9564 GOMF_ODORANT_BINDING 100 0 -0.3824311 0.00e+00
3451 GOBP_NERVOUS_SYSTEM_PROCESS 1381 0 -0.0938900 5.50e-06
708 GOBP_CELL_ACTIVATION 1058 0 0.1039068 1.14e-05 
nrow(subset(top,p.adjustANOVA<0.05))
## [1] 144
# cognit
cognit_g <- mitch_import(x=cognit,DEtype="prescored",geneTable=gt1)
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 743553
## Note: no. genes in output = 21942
## Warning in mitch_import(x = cognit, DEtype = "prescored", geneTable = gt1): Warning: less than half of the input genes are also in the
##         output
m_cognit <- mitch_calc(cognit_g,genesets=gene_sets,cores=16, minsetsize=5, resrows=50, priority="effect")
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
head(m_cognit$enrichment_result,10) %>% kbl(caption = "Top 10 effect size") %>% kable_paper("hover", full_width = F)
Top 10 effect size
set setSize pANOVA s.dist p.adjustANOVA
8594 GOMF_ALKANE_1_MONOOXYGENASE_ACTIVITY 5 0.0006853 0.8767197 0.0273940
9743 GOMF_PIRNA_BINDING 5 0.0013535 0.8274149 0.0446021
8782 GOMF_CCR6_CHEMOKINE_RECEPTOR_BINDING 5 0.0024358 0.7827050 0.0631031
1108 GOBP_DETOXIFICATION_OF_NITROGEN_COMPOUND 5 0.0034676 0.7547522 0.0808283
5245 GOBP_REGULATION_OF_APOPTOTIC_PROCESS_IN_BONE_MARROW_CELL 5 0.0045132 0.7333090 0.0931121
243 GOBP_APOPTOTIC_PROCESS_IN_BONE_MARROW_CELL 6 0.0019061 0.7317955 0.0552575
7832 GOCC_EUKARYOTIC_TRANSLATION_INITIATION_FACTOR_2B_COMPLEX 5 0.0049347 -0.7259242 0.0978912
2471 GOBP_MICROGLIAL_CELL_MEDIATED_CYTOTOXICITY 5 0.0051546 0.7222957 0.1010479
5968 GOBP_REGULATION_OF_NEUTROPHIL_DEGRANULATION 8 0.0004187 0.7202175 0.0191290
5201 GOBP_REGULATION_OF_ACTIVATION_OF_JANUS_KINASE_ACTIVITY 7 0.0009672 0.7201602 0.0344508 
top <- m_cognit$enrichment_result
top <- top[order(top$pANOVA),]
head(top,10) %>% kbl(caption = "Top 10 significance") %>% kable_paper("hover", full_width = F)
Top 10 significance
set setSize pANOVA s.dist p.adjustANOVA
6790 GOBP_RNA_PROCESSING 1248 0 -0.1728327 0
6748 GOBP_RIBONUCLEOPROTEIN_COMPLEX_BIOGENESIS 449 0 -0.2823454 0
7617 GOCC_CATALYTIC_COMPLEX 1609 0 -0.1425679 0
8122 GOCC_NUCLEAR_PROTEIN_CONTAINING_COMPLEX 1135 0 -0.1641531 0
6770 GOBP_RIBOSOME_BIOGENESIS 303 0 -0.3007690 0
8131 GOCC_NUCLEOLUS 1318 0 -0.1394699 0
9435 GOMF_MOLECULAR_TRANSDUCER_ACTIVITY 1357 0 0.1366308 0
8049 GOCC_MITOCHONDRION 1569 0 -0.1225811 0
2739 GOBP_NCRNA_PROCESSING 415 0 -0.2311716 0
1764 GOBP_G_PROTEIN_COUPLED_RECEPTOR_SIGNALING_PATHWAY 1133 0 0.1399526 0 
