Multi-EWAS enrichment example
The GMEA team
2024-01-16
Introduction
Here we are looking at applying LAM/mitch to a multi-EWAS dataset.
Requirements
suppressPackageStartupMessages({
library("limma")
library("eulerr")
library("IlluminaHumanMethylation450kmanifest")
library("IlluminaHumanMethylationEPICanno.ilm10b4.hg19")
library("HGNChelper")
library("tictoc")
library("mitch")
library("gplots")
library("kableExtra")
library("beeswarm")
library("missMethyl")
library("gridExtra")
library("png")
})
CORES=8Load data
Reactome pathways were downloaded on the 14th Sept 2023 from MsigDB.
We’ll also score the probes by the signed -log pvalue
if(!dir.exists("hillary")){
dir.create("hillary")
}
if(!file.exists("hillary/compressed-disease-states-ewas.tar.gz")){
setwd("hillary")
download.file("https://zenodo.org/records/8021411/files/compressed-disease-states-ewas.tar.gz?download=1",destfile="compressed-disease-states-ewas.tar.gz")
untar("compressed-disease-states-ewas.tar.gz")
setwd("..")
}myfiles1 <- list.files("hillary",pattern="prevalent_full",full.names=TRUE)
l1 <- lapply(myfiles1, read.csv)
l1 <- lapply(l1,function(x) { x[,c("CpG","Beta"),drop=FALSE] } )
names(l1) <- gsub("_gs.csv","",gsub("hillary/prevalent_full_","",myfiles))
str(l1)## List of 14
## $ alzheimers :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.004897 -0.11068 -0.005802 0.014743 -0.000639 ...
## $ breast_cancer :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.008027 -0.001347 -0.015014 0.000159 0.008284 ...
## $ chronic_pain :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.05475 0.00926 0.01377 0.01013 -0.0095 ...
## $ CKD :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.00596 0.0054 0.00243 -0.00618 -0.00671 ...
## $ colorectal_cancer :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.010005 -0.000702 -0.006291 -0.00484 0.001418 ...
## $ COPD :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.025953 -0.015607 -0.001531 -0.010835 0.000989 ...
## $ diabetes :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.006468 -0.001714 -0.018136 0.003039 -0.000918 ...
## $ heart_disease :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.00228 -0.02185 0.02049 -0.0039 0.00153 ...
## $ lung_cancer :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.00586 -0.00214 -0.00204 -0.00697 0.00332 ...
## $ osteoarthritis :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.02473 -0.00845 0.00772 0.01197 0.01001 ...
## $ parkinsons :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.005442 -0.001892 -0.004781 -0.001103 -0.000104 ...
## $ prostate_cancer :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.00579 0.02438 -0.01237 0.0074 -0.00241 ...
## $ rheumatoid_arthritis:'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.01251 0.03812 0.00982 0.01406 0.00283 ...
## $ stroke :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.01456 -0.0153 -0.02123 0.00254 0.00112 ...myfiles2 <- list.files("hillary",pattern="incident_full",full.names=TRUE)
l2 <- lapply(myfiles2, read.csv)
l2 <- lapply(l2,function(x) { x[,c("CpG","Beta"),drop=FALSE] } )
names(l2) <- gsub("_gs.csv","",gsub("hillary/incident_full_","",myfiles2))
str(l2)## List of 19
## $ alzheimers :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.0095 0.00121 0.00108 0.00209 -0.00256 ...
## $ breast_cancer :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.002121 0.008666 0.000462 0.004987 -0.000953 ...
## $ chronic_pain :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.01489 -0.03197 0.00993 -0.0136 0.00384 ...
## $ CKD :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.001575 -0.003478 -0.000156 0.000612 -0.000292 ...
## $ colorectal_cancer :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.001718 0.000956 0.002322 -0.003885 0.001745 ...
## $ COPD :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.00799 0.00653 -0.00138 -0.00247 0.00342 ...
## $ covid_hospitalisation:'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -4.23e-02 6.87e-03 7.28e-05 7.01e-03 5.08e-03 ...
## $ diabetes :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.005986 0.006475 -0.005368 0.000561 0.004567 ...
## $ heart_disease :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.01333 0.00943 -0.00269 -0.0017 -0.00511 ...
## $ ibd :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.0084 0.0066 -0.0147 0.00183 -0.00311 ...
## $ liver_cirrhosis :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.000439 -0.003165 0.002227 0.0022 -0.000803 ...
## $ long_covid :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.2922 0.0673 0.6262 0.0668 -0.1499 ...
## $ lung_cancer :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.00133 0.00213 -0.00791 0.00141 -0.00232 ...
## $ osteoarthritis :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.00269 -0.01725 0.02689 -0.00334 -0.00308 ...
## $ ovarian_cancer :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.000657 0.003344 -0.003583 -0.00115 0.001788 ...
## $ parkinsons :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.00016 0.000965 0.001041 -0.00135 -0.000752 ...
## $ prostate_cancer :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.007516 -0.011051 -0.007453 0.000616 0.001915 ...
## $ rheumatoid_arthritis :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] 0.000515 -0.001186 0.000713 0.001359 0.001786 ...
## $ stroke :'data.frame': 752722 obs. of 2 variables:
## ..$ CpG : chr [1:752722] "cg18478105" "cg14361672" "cg01763666" "cg02115394" ...
## ..$ Beta: num [1:752722] -0.00412 0.002 0.00319 0.00383 -0.00119 ...gs_symbols <- gmt_import("c2.cp.reactome.v2023.1.Hs.symbols.gmt")Curate the annotation
Use all probes on the chip.
Update old gene symbols.
tic()
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$UCSC_RefGene_Name,";")
names(gp2) <- rownames(gp)
gp2 <- lapply(gp2,unique)
gt <- stack(gp2)
colnames(gt) <- c("gene","probe")
gt$probe <- as.character(gt$probe)
dim(gt)## [1] 684970 2str(gt)## 'data.frame': 684970 obs. of 2 variables:
## $ gene : chr "YTHDF1" "EIF2S3" "PKN3" "CCDC57" ...
## $ probe: chr "cg18478105" "cg09835024" "cg14361672" "cg01763666" ...toc() #9.0s## 12.409 sec elapsedtic()
#new.hgnc.table <- getCurrentHumanMap()
new.hgnc.table <- readRDS("new.hgnc.table.rds")
fix <- checkGeneSymbols(gt$gene,map=new.hgnc.table)## Warning in checkGeneSymbols(gt$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(gt$gene, map = new.hgnc.table): x contains
## non-approved gene symbolsfix2 <- fix[which(fix$x != fix$Suggested.Symbol),]
length(unique(fix2$x))## [1] 3253gt$gene <- fix$Suggested.Symbol
toc()## 50.731 sec elapsedMitch import
As we don’t have the test statistic, we will use the delta beta value, which according to preliminary investigations works better than the directional p-value.