nrow(subset(top,p.adjustANOVA<0.05))
## [1] 321
# igaf
igaf_g <- mitch_import(x=igaf,DEtype="prescored",geneTable=gt1)
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 743553
## Note: no. genes in output = 21942
## Warning in mitch_import(x = igaf, DEtype = "prescored", geneTable = gt1): Warning: less than half of the input genes are also in the
##         output
m_igaf <- mitch_calc(igaf_g,genesets=gene_sets,cores=16, minsetsize=5, resrows=50, priority="effect")
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
head(m_igaf$enrichment_result,10) %>% kbl(caption = "Top 10 effect size") %>% kable_paper("hover", full_width = F)
Top 10 effect size
set setSize pANOVA s.dist p.adjustANOVA
9129 GOMF_HEMOGLOBIN_ALPHA_BINDING 5 0.0006753 0.8777590 0.0125189
7033 GOBP_STEM_CELL_FATE_SPECIFICATION 6 0.0002584 -0.8612479 0.0057536
208 GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_ENDOGENOUS_LIPID_ANTIGEN_VIA_MHC_CLASS_IB 5 0.0010892 0.8434244 0.0177483
9310 GOMF_LIPID_ANTIGEN_BINDING 5 0.0010892 0.8434244 0.0177483
5066 GOBP_PROXIMAL_DISTAL_PATTERN_FORMATION_INVOLVED_IN_NEPHRON_DEVELOPMENT 5 0.0011173 -0.8415645 0.0180900
9275 GOMF_KETOSTEROID_MONOOXYGENASE_ACTIVITY 5 0.0013588 0.8271231 0.0209591
5939 GOBP_REGULATION_OF_NATURAL_KILLER_CELL_CHEMOTAXIS 7 0.0002719 0.7945098 0.0059715
7502 GOBP_XENOBIOTIC_GLUCURONIDATION 6 0.0007881 0.7912868 0.0140506
1883 GOBP_HYPOXIA_INDUCIBLE_FACTOR_1ALPHA_SIGNALING_PATHWAY 5 0.0031303 -0.7629393 0.0389693
1538 GOBP_FLAVONOID_GLUCURONIDATION 5 0.0031461 0.7625382 0.0391184 
top <- m_igaf$enrichment_result
top <- top[order(top$pANOVA),]
head(top) %>% kbl(caption = "Top 10 significance") %>% kable_paper("hover", full_width = F)
Top 10 significance
set setSize pANOVA s.dist p.adjustANOVA
9566 GOMF_OLFACTORY_RECEPTOR_ACTIVITY 337 0 0.6203288 0
6854 GOBP_SENSORY_PERCEPTION_OF_SMELL 363 0 0.5863283 0
1093 GOBP_DETECTION_OF_STIMULUS_INVOLVED_IN_SENSORY_PERCEPTION 451 0 0.5193368 0
6850 GOBP_SENSORY_PERCEPTION_OF_CHEMICAL_STIMULUS 438 0 0.5183220 0
1092 GOBP_DETECTION_OF_STIMULUS 571 0 0.4412943 0
9118 GOMF_G_PROTEIN_COUPLED_RECEPTOR_ACTIVITY 741 0 0.3484517 0 
nrow(subset(top,p.adjustANOVA<0.05))
## [1] 931
# prs
prs_g <- mitch_import(x=prs,DEtype="prescored",geneTable=gt1)
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 743553