l1: prevalent
l2: incident
tic()
m1 <- mitch_import(x=l1,DEtype="prescored",geneTable=gt,geneIDcol="CpG")## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = l1, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## outputtoc() #117 sec## 66.042 sec elapsedhead(m1)## alzheimers breast_cancer chronic_pain CKD
## A1BG -0.008221589 0.0053105947 -0.013071700 0.0033540211
## A1BG-AS1 -0.004233515 0.0024686615 -0.019011331 -0.0014040615
## A1CF -0.009002804 0.0002532217 -0.013886839 0.0007266396
## A2M 0.006820743 0.0034871700 -0.006451970 -0.0046553807
## A2M-AS1 0.061286000 0.0099358000 0.024287000 0.0019842000
## A2ML1 -0.015142667 0.0062460789 0.001835431 -0.0002313603
## colorectal_cancer COPD diabetes heart_disease
## A1BG 0.005786606 -0.0008826229 -0.006799651 -0.004042047
## A1BG-AS1 0.008133452 -0.0026733551 -0.009933892 -0.008125077
## A1CF -0.001142371 0.0055164978 0.001369157 0.015620270
## A2M 0.005373974 0.0025985720 -0.001004644 -0.002524372
## A2M-AS1 0.013436000 0.0105620000 -0.008632800 -0.030906000
## A2ML1 0.002453735 0.0035715661 0.004802442 -0.000115850
## lung_cancer osteoarthritis parkinsons prostate_cancer
## A1BG 0.0021250121 -0.003627459 0.0022200363 0.002281968
## A1BG-AS1 0.0032586708 -0.002584733 0.0031032277 0.004377554
## A1CF -0.0002496335 0.008616171 0.0029512548 0.008346012
## A2M 0.0023641197 -0.003620486 0.0011883607 0.001014237
## A2M-AS1 0.0040564000 0.016744000 0.0002487200 0.005403400
## A2ML1 -0.0009114175 0.008265458 0.0002088644 0.002255033
## rheumatoid_arthritis stroke
## A1BG -0.006779316 0.0002895914
## A1BG-AS1 -0.010101462 0.0007705874
## A1CF -0.001300365 0.0059818522
## A2M 0.006746609 0.0014745213
## A2M-AS1 -0.013946000 -0.0142040000
## A2ML1 0.003013324 0.0022876761tic()
m2 <- mitch_import(x=l2,DEtype="prescored",geneTable=gt,geneIDcol="CpG")## Note: Mean no. genes in input = 752722
## Note: no. genes in output = 22007## Warning in mitch_import(x = l2, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## outputtoc() #158 sec## 91.743 sec elapsedhead(m2)## alzheimers breast_cancer chronic_pain CKD
## A1BG 5.084368e-04 -6.318768e-04 0.0002301753 0.0005615255
## A1BG-AS1 -2.090631e-04 4.519615e-05 -0.0019561746 0.0011224565
## A1CF 1.244080e-03 -1.628947e-03 -0.0031212174 0.0008953483
## A2M -7.458717e-05 -1.811270e-03 0.0034057620 0.0001440781
## A2M-AS1 4.677900e-03 -1.215700e-02 -0.0027084000 -0.0026319000
## A2ML1 1.669304e-03 -5.637453e-04 -0.0045962901 -0.0005658983
## colorectal_cancer COPD covid_hospitalisation diabetes
## A1BG 0.0020387679 0.0017412103 0.007950112 0.006480400
## A1BG-AS1 0.0029418062 0.0022725962 0.009102761 0.009699762
## A1CF -0.0015391277 -0.0002604017 0.004859927 -0.005793625
## A2M -0.0004250785 -0.0015433390 -0.002574839 -0.000805537
## A2M-AS1 0.0057066000 -0.0101360000 -0.009949800 0.010303000
## A2ML1 0.0000405520 0.0017475522 -0.007598520 0.003072690
## heart_disease ibd liver_cirrhosis long_covid lung_cancer
## A1BG 0.0016748316 -0.0016281726 0.0011774437 -0.03270144 -1.829838e-04
## A1BG-AS1 0.0003791538 -0.0019914477 0.0013469785 -0.03182165 -2.571425e-04
## A1CF -0.0001371752 0.0018986026 -0.0019445357 0.03079543 -1.924523e-04
## A2M 0.0005329207 -0.0008026473 -0.0008272411 -0.01638500 -4.189465e-04
## A2M-AS1 0.0057750000 -0.0051522000 0.0026419000 -0.31487000 -2.710530e-04
## A2ML1 0.0028732975 0.0006945010 -0.0010377521 0.10090850 -1.884505e-05
## osteoarthritis ovarian_cancer parkinsons prostate_cancer
## A1BG 0.008759511 0.0006228232 -5.963763e-04 -0.006757779
## A1BG-AS1 0.011684431 0.0006301492 -7.590546e-04 -0.008730646
## A1CF 0.003113947 -0.0013322063 8.361253e-04 -0.001651735
## A2M -0.000602872 -0.0010100706 1.342940e-05 0.002817242
## A2M-AS1 -0.009950300 -0.0000064800 -1.579100e-03 0.010934000
## A2ML1 0.002953585 -0.0008099856 3.977747e-05 0.003276922
## rheumatoid_arthritis stroke
## A1BG 0.0002831442 0.0024092732
## A1BG-AS1 0.0005559785 0.0030393531
## A1CF 0.0017111817 -0.0003764426
## A2M 0.0016425058 0.0010126575
## A2M-AS1 -0.0020590000 0.0117930000
## A2ML1 -0.0006907633 0.0004472456Mitch calc prevalence
Multi-comparison for prevalence.
tic()
mres1 <- mitch_calc(x=m1,genesets=gs_symbols,minsetsize=5,cores=8, priority="effect")## Note: Enrichments with large effect sizes may not be
## statistically significant.toc() #21.3s## 68.186 sec elapsedmtable1 <- mres1$enrichment_result
head(mtable1,20)## set
## 1055 REACTOME_METAL_SEQUESTRATION_BY_ANTIMICROBIAL_PROTEINS
## 758 REACTOME_SODIUM_COUPLED_SULPHATE_DI_AND_TRI_CARBOXYLATE_TRANSPORTERS
## 898 REACTOME_ORGANIC_ANION_TRANSPORT
## 421 REACTOME_REGULATION_OF_GENE_EXPRESSION_IN_ENDOCRINE_COMMITTED_NEUROG3_PROGENITOR_CELLS
## 1588 REACTOME_SENSORY_PERCEPTION_OF_SALTY_TASTE
## 1614 REACTOME_FORMATION_OF_LATERAL_PLATE_MESODERM
## 213 REACTOME_LECTIN_PATHWAY_OF_COMPLEMENT_ACTIVATION
## 1393 REACTOME_INTERLEUKIN_36_PATHWAY
## 521 REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE
## 422 REACTOME_REGULATION_OF_GENE_EXPRESSION_IN_EARLY_PANCREATIC_PRECURSOR_CELLS
## 1532 REACTOME_DEFECTIVE_F9_ACTIVATION
## 1007 REACTOME_CD22_MEDIATED_BCR_REGULATION
## 256 REACTOME_ACTIVATION_OF_C3_AND_C5
## 1534 REACTOME_SIGNALING_BY_MEMBRANE_TETHERED_FUSIONS_OF_PDGFRA_OR_PDGFRB
## 634 REACTOME_VASOPRESSIN_LIKE_RECEPTORS
## 1187 REACTOME_FASL_CD95L_SIGNALING
## 775 REACTOME_RSK_ACTIVATION
## 216 REACTOME_CREATION_OF_C4_AND_C2_ACTIVATORS
## 774 REACTOME_FREE_FATTY_ACID_RECEPTORS
## 1221 REACTOME_PTK6_REGULATES_PROTEINS_INVOLVED_IN_RNA_PROCESSING
## setSize pMANOVA s.alzheimers s.breast_cancer s.chronic_pain
## 1055 6 8.016738e-04 -0.904913413 0.120797540 0.51735527
## 758 5 5.687922e-02 -0.047322971 -0.004145078 -0.65195891
## 898 5 4.249713e-02 -0.245323152 0.179983638 -0.54018726
## 421 5 8.504890e-02 -0.008344696 0.068466503 0.57414780
## 1588 6 8.522210e-03 0.328485069 0.344499492 0.01545384
## 1614 5 1.020562e-01 -0.062212526 -0.237505681 0.37903827
## 213 8 1.019821e-03 -0.290274558 0.301547798 -0.30354789
## 1393 7 2.045340e-03 -0.311610390 0.046038961 -0.36320779
## 521 5 1.716184e-02 -0.206435779 0.267775657 -0.07101173
## 422 8 4.040498e-03 0.091504159 -0.493874722 0.41630301
## 1532 5 3.297438e-04 0.172475230 -0.019216435 0.16436688
## 1007 5 1.069337e-01 -0.214416871 0.224543223 0.25872193
## 256 6 1.486393e-01 -0.149190188 0.348150842 -0.44740391
## 1534 5 1.226236e-01 -0.458394691 0.048941005 0.65586765
## 634 5 2.279373e-01 -0.104572312 0.318571039 0.44294155
## 1187 5 2.404745e-01 -0.137896555 -0.545659486 0.15611308
## 775 5 3.170955e-01 0.212144351 -0.325661304 -0.29730025
## 216 14 2.696386e-05 -0.270995317 0.383154380 -0.37591182
## 774 5 4.308581e-02 0.226252159 -0.026997546 0.48473775
## 1221 5 9.807675e-03 0.108971912 -0.321861649 0.17982002
## s.CKD s.colorectal_can s.COPD s.diabetes s.heart_disease
## 1055 0.31663712 -0.24709483 -0.254912655 0.15840189 0.4699180
## 758 -0.69824561 0.18978275 0.612398873 -0.30031815 -0.4302700
## 898 -0.55885829 0.30531770 0.742277975 0.20599945 -0.5490774
## 421 0.40979911 -0.36576675 -0.277483865 0.29628216 0.5824561
## 1588 -0.70289229 0.39770920 0.203930124 0.22026272 -0.4092693
## 1614 0.22810654 -0.62085265 -0.258012908 0.15274975 0.3437687
## 213 -0.06496886 0.43357653 0.459770899 -0.09761807 -0.5660371
## 1393 -0.09242857 0.16329870 0.739818182 0.02364935 -0.4857403
## 521 0.17634760 0.46837560 0.392164349 -0.01701663 -0.5638578
## 422 -0.03661530 -0.52031911 -0.261148234 0.22670349 0.5447407
## 1532 -0.08379238 -0.70200891 -0.641505318 -0.42899736 -0.4364694
## 1007 0.24543223 -0.19938187 -0.216471230 0.28793746 0.2999182
## 256 -0.33854522 0.13984213 0.300259079 -0.14817508 -0.4767662
## 1534 0.04785020 -0.52764294 -0.601818017 0.09055540 0.2462503
## 634 0.01507136 -0.54884101 -0.570929915 0.09053722 0.4532497
## 1187 0.22523407 -0.83081538 -0.371329879 -0.01643487 0.2690119
## 775 -0.40289065 0.08995546 -0.113080629 0.07637488 -0.4232888
## 216 -0.02572897 0.15685510 0.553832063 -0.07016518 -0.4119428
## 774 0.22027088 0.69359149 -0.006799382 0.19483683 0.2549405
## 1221 0.38991001 -0.30773566 0.454395055 -0.28279247 0.1054995
## s.lung_cancer s.osteoarthritis s.parkinsons s.prostate_cance
## 1055 -0.5431874309 0.50578004 -0.418677939 0.73154251
## 758 -0.6305063176 0.47715662 -0.341532588 -0.08853741
## 898 -0.1713844196 0.57669303 0.299809108 0.00219980
## 421 0.3288246523 -0.43494228 -0.167402963 -0.59989092
## 1588 -0.2490795873 0.30216808 -0.494689635 -0.09723800
## 1614 0.0581219889 -0.55631306 -0.454122353 -0.70542678
## 213 -0.6514841584 0.37590345 -0.006920769 0.30888904
## 1393 -0.5186103896 0.19079221 0.296571429 -0.06954545
## 521 -0.7477865649 0.36365785 -0.182510681 0.08004727
## 422 0.1773603346 -0.58117414 -0.167257603 -0.51153462
## 1532 -0.1095900373 0.09613671 0.184874102 -0.27126625
## 1007 0.3795836742 0.54435051 -0.125734024 0.26257613
## 256 -0.4030574368 -0.13300911 -0.128933533 -0.23147433
## 1534 -0.0640669030 -0.23939642 -0.298045632 0.35476775
## 634 -0.1568402872 0.06477593 -0.291009908 -0.62574311
## 1187 -0.2199800018 -0.15527679 -0.380383601 -0.35364058
## 775 0.0789928188 -0.06470321 -0.597872921 -0.21579856
## 216 -0.4277140129 0.42045846 0.156939546 0.26242766
## 774 0.4330333606 0.17952913 0.446286701 0.23032452
## 1221 -0.0005635851 0.51233524 0.436033088 0.28355604
## s.rheumatoid_art s.stroke p.alzheimers p.breast_cancer p.chronic_pain
## 1055 0.10554066 -0.08685969 0.0001234742 0.60836892 0.02818881
## 758 -0.17840196 0.02901554 0.8546019302 0.98719364 0.01157712
## 898 -0.43208799 0.05112263 0.3421222521 0.48582864 0.03644892
## 421 0.42787019 0.39181893 0.9742221537 0.79091344 0.02618811
## 1588 0.36034726 -0.60968441 0.1634971397 0.14392195 0.94773415
## 1614 0.02756113 -0.18011090 0.8096270581 0.35772556 0.14216000
## 213 -0.06650302 0.46478249 0.1551010173 0.13968619 0.13707888
## 1393 0.07972727 0.62762338 0.1533773511 0.83294065 0.09608839
## 521 0.14720480 0.45044996 0.4240632075 0.29977006 0.78333114
## 422 0.04874085 -0.01804627 0.6540297342 0.01556043 