## Note: no. genes in output = 21942
## Warning in mitch_import(x = prs, DEtype = "prescored", geneTable = gt1): Warning: less than half of the input genes are also in the
##         output
m_prs <- mitch_calc(prs_g,genesets=gene_sets,cores=16, minsetsize=5, resrows=50, priority="effect")
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
head(m_prs$enrichment_result,10) %>% kbl(caption = "Top 10 effect size") %>% kable_paper("hover", full_width = F)
Top 10 effect size
set setSize pANOVA s.dist p.adjustANOVA
866 GOBP_CHORIONIC_TROPHOBLAST_CELL_PROLIFERATION 5 0.0005413 0.8932580 0.1360884
9659 GOMF_PEPTIDOGLYCAN_IMMUNE_RECEPTOR_ACTIVITY 5 0.0022379 -0.7892875 0.2948489
3049 GOBP_NEGATIVE_REGULATION_OF_INTERLEUKIN_6_MEDIATED_SIGNALING_PATHWAY 8 0.0005997 -0.7005790 0.1375828
5066 GOBP_PROXIMAL_DISTAL_PATTERN_FORMATION_INVOLVED_IN_NEPHRON_DEVELOPMENT 5 0.0082562 0.6820714 0.5415738
7856 GOCC_FC_RECEPTOR_COMPLEX 5 0.0085837 0.6786616 0.5439996
7509 GOBP_XYLULOSE_5_PHOSPHATE_METABOLIC_PROCESS 6 0.0051745 0.6590840 0.4332814
3775 GOBP_PEPTIDOGLYCAN_METABOLIC_PROCESS 6 0.0062729 -0.6442834 0.4719874
2847 GOBP_NEGATIVE_REGULATION_OF_CELLULAR_RESPONSE_TO_TRANSFORMING_GROWTH_FACTOR_BETA_STIMULUS 5 0.0138782 0.6353376 0.6250317
7692 GOCC_CLATHRIN_COMPLEX 6 0.0075174 -0.6301058 0.5128338
7604 GOCC_B_CELL_RECEPTOR_COMPLEX 5 0.0162681 0.6204768 0.6281956 
top <- m_prs$enrichment_result
top <- top[order(top$pANOVA),]
head(top,10) %>% kbl(caption = "Top 10 significance") %>% kable_paper("hover", full_width = F)
Top 10 significance
set setSize pANOVA s.dist p.adjustANOVA
8922 GOMF_DNA_BINDING_TRANSCRIPTION_FACTOR_ACTIVITY 1361 0.0e+00 0.1159164 0.0000000
9948 GOMF_SEQUENCE_SPECIFIC_DNA_BINDING 1562 0.0e+00 0.1058921 0.0000000
7665 GOCC_CHROMOSOME 1811 0.0e+00 0.0987343 0.0000000
10106 GOMF_TRANSCRIPTION_REGULATOR_ACTIVITY 1821 0.0e+00 0.0935796 0.0000001
8259 GOCC_PROTEIN_DNA_COMPLEX 1325 0.0e+00 0.1077949 0.0000001
8818 GOMF_CIS_REGULATORY_REGION_SEQUENCE_SPECIFIC_DNA_BINDING 1138 0.0e+00 0.1082235 0.0000012
3374 GOBP_NEGATIVE_REGULATION_OF_TRANSCRIPTION_BY_RNA_POLYMERASE_II 891 1.0e-07 0.1042355 0.0001889
3310 GOBP_NEGATIVE_REGULATION_OF_RNA_BIOSYNTHETIC_PROCESS 1211 3.0e-07 0.0877487 0.0003473
3196 GOBP_NEGATIVE_REGULATION_OF_NUCLEOBASE_CONTAINING_COMPOUND_METABOLIC_PROCESS 1430 7.0e-07 0.0785230 0.0007464
4641 GOBP_POSITIVE_REGULATION_OF_RNA_METABOLIC_PROCESS 1805 2.6e-06 0.0666594 0.0026613 
nrow(subset(top,p.adjustANOVA<0.05))
## [1] 22

Now make a barplot of these top findings.

par(mar=c(5,27,5,3))

# iageonset
top <- m_iageonset$enrichment_result
top <- subset(top,p.adjustANOVA<0.05)
up <- head(subset(top,s.dist>0),20)
dn <- head(subset(top,s.dist<0),20)
top <- rbind(up,dn)
vec=top$s.dist
names(vec)=top$set
names(vec) <- gsub("_"," ",names(vec))
vec <- vec[order(vec)]
barplot(abs(vec),col=sign(-vec)+3,horiz=TRUE,las=1,cex.names=0.7,main="Age of onset",xlab="ES")
grid()

# cloz
top <- m_cloz$enrichment_result
top <- subset(top,p.adjustANOVA<0.05)
up <- head(subset(top,s.dist>0),20)
dn <- head(subset(top,s.dist<0),20)
top <- rbind(up,dn)
vec=top$s.dist
names(vec)=top$set
names(vec) <- gsub("_"," ",names(vec))
vec <- vec[order(vec)]
barplot(abs(vec),col=sign(-vec)+3,horiz=TRUE,las=1,cex.names=0.7,main="Clozapine",xlab="ES")
grid()

# cognitive deficit
top <- m_cognit$enrichment_result
top <- subset(top,p.adjustANOVA<0.05)
up <- head(subset(top,s.dist>0),20)
dn <- head(subset(top,s.dist<0),20)
top <- rbind(up,dn)
vec=top$s.dist
names(vec)=top$set
names(vec) <- gsub("_"," ",names(vec))
vec <- vec[order(vec)]
barplot(abs(vec),col=sign(-vec)+3,horiz=TRUE,las=1,cex.names=0.6,main="Cognitive deficit",xlab="ES")
grid()

# igaf
top <- m_igaf$enrichment_result
top <- subset(top,p.adjustANOVA<0.05)
up <- head(subset(top,s.dist>0),20)
dn <- head(subset(top,s.dist<0),20)
top <- rbind(up,dn)
vec=top$s.dist
names(vec)=top$set
names(vec) <- gsub("_"," ",names(vec))
vec <- vec[order(vec)]
barplot(abs(vec),col=sign(-vec)+3,horiz=TRUE,las=1,cex.names=0.6,main="iGAF score",xlab="ES")
grid()

# prs
top <- m_prs$enrichment_result
top <- top[order(top$pANOVA),]
top <- subset(top,p.adjustANOVA<0.05)
up <- head(subset(top,s.dist>0),20)
dn <- head(subset(top,s.dist<0),20)
top <- rbind(up,dn)
vec=top$s.dist
names(vec)=top$set
names(vec) <- gsub("_"," ",names(vec))
vec <- vec[order(vec)]
barplot(abs(vec),col=sign(-vec)+3,horiz=TRUE,las=1,cex.names=0.7,main="PRS",xlab="ES")
grid()

par( mar = c(5.1, 4.1, 4.1, 2.1) )

Reports

if(!file.exists("mitch_iageonset.html")) {
  mitch_report(res=m_iageonset,outfile="mitch_iageonset.html")
}

if(!file.exists("mitch_cloz.html")) {
  mitch_report(res=m_cloz,outfile="mitch_cloz.html")
}

if(!file.exists("mitch_cognit.html")) {
  mitch_report(res=m_cognit,outfile="mitch_cognit.html")
}

if(!file.exists("mitch_igaf.html")) {
  mitch_report(res=m_igaf,outfile="mitch_igaf.html")
}

if(!file.exists("mitch_prs.html")) {
  mitch_report(res=m_prs,outfile="mitch_prs.html")
}