0.04144307
## 1532 0.14185983 0.51815290 0.5042081203 0.94068236 0.52446060
## 1007 0.80669030 0.30695391 0.4063703366 0.38456745 0.31640811
## 256 -0.66298502 -0.47678136 0.5268374771 0.13972114 0.05771122
## 1534 0.20070903 0.25119535 0.0758787305 0.84968883 0.01108785
## 634 0.12851559 -0.09264612 0.6855226719 0.21734028 0.08629569
## 1187 -0.10617217 -0.16562131 0.5933541369 0.03459416 0.54549609
## 775 -0.44603218 -0.66243069 0.4113627605 0.20727690 0.24962528
## 216 -0.11186676 0.51659294 0.0791476056 0.01305128 0.01487378
## 774 0.44099627 0.03972366 0.3809598182 0.91673826 0.06049939
## 1221 0.48968276 -0.30206345 0.6730430203 0.21262698 0.48622526
## p.CKD p.colorectal_can p.COPD p.diabetes p.heart_disease
## 1055 0.179224822 0.294571714 0.2795599942 0.50163719 0.046217846
## 758 0.006849845 0.462397548 0.0177132658 0.24485106 0.095674175
## 898 0.030449374 0.237083655 0.0040452655 0.42504328 0.033477098
## 421 0.112530747 0.156658594 0.2825890786 0.25125043 0.024096389
## 1588 0.002865173 0.091590681 0.3870154584 0.35013661 0.082544988
## 1614 0.377068747 0.016203464 0.3177362010 0.55418627 0.183119736
## 213 0.750329017 0.033696527 0.0243222339 0.63256758 0.005561305
## 1393 0.671952116 0.454356481 0.0006989680 0.91371682 0.026041905
## 521 0.494683575 0.069713206 0.1288564188 0.94746228 0.028994732
## 422 0.857674761 0.010815237 0.2008694482 0.26684120 0.007624693
## 1532 0.745581027 0.006555214 0.0129809963 0.09665951 0.090988633
## 1007 0.341907657 0.440069224 0.4018884590 0.26484926 0.245480075
## 256 0.150979016 0.553055546 0.2027808028 0.52965349 0.043129775
## 1534 0.853000402 0.041022649 0.0197756336 0.72584321 0.340300940
## 634 0.953460943 0.033553336 0.0270394348 0.72589604 0.079226666
## 1187 0.383106538 0.001292841 0.1504501840 0.94925598 0.297544321
## 775 0.118720654 0.727587030 0.6614691290 0.76742215 0.101179708
## 216 0.867621612 0.309549364 0.0003326864 0.64943090 0.007609800
## 774 0.393677523 0.007230644 0.9789946007 0.45056339 0.323533409
## 1221 0.131069772 0.233390187 0.0784711559 0.27348210 0.682885377
## p.lung_cancer p.osteoarthritis p.parkinsons p.prostate_cance
## 1055 0.021209005 0.031909635 0.07572951 0.001913030
## 758 0.014618704 0.064634693 0.18598477 0.731714375
## 898 0.506908447 0.025531247 0.24565182 0.993203433
## 421 0.202897827 0.092125359 0.51682876 0.020173025
## 1588 0.290710385 0.199924412 0.03586188 0.679997135
## 1614 0.821926945 0.031213700 0.07865051 0.006297556
## 213 0.001416757 0.065595937 0.97295959 0.130300132
## 1393 0.017491466 0.382042775 0.17421119 0.750007945
## 521 0.003780159 0.159062229 0.47972534 0.756584342
## 422 0.385019557 0.004416900 0.41267099 0.012225128
## 1532 0.671296774 0.709689573 0.47405527 0.293514114
## 1007 0.141586992 0.035030328 0.62634046 0.309252565
## 256 0.087311295 0.572616052 0.58443722 0.326157248
## 1534 0.804066366 0.353911269 0.24844010 0.169502257
## 634 0.543626056 0.801942820 0.25978445 0.015382703
## 1187 0.394302498 0.547650560 0.14074979 0.170861822
## 775 0.759691396 0.802160553 0.02059661 0.403352701
## 216 0.005586621 0.006447888 0.30928897 0.089103882
## 774 0.093562276 0.486930799 0.08394788 0.372447033
## 1221 0.998258711 0.047254004 0.09131226 0.272188889
## p.rheumatoid_art p.stroke s.dist SD p.adjustMANOVA
## 1055 0.654380093 0.7125423081 1.689178 0.4671946 0.0071931607
## 758 0.489669966 0.9105398839 1.543890 0.3999646 0.2142476469
## 898 0.094280520 0.8430736797 1.525113 0.4228746 0.1753273690
## 421 0.097539000 0.1291936651 1.470356 0.3975515 0.2826929032
## 1588 0.126371261 0.0096993065 1.430294 0.3956374 0.0518276618
## 1614 0.915006653 0.4855202809 1.378200 0.3557849 0.3191930236
## 213 0.744636504 0.0228130686 1.372042 0.3798981 0.0089718343
## 1393 0.714903932 0.0040302921 1.359633 0.3763174 0.0162275629
## 521 0.568659594 0.0810983897 1.340738 0.3695512 0.0907074594
## 422 0.811319898 0.9295688973 1.332503 0.3607191 0.0281122764
## 1532 0.582779672 0.0447978815 1.326901 0.3527535 0.0031663117
## 1007 0.001783425 0.2345798262 1.324072 0.2998233 0.3309976823
## 256 0.004915547 0.0431231413 1.316983 0.3001552 0.4041522171
## 1534 0.437031585 0.3306924480 1.316774 0.3645601 0.3616819425
## 634 0.618727003 0.7197774265 1.296072 0.3535398 0.5174090100
## 1187 0.680974462 0.5213003394 1.293676 0.3010828 0.5287060830
## 775 0.084124681 0.0103075448 1.288356 0.2741632 0.6127899048
## 216 0.468628730 0.0008166891 1.265218 0.3464056 0.0003298741
## 774 0.087685197 0.8777493734 1.259963 0.2057344 0.1768672636
## 1221 0.057922044 0.2421194881 1.247462 0.3208897 0.0583485567#numsig manova
nrow(subset(mtable1,p.adjustMANOVA<0.05))## [1] 266sig <- subset(mtable1,p.adjustMANOVA<0.05)Individual comparisons.
r1 <- lapply(l1,function(m) {
m2 <- mitch_import(x=m,DEtype="prescored",geneTable=gt,geneIDcol="CpG")
mres <- mitch_calc(x=m2,genesets=gs_symbols,minsetsize=5,cores=8, priority="effect")
mtable <- mres$enrichment_result
})## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.nup <- lapply(r1,function(r) {
ups <- subset(r,p.adjustANOVA<0.05 & s.dist>0)$set
length(ups)
} )
ndn <- lapply(r1,function(r) {
dns <- subset(r,p.adjustANOVA<0.05 & s.dist<0)$set
length(dns)
} )
nums <- cbind(unlist(ndn),unlist(nup))
colnames(nums) <- c("dn","up")
nums## dn up
## alzheimers 2 1
## breast_cancer 60 2
## chronic_pain 2 4
## CKD 15 132
## colorectal_cancer 20 1
## COPD 214 49
## diabetes 11 76
## heart_disease 33 181
## lung_cancer 8 1
## osteoarthritis 29 14
## parkinsons 2 5
## prostate_cancer 3 17
## rheumatoid_arthritis 0 8
## stroke 4 5rownames(nums) <- gsub("_"," ",rownames(nums))
par(mar = c(5.1, 10.1, 4.1, 2.1))
barplot(t(nums),beside=TRUE,horiz=TRUE,las=1,
legend.text = c("down","up"),xlab="no. pathways")
abline(v=seq(0,200,50),lty=2,lwd=0.5,col="gray")
pdf("fig8a.pdf",height=6,width=4)
par(mar = c(5.1, 10.1, 4.1, 2.1))
barplot(t(nums),beside=TRUE,horiz=TRUE,las=1,
legend.text = c("down","up"),xlab="no. pathways")
abline(v=seq(0,200,50),lty=2,lwd=0.5,col="gray")
dev.off()## png
## 2r2 <- lapply(r1,function(r) {
ups <- head(subset(r,p.adjustANOVA<0.05 & s.dist > 0.3)$set,5)
dns <- head(subset(r,p.adjustANOVA<0.05 & s.dist < -0.3)$set,5)
list("ups"=ups,"dns"=dns)
} )
r2## $alzheimers
## $alzheimers$ups
## character(0)
##
## $alzheimers$dns
## character(0)
##
##
## $breast_cancer
## $breast_cancer$ups
## character(0)
##
## $breast_cancer$dns
## [1] "REACTOME_ASSEMBLY_OF_THE_ORC_COMPLEX_AT_THE_ORIGIN_OF_REPLICATION"
## [2] "REACTOME_DNA_METHYLATION"
## [3] "REACTOME_ACTIVATED_PKN1_STIMULATES_TRANSCRIPTION_OF_AR_ANDROGEN_RECEPTOR_REGULATED_GENES_KLK2_AND_KLK3"
## [4] "REACTOME_SIRT1_NEGATIVELY_REGULATES_RRNA_EXPRESSION"
## [5] "REACTOME_CONDENSATION_OF_PROPHASE_CHROMOSOMES"
##
##
## $chronic_pain
## $chronic_pain$ups
## character(0)
##
## $chronic_pain$dns
## character(0)
##
##
## $CKD
## $CKD$ups
## [1] "REACTOME_SARS_COV_1_MODULATES_HOST_TRANSLATION_MACHINERY"
## [2] "REACTOME_SARS_COV_2_MODULATES_HOST_TRANSLATION_MACHINERY"
## [3] "REACTOME_RESPONSE_OF_EIF2AK1_HRI_TO_HEME_DEFICIENCY"
## [4] "REACTOME_ACTIVATED_PKN1_STIMULATES_TRANSCRIPTION_OF_AR_ANDROGEN_RECEPTOR_REGULATED_GENES_KLK2_AND_KLK3"
## [5] "REACTOME_NGF_STIMULATED_TRANSCRIPTION"
##
## $CKD$dns
## [1] "REACTOME_SENSORY_PERCEPTION_OF_SALTY_TASTE"
## [2] "REACTOME_SCAVENGING_OF_HEME_FROM_PLASMA"
## [3] "REACTOME_ACYL_CHAIN_REMODELLING_OF_PC"
##
##
## $colorectal_cancer
## $colorectal_cancer$ups
## character(0)
##
## $colorectal_cancer$dns
## [1] "REACTOME_INTERLEUKIN_10_SIGNALING"
## [2] "REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION"
## [3] "REACTOME_CHEMOKINE_RECEPTORS_BIND_CHEMOKINES"
## [4] "REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE"
##
##
## $COPD
## $COPD$ups
## [1] "REACTOME_ORGANIC_ANION_TRANSPORT"
## [2] "REACTOME_INTERLEUKIN_36_PATHWAY"
## [3] "REACTOME_EICOSANOIDS"
## [4] "REACTOME_CREATION_OF_C4_AND_C2_ACTIVATORS"
## [5] "REACTOME_FATTY_ACIDS"
##
## $COPD$dns
## [1] "REACTOME_VITAMIN_C_ASCORBATE_METABOLISM"
## [2] "REACTOME_NFE2L2_REGULATES_PENTOSE_PHOSPHATE_PATHWAY_GENES"
## [3] "REACTOME_MAPK1_ERK2_ACTIVATION"
## [4] "REACTOME_SYNTHESIS_OF_5_EICOSATETRAENOIC_ACIDS"
## [5] "REACTOME_STAT5_ACTIVATION_DOWNSTREAM_OF_FLT3_ITD_MUTANTS"
##
##
## $diabetes
## $diabetes$ups
## [1] "REACTOME_KSRP_KHSRP_BINDS_AND_DESTABILIZES_MRNA"
## [2] "REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_G2_CELL_CYCLE_ARREST"
## [3] "REACTOME_PD_1_SIGNALING"
## [4] "REACTOME_GENERATION_OF_SECOND_MESSENGER_MOLECULES"
## [5] "REACTOME_SUMOYLATION_OF_DNA_REPLICATION_PROTEINS"
##
## $diabetes$dns
## [1] "REACTOME_METABOLISM_OF_ANGIOTENSINOGEN_TO_ANGIOTENSINS"
## [2] "REACTOME_DEFENSINS"
## [3] "REACTOME_O_GLYCOSYLATION_OF_TSR_DOMAIN_CONTAINING_PROTEINS"
##
##
## $heart_disease
## $heart_disease$ups
## [1] "REACTOME_RECYCLING_OF_EIF2_GDP"
## [2] "REACTOME_FLT3_SIGNALING_BY_CBL_MUTANTS"
## [3] "REACTOME_FOLDING_OF_ACTIN_BY_CCT_TRIC"
## [4] "REACTOME_ENDOSOMAL_VACUOLAR_PATHWAY"
## [5] "REACTOME_NEGATIVE_REGULATION_OF_FLT3"
##
## $heart_disease$dns
## [1] "REACTOME_LECTIN_PATHWAY_OF_COMPLEMENT_ACTIVATION"
## [2] "REACTOME_DIGESTION_AND_ABSORPTION"
## [3] "REACTOME_DIGESTION"
## [4] "REACTOME_PTK6_REGULATES_RHO_GTPASES_RAS_GTPASE_AND_MAP_KINASES"
## [5] "REACTOME_MET_ACTIVATES_PTK2_SIGNALING"
##
##
## $lung_cancer
## $lung_cancer$ups
## character(0)
##
## $lung_cancer$dns
## character(0)
##
##
## $osteoarthritis
## $osteoarthritis$ups
## [1] "REACTOME_INTERLEUKIN_10_SIGNALING"
##
## $osteoarthritis$dns
## [1] "REACTOME_FORMATION_OF_SENESCENCE_ASSOCIATED_HETEROCHROMATIN_FOCI_SAHF"
## [2] "REACTOME_SEALING_OF_THE_NUCLEAR_ENVELOPE_NE_BY_ESCRT_III"
##
##
## $parkinsons
## $parkinsons$ups
## [1] "REACTOME_ASPIRIN_ADME"
##
## $parkinsons$dns
## character(0)
##
##
## $prostate_cancer
## $prostate_cancer$ups
## [1] "REACTOME_EICOSANOIDS"
## [2] "REACTOME_FATTY_ACIDS"
## [3] "REACTOME_ASPIRIN_ADME"
## [4] "REACTOME_SARS_COV_2_MODULATES_HOST_TRANSLATION_MACHINERY"
## [5] "REACTOME_SNRNP_ASSEMBLY"
##
## $prostate_cancer$dns
## [1] "REACTOME_REGULATION_OF_GENE_EXPRESSION_IN_BETA_CELLS"
## [2] "REACTOME_REGULATION_OF_BETA_CELL_DEVELOPMENT"
##
##
## $rheumatoid_arthritis
## $rheumatoid_arthritis$ups
## [1] "REACTOME_INTERLEUKIN_10_SIGNALING"
##
## $rheumatoid_arthritis$dns
## character(0)
##
##
## $stroke
## $stroke$ups
## [1] "REACTOME_GENERATION_OF_SECOND_MESSENGER_MOLECULES"
## [2] "REACTOME_ASPIRIN_ADME"
##
## $stroke$dns
## character(0)gsets <- unique(unname(unlist(r2)))
gsets## [1] "REACTOME_ASSEMBLY_OF_THE_ORC_COMPLEX_AT_THE_ORIGIN_OF_REPLICATION"
## [2] "REACTOME_DNA_METHYLATION"
## [3] "REACTOME_ACTIVATED_PKN1_STIMULATES_TRANSCRIPTION_OF_AR_ANDROGEN_RECEPTOR_REGULATED_GENES_KLK2_AND_KLK3"
## [4] "REACTOME_SIRT1_NEGATIVELY_REGULATES_RRNA_EXPRESSION"
## [5] "REACTOME_CONDENSATION_OF_PROPHASE_CHROMOSOMES"
## [6] "REACTOME_SARS_COV_1_MODULATES_HOST_TRANSLATION_MACHINERY"
## [7] "REACTOME_SARS_COV_2_MODULATES_HOST_TRANSLATION_MACHINERY"
## [8] "REACTOME_RESPONSE_OF_EIF2AK1_HRI_TO_HEME_DEFICIENCY"
## [9] "REACTOME_NGF_STIMULATED_TRANSCRIPTION"
## [10] "REACTOME_SENSORY_PERCEPTION_OF_SALTY_TASTE"
## [11] "REACTOME_SCAVENGING_OF_HEME_FROM_PLASMA"
## [12] "REACTOME_ACYL_CHAIN_REMODELLING_OF_PC"
## [13] "REACTOME_INTERLEUKIN_10_SIGNALING"
## [14] "REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION"
## [15] "REACTOME_CHEMOKINE_RECEPTORS_BIND_CHEMOKINES"
## [16] "REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE"
## [17] "REACTOME_ORGANIC_ANION_TRANSPORT"
## [18] "REACTOME_INTERLEUKIN_36_PATHWAY"
## [19] "REACTOME_EICOSANOIDS"
## [20] "REACTOME_CREATION_OF_C4_AND_C2_ACTIVATORS"
## [21] "REACTOME_FATTY_ACIDS"
## [22] "REACTOME_VITAMIN_C_ASCORBATE_METABOLISM"
## [23] "REACTOME_NFE2L2_REGULATES_PENTOSE_PHOSPHATE_PATHWAY_GENES"
## [24] "REACTOME_MAPK1_ERK2_ACTIVATION"
## [25] "REACTOME_SYNTHESIS_OF_5_EICOSATETRAENOIC_ACIDS"
## [26] "REACTOME_STAT5_ACTIVATION_DOWNSTREAM_OF_FLT3_ITD_MUTANTS"
## [27] "REACTOME_KSRP_KHSRP_BINDS_AND_DESTABILIZES_MRNA"
## [28] "REACTOME_TP53_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_G2_CELL_CYCLE_ARREST"
## [29] "REACTOME_PD_1_SIGNALING"
## [30] "REACTOME_GENERATION_OF_SECOND_MESSENGER_MOLECULES"
## [31] "REACTOME_SUMOYLATION_OF_DNA_REPLICATION_PROTEINS"
## [32] "REACTOME_METABOLISM_OF_ANGIOTENSINOGEN_TO_ANGIOTENSINS"
## [33] "REACTOME_DEFENSINS"
## [34] "REACTOME_O_GLYCOSYLATION_OF_TSR_DOMAIN_CONTAINING_PROTEINS"
## [35] "REACTOME_RECYCLING_OF_EIF2_GDP"
## [36] "REACTOME_FLT3_SIGNALING_BY_CBL_MUTANTS"
## [37] "REACTOME_FOLDING_OF_ACTIN_BY_CCT_TRIC"
## [38] "REACTOME_ENDOSOMAL_VACUOLAR_PATHWAY"
## [39] "REACTOME_NEGATIVE_REGULATION_OF_FLT3"
## [40] "REACTOME_LECTIN_PATHWAY_OF_COMPLEMENT_ACTIVATION"
## [41] "REACTOME_DIGESTION_AND_ABSORPTION"
## [42] "REACTOME_DIGESTION"
## [43] "REACTOME_PTK6_REGULATES_RHO_GTPASES_RAS_GTPASE_AND_MAP_KINASES"
## [44] "REACTOME_MET_ACTIVATES_PTK2_SIGNALING"
## [45] "REACTOME_FORMATION_OF_SENESCENCE_ASSOCIATED_HETEROCHROMATIN_FOCI_SAHF"
## [46] "REACTOME_SEALING_OF_THE_NUCLEAR_ENVELOPE_NE_BY_ESCRT_III"
## [47] "REACTOME_ASPIRIN_ADME"
## [48] "REACTOME_SNRNP_ASSEMBLY"
## [49] "REACTOME_REGULATION_OF_GENE_EXPRESSION_IN_BETA_CELLS"
## [50] "REACTOME_REGULATION_OF_BETA_CELL_DEVELOPMENT"par(mar = c(5.1, 4.1, 4.1, 2.1))Make a heatmap with these gene sets.
top <- mtable1[which(mtable1$set %in% gsets),]
top <- top[,c(1,4:17)]
rownames(top) <- top$set
top$set=NULL
head(top,2)## s.alzheimers s.breast_cancer
## REACTOME_ORGANIC_ANION_TRANSPORT -0.2453232 0.1799836
## REACTOME_SENSORY_PERCEPTION_OF_SALTY_TASTE 0.3284851 0.3444995
## s.chronic_pain s.CKD
## REACTOME_ORGANIC_ANION_TRANSPORT -0.54018726 -0.5588583
## REACTOME_SENSORY_PERCEPTION_OF_SALTY_TASTE 0.01545384 -0.7028923
## s.colorectal_can s.COPD
## REACTOME_ORGANIC_ANION_TRANSPORT 0.3053177 0.7422780
## REACTOME_SENSORY_PERCEPTION_OF_SALTY_TASTE 0.3977092 0.2039301
## s.diabetes s.heart_disease
## REACTOME_ORGANIC_ANION_TRANSPORT 0.2059995 -0.5490774
## REACTOME_SENSORY_PERCEPTION_OF_SALTY_TASTE 0.2202627 -0.4092693
## s.lung_cancer s.osteoarthritis
## REACTOME_ORGANIC_ANION_TRANSPORT -0.1713844 0.5766930
## REACTOME_SENSORY_PERCEPTION_OF_SALTY_TASTE -0.2490796 0.3021681
## s.parkinsons s.prostate_cance
## REACTOME_ORGANIC_ANION_TRANSPORT 0.2998091 0.0021998
## REACTOME_SENSORY_PERCEPTION_OF_SALTY_TASTE -0.4946896 -0.0972380
## s.rheumatoid_art s.stroke
## REACTOME_ORGANIC_ANION_TRANSPORT -0.4320880 0.05112263
## REACTOME_SENSORY_PERCEPTION_OF_SALTY_TASTE 0.3603473 -0.60968441rownames(top) <- gsub("REACTOME_","",rownames(top))
rownames(top) <- gsub("_"," ",rownames(top))
colnames(top) <- gsub("^s.","",colnames(top))
colnames(top) <- gsub("_"," ",colnames(top))
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(as.matrix(top),scale="none",trace="none",margins=c(6,25),
col=colfunc(25),cexRow=0.6,cexCol=0.8)
pdf("fig8b.pdf",height=9,width=7)
heatmap.2(as.matrix(top),scale="none",trace="none",margins=c(6,22),
col=colfunc(25),cexRow=0.5,cexCol=0.8)
dev.off()## png
## 2Mitch calc incidence
Multi-comparison for incidence.
tic()
mres2 <- mitch_calc(x=m2,genesets=gs_symbols,minsetsize=5,cores=8, priority="effect")## Note: Enrichments with large effect sizes may not be
## statistically significant.toc() #29.5s## 75.28 sec elapsedmtable2 <- mres2$enrichment_result
head(mtable2,20)## set
## 898 REACTOME_ORGANIC_ANION_TRANSPORT
## 1007 REACTOME_CD22_MEDIATED_BCR_REGULATION
## 971 REACTOME_MELANIN_BIOSYNTHESIS
## 521 REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE
## 1393 REACTOME_INTERLEUKIN_36_PATHWAY
## 1055 REACTOME_METAL_SEQUESTRATION_BY_ANTIMICROBIAL_PROTEINS
## 448 REACTOME_SYNTHESIS_OF_LIPOXINS_LX
## 1578 REACTOME_REGULATION_OF_HMOX1_EXPRESSION_AND_ACTIVITY
## 421 REACTOME_REGULATION_OF_GENE_EXPRESSION_IN_ENDOCRINE_COMMITTED_NEUROG3_PROGENITOR_CELLS
## 123 REACTOME_ACYL_CHAIN_REMODELING_OF_DAG_AND_TAG
## 165 REACTOME_SULFIDE_OXIDATION_TO_SULFATE
## 1187 REACTOME_FASL_CD95L_SIGNALING
## 1420 REACTOME_NR1H2_NR1H3_REGULATE_GENE_EXPRESSION_TO_LIMIT_CHOLESTEROL_UPTAKE
## 1331 REACTOME_HDL_CLEARANCE
## 422 REACTOME_REGULATION_OF_GENE_EXPRESSION_IN_EARLY_PANCREATIC_PRECURSOR_CELLS
## 1234 REACTOME_SIGNALING_BY_MST1
## 213 REACTOME_LECTIN_PATHWAY_OF_COMPLEMENT_ACTIVATION
## 337 REACTOME_GLUCOCORTICOID_BIOSYNTHESIS
## 1532 REACTOME_DEFECTIVE_F9_ACTIVATION
## 1001 REACTOME_DEFECTIVE_CSF2RB_CAUSES_SMDP5
## setSize pMANOVA s.alzheimers s.breast_cancer s.chronic_pain
## 898 5 0.0097627461 -0.31964367 -0.03148805 -0.04161440
## 1007 5 0.0324964590 0.87512044 0.19625489 0.51446232
## 971 5 0.0178976276 0.14845923 -0.50431779 0.03128806
## 521 5 0.0546638769 -0.43710572 -0.15631306 0.07602945
## 1393 7 0.0160223666 -0.11277922 -0.28037662 -0.35148052
## 1055 6 0.0004000794 0.12253988 -0.73417875 -0.41629926
## 448 6 0.0033788284 0.21083890 0.02313531 0.24209506
## 1578 5 0.1133421398 0.09802745 0.49179166 -0.06933915
## 421 5 0.1452669652 0.11906190 0.33809654 0.45439505
## 123 5 0.1426703530 0.01047178 -0.60963549 0.08031997
## 165 6 0.0878475646 -0.24930685 -0.20105147 0.20706635
## 1187 5 0.1555265536 0.40169076 0.61303518 0.35511317
## 1420 5 0.3549335662 0.15814926 -0.24850468 0.13533315
## 1331 5 0.1617196056 -0.09733661 -0.48211981 -0.14307790
## 422 8 0.0202653474 0.25739806 0.03206964 0.27913769
## 1234 5 0.1417669637 -0.09075539 -0.27093901 -0.31528043
## 213 8 0.0823675582 -0.55388881 -0.18294013 -0.25052275
## 337 10 0.0044797814 -0.44311497 0.08776651 -0.05582579
## 1532 5 0.2322157608 0.50709935 0.18171075 0.21921643
## 1001 7 0.0128918727 -0.32993506 0.04405195 0.13567532
## s.CKD s.colorectal_can s.COPD s.covid_hospital s.diabetes
## 898 0.399818198 0.145150441 0.813307881 0.481919825 0.33904191
## 1007 -0.125843105 -0.429942732 0.068557404 -0.160839924 0.36674848
## 971 -0.327588401 0.032015271 0.077229343 -0.131933461 -0.29919098
## 521 0.105244978 0.103681484 0.704772293 0.320498137 0.38191074
## 1393 0.298298701 0.437246753 0.650662338 -0.236000000 0.04133766
## 1055 -0.353120313 0.283623472 0.179007015 -0.412753966 0.35915034
## 448 0.269578656 -0.005454298 0.211308577 -0.161507810 -0.13528173
## 1578 -0.140041814 0.033833288 -0.197509317 -0.615634942 0.11469866
## 421 0.017362058 -0.329733661 0.374911372 -0.025088628 -0.46675757
## 123 0.211780747 0.387055722 0.241868921 -0.224997727 0.58183801
## 165 0.006848174 0.123948911 -0.219474872 -0.482584125 0.50646183
## 1187 0.038796473 -0.612253432 -0.391909826 0.286701209 -0.55293155
## 1420 -0.294173257 0.193327879 -0.016271248 -0.431142623 0.57143896
## 1331 -0.033724207 0.544295973 -0.029815471 -0.148295609 0.51684392
## 422 0.317503068 -0.155677531 -0.008739034 0.003386518 -0.48490840
## 1234 -0.364439596 0.117243887 0.499645487 -0.154404145 0.60330879
## 213 0.109004955 0.183644711 0.412393745 0.154325197 0.05855948
## 337 -0.029813156 -0.229722235 0.369577670 0.607655589 -0.14606537
## 1532 0.113498773 0.397272975 -0.107844741 0.359694573 0.24652304
## 1001 -0.119363636 0.420519481 0.595701299 0.538038961 -0.17738961
## s.heart_disease s.ibd s.liver_cirrhosi s.long_covid s.lung_cancer
## 898 -0.170439051 0.18745569 -0.41038087 0.30760840 -0.51291701
## 1007 0.513626034 -0.05741296 0.29062812 -0.30299064 0.03061540
## 971 -0.290155440 -0.19243705 -0.62534315 -0.55425870 0.31938915
## 521 -0.319316426 0.80929006 -0.39190983 -0.10980820 0.41068994
## 1393 -0.054246753 0.46533766 -0.06483117 -0.09228571 0.33841558
## 1055 0.309849552 0.55029317 -0.09623805 0.06601215 0.21108131
## 448 0.179249428 0.20935412 0.24323137 0.03737709 0.40439071
## 1578 0.703426961 -0.39189165 0.33996909 -0.35245887 -0.12740660
## 421 -0.226615762 -0.27793837 -0.21063540 0.39369148 0.49740933
## 123 0.569439142 -0.32897009 0.26324880 0.13502409 0.46428506
## 165 0.434692362 -0.48793237 0.17988576 0.19497599 -0.06813327
## 1187 -0.002345241 -0.10089992 0.25055904 -0.23226979 0.33458776
## 1420 0.553240614 0.18636488 0.34976820 -0.27864740 0.21623489
## 1331 0.146550314 0.48579220 0.15854922 0.30957186 -0.12987910
## 422 -0.291285968 -0.15754125 -0.35069776 0.55004773 0.46544161
## 1234 0.625743114 -0.07302972 -0.32109808 0.26661213 0.21825289
## 213 -0.178633120 0.65796173 -0.48461294 0.02631938 0.23665848
## 337 -0.300695549 0.03526845 -0.55548484 0.08744829 -0.04184207
## 1532 0.436560313 -0.41738024 0.30660849 -0.01792564 0.25670394
## 1001 -0.072987013 0.33855844 -0.20636364 0.07089610 0.55210390
## s.osteoarthritis s.ovarian_cancer s.parkinsons s.prostate_cance
## 898 0.59969094 -0.86312153 0.04817744 0.58271066
## 1007 0.25121353 0.45014090 -0.60132715 -0.29813653
## 971 -0.23150623 -0.38638306 0.58176529 -0.24854104
## 521 0.12807927 -0.53136988 -0.13047905 0.13073357
## 1393 0.52837662 -0.39461039 -0.32990909 0.36772727
## 1055 -0.03471054 0.24467070 0.44072239 -0.21179340
## 448 -0.59803342 -0.02063543 -0.46178204 -0.34743875
## 1578 -0.37927461 0.23063358 -0.24626852 -0.09437324
## 421 0.17125716 0.07990183 0.38451050 -0.03872375
## 123 -0.35189528 0.06310335 -0.15962185 -0.08690119
## 165 -0.08566277 0.53871794 -0.27560868 -0.20873294
## 1187 0.33480593 0.19300064 0.10404509 -0.04077811
## 1420 -0.07890192 