Session information

sessionInfo()
## R version 4.4.0 (2024-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 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] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] kableExtra_1.4.0                                   
##  [2] mitch_1.15.6                                       
##  [3] HGNChelper_0.8.1                                   
##  [4] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
##  [5] minfi_1.48.0                                       
##  [6] bumphunter_1.44.0                                  
##  [7] locfit_1.5-9.9                                     
##  [8] iterators_1.0.14                                   
##  [9] foreach_1.5.2                                      
## [10] Biostrings_2.70.2                                  
## [11] XVector_0.42.0                                     
## [12] SummarizedExperiment_1.32.0                        
## [13] Biobase_2.62.0                                     
## [14] MatrixGenerics_1.14.0                              
## [15] matrixStats_1.3.0                                  
## [16] GenomicRanges_1.54.1                               
## [17] GenomeInfoDb_1.38.6                                
## [18] IRanges_2.36.0                                     
## [19] S4Vectors_0.40.2                                   
## [20] BiocGenerics_0.48.1                                
## [21] limma_3.58.1                                       
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.4.0             later_1.3.2              
##   [3] BiocIO_1.12.0             bitops_1.0-7             
##   [5] filelock_1.0.3            tibble_3.2.1             
##   [7] preprocessCore_1.64.0     XML_3.99-0.16.1          
##   [9] lifecycle_1.0.4           lattice_0.22-6           
##  [11] MASS_7.3-60.2             base64_2.0.1             
##  [13] scrime_1.3.5              magrittr_2.0.3           
##  [15] sass_0.4.9                rmarkdown_2.26           
##  [17] jquerylib_0.1.4           yaml_2.3.8               
##  [19] httpuv_1.6.15             doRNG_1.8.6              
##  [21] askpass_1.2.0             DBI_1.2.2                
##  [23] RColorBrewer_1.1-3        abind_1.4-5              
##  [25] zlibbioc_1.48.0           quadprog_1.5-8           
##  [27] purrr_1.0.2               RCurl_1.98-1.14          
##  [29] rappdirs_0.3.3            GenomeInfoDbData_1.2.11  
##  [31] genefilter_1.84.0         annotate_1.80.0          
##  [33] svglite_2.1.3             DelayedMatrixStats_1.24.0
##  [35] codetools_0.2-20          DelayedArray_0.28.0      
##  [37] xml2_1.3.6                tidyselect_1.2.1         
##  [39] beanplot_1.3.1            BiocFileCache_2.10.1     
##  [41] illuminaio_0.44.0         GenomicAlignments_1.38.2 
##  [43] jsonlite_1.8.8            multtest_2.58.0          
##  [45] survival_3.6-4            systemfonts_1.0.6        
##  [47] tools_4.4.0               progress_1.2.3           
##  [49] Rcpp_1.0.12               glue_1.7.0               
##  [51] gridExtra_2.3             SparseArray_1.2.4        
##  [53] xfun_0.43                 dplyr_1.1.4              
##  [55] HDF5Array_1.30.1          fastmap_1.1.1            
##  [57] GGally_2.2.1              rhdf5filters_1.14.1      
##  [59] fansi_1.0.6               openssl_2.1.2            
##  [61] caTools_1.18.2            digest_0.6.35            
##  [63] R6_2.5.1                  mime_0.12                
##  [65] colorspace_2.1-0          gtools_3.9.5             
##  [67] biomaRt_2.58.2            RSQLite_2.3.6            
##  [69] utf8_1.2.4                tidyr_1.3.1              
##  [71] generics_0.1.3            data.table_1.15.4        
##  [73] rtracklayer_1.62.0        prettyunits_1.2.0        
##  [75] httr_1.4.7                htmlwidgets_1.6.4        
##  [77] S4Arrays_1.2.0            ggstats_0.6.0            
##  [79] pkgconfig_2.0.3           gtable_0.3.5             
##  [81] blob_1.2.4                siggenes_1.76.0          
##  [83] htmltools_0.5.8.1         echarts4r_0.4.5          
##  [85] scales_1.3.0              png_0.1-8                
##  [87] knitr_1.46                rstudioapi_0.16.0        
##  [89] reshape2_1.4.4            tzdb_0.4.0               
##  [91] rjson_0.2.21              nlme_3.1-164             
##  [93] curl_5.2.1                cachem_1.0.8             
##  [95] rhdf5_2.46.1              stringr_1.5.1            
##  [97] KernSmooth_2.23-22        AnnotationDbi_1.64.1     
##  [99] restfulr_0.0.15           GEOquery_2.70.0          
## [101] pillar_1.9.0              grid_4.4.0               
## [103] reshape_0.8.9             vctrs_0.6.5              
## [105] gplots_3.1.3.1            promises_1.3.0           
## [107] dbplyr_2.5.0              xtable_1.8-4             
## [109] beeswarm_0.4.0            evaluate_0.23            
## [111] readr_2.1.5               GenomicFeatures_1.54.3   
## [113] cli_3.6.2                 compiler_4.4.0           
## [115] Rsamtools_2.18.0          rlang_1.1.3              
## [117] crayon_1.5.2              rngtools_1.5.2           
## [119] nor1mix_1.3-3             mclust_6.1.1             
## [121] plyr_1.8.9                stringi_1.8.3            
## [123] viridisLite_0.4.2         BiocParallel_1.36.0      
## [125] munsell_0.5.1             Matrix_1.7-0             
## [127] hms_1.1.3                 sparseMatrixStats_1.14.0 
## [129] bit64_4.0.5               ggplot2_3.5.1            
## [131] Rhdf5lib_1.24.2           KEGGREST_1.42.0          
## [133] statmod_1.5.0             shiny_1.8.1.1            
## [135] highr_0.10                memoise_2.0.1.9000       
## [137] bslib_0.7.0               bit_4.0.5

1. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database hallmark gene set collection. Cell Syst 2015; 1:417–25.