0.61112626 -0.18643760 -0.16703936
## 1331 0.06443051 0.37287519 -0.19416417 -0.49726389
## 422 -0.43748579 0.37608528 0.27178508 -0.10089095
## 1234 0.03074266 0.21710754 -0.19032815 -0.33186074
## 213 0.24540888 -0.43370153 0.08618574 0.18180372
## 337 0.49043051 -0.09618584 0.05482566 -0.19093513
## 1532 -0.18643760 0.07352059 0.04370512 -0.66117626
## 1001 0.06689610 0.10102597 0.38283117 0.13683117
## s.rheumatoid_art s.stroke p.alzheimers p.breast_cancer p.chronic_pain
## 898 0.501390783 0.16816653 0.2157957177 0.902952936 0.87197963
## 1007 0.144441414 0.42774293 0.0007009901 0.447274089 0.04634316
## 971 0.526006727 -0.38352877 0.5653695414 0.050823601 0.90356627
## 521 0.003072448 0.01359876 0.0905183385 0.544981510 0.76844393
## 1393 0.628337662 0.08841558 0.6053590815 0.198934247 0.10731364
## 1055 0.366892414 -0.39914852 0.6032072365 0.001841956 0.07740765
## 448 -0.546702423 0.78828235 0.3711351490 0.921824404 0.30445047
## 1578 -0.173184256 0.45428597 0.7042463576 0.056850978 0.78831152
## 421 -0.302427052 -0.65935824 0.6447646653 0.190451501 0.07847116
## 123 -0.042068903 0.39238251 0.9676545649 0.018232846 0.75578143
## 165 -0.629289578 0.21206612 0.2902704310 0.393752780 0.37975590
## 1187 0.065703118 0.06133988 0.1198224495 0.017595480 0.16908724
## 1420 -0.428197437 0.08359240 0.5402680590 0.335898621 0.60024003
## 1331 -0.269520953 0.45312244 0.7062334865 0.061901904 0.57954748
## 422 -0.404427474 -0.08789036 0.2074134334 0.875188838 0.17156494
## 1234 -0.166348514 -0.01181711 0.7252622564 0.294096861 0.22212826
## 213 0.249920451 -0.14216555 0.0066663132 0.370245112 0.21980981
## 337 0.481920262 -0.20202755 0.0152446769 0.630813807 0.75984529
## 1532 -0.034505954 0.20150895 0.0495603995 0.481652784 0.39594591
## 1001 0.074935065 -0.31948052 0.1306190283 0.840051264 0.53419770
## p.CKD p.colorectal_can p.COPD p.covid_hospital p.diabetes
## 898 0.1215579 0.57406733 0.001634124 0.0620101370 0.18921476
## 1007 0.6260411 0.09592678 0.790642295 0.5333982510 0.15554910
## 971 0.2046011 0.90133625 0.764896330 0.6094277057 0.24662665
## 521 0.6836090 0.68806016 0.006346177 0.2145709709 0.13916207
## 1393 0.1717161 0.04513703 0.002869648 0.2795775648 0.84978547
## 1055 0.1341563 0.22893767 0.447660708 0.0799649354 0.12763572
## 448 0.2528221 0.98154167 0.370070448 0.4932865439 0.56607400
## 1578 0.5876194 0.89576466 0.444375740 0.0171213432 0.65693319
## 421 0.9463974 0.20165203 0.146553653 0.9226062479 0.07068384
## 123 0.4121649 0.13391432 0.348962286 0.3836059409 0.02424686
## 165 0.9768257 0.59904747 0.351862687 0.0406450174 0.03167935
## 1187 0.8805812 0.01774029 0.129104850 0.2669062425 0.03225458
## 1420 0.2546403 0.45407854 0.949760502 0.0950031758 0.02690319
## 1331 0.8960988 0.03504860 0.908084356 0.5657981595 0.04534091
## 422 0.1199192 0.44577112 0.965859263 0.9867664768 0.01754221
## 1234 0.1581680 0.64982357 0.053006103 0.5499031830 0.01947292
## 213 0.5934195 0.36840487 0.043391671 0.4497328308 0.77425511
## 337 0.8703239 0.20842584 0.042992843 0.0008755351 0.42381997
## 1532 0.6602957 0.12394820 0.676231888 0.1636547475 0.33976644
## 1001 0.5844634 0.05401358 0.006343274 0.0136920631 0.41637176
## p.heart_disease p.ibd p.liver_cirrhosi p.long_covid p.lung_cancer
## 898 0.509254863 0.467904134 0.112021328 0.233583557 0.04700340
## 1007 0.046699492 0.824063475 0.260410135 0.240677093 0.90562971
## 971 0.261186231 0.456160981 0.015448427 0.031842572 0.21616151
## 521 0.216266100 0.001723336 0.129104850 0.670680875 0.11175144
## 1393 0.803719433 0.032999046 0.766443674 0.672429624 0.12101612
## 1055 0.188727695 0.019575319 0.683108212 0.779468298 0.37058539
## 448 0.447045974 0.374513463 0.302186208 0.874025805 0.08626999
## 1578 0.006447184 0.129122609 0.188007555 0.172295924 0.62175759
## 421 0.380194926 0.281801419 0.414697747 0.127373625 0.05407804
## 123 0.027441864 0.202698123 0.308014766 0.601072684 0.07218843
## 165 0.065189178 0.038468954 0.445434582 0.408203210 0.77257417
## 1187 0.992754085 0.696005847 0.331918899 0.368422590 0.19509377
## 1420 0.032158229 0.470497798 0.175594825 0.280575647 0.40240256
## 1331 0.570379768 0.059941980 0.539244078 0.230612757 0.61500974
## 422 0.153667385 0.440343994 0.085849737 0.007054683 0.02262077
## 1234 0.015382703 0.777334012 0.213714042 0.301874997 0.39802564
## 213 0.381618076 0.001268767 0.017611120 0.897430955 0.24640476
## 337 0.099648455 0.846866203 0.002350342 0.632053017 0.81878070
## 1532 0.090921328 0.106033956 0.235106847 0.944660172 0.32019764
## 1001 0.738080196 0.120859221 0.344412617 0.745319557 0.01141615
## p.osteoarthritis p.ovarian_cancer p.parkinsons p.prostate_cance
## 898 0.020214660 0.0008296455 0.85200665 0.02403466
## 1007 0.330657450 0.0813071903 0.01987620 0.24829583
## 971 0.369999040 0.1345915931 0.02426462 0.33582792
## 521 0.619918596 0.0396155802 0.61337726 0.61268528
## 1393 0.015479391 0.0706051298 0.13064932 0.09202163
## 1055 0.882945778 0.2993342680 0.06154774 0.36897339
## 448 0.011182726 0.9302488124 0.05012657 0.14053289
## 1578 0.141911480 0.3718058216 0.34026529 0.71477981
## 421 0.507223980 0.7570126686 0.13649092 0.88080333
## 123 0.172983043 0.8069545272 0.53650275 0.73648635
## 165 0.716330957 0.0222963662 0.24236296 0.37593246
## 1187 0.194802736 0.4548428920 0.68702402 0.87453081
## 1420 0.759959428 0.0179509122 0.47032464 0.51773971
## 1331 0.802977187 0.1487593607 0.45212847 0.05414838
## 422 0.032126397 0.0654655662 0.18313022 0.62120297
## 1234 0.905239277 0.4005063046 0.46111219 0.19875866
## 213 0.229368956 0.0336453221 0.67293577 0.37322526
## 337 0.007238764 0.5984133300 0.76401887 0.29578699
## 1532 0.470324641 0.7758772404 0.86560755 0.01045280
## 1001 0.759231122 0.6434644535 0.07942443 0.53072043
## p.rheumatoid_art p.stroke s.dist SD p.adjustMANOVA
## 898 0.052181858 0.5149184711 1.900249 0.4314334 0.043442897
## 1007 0.575939353 0.0976386973 1.673063 0.3767668 0.114998245
## 971 0.041653633 0.1374949184 1.564275 0.3438913 0.072742338
## 521 0.990507383 0.9580037563 1.553280 0.3611464 0.172944289
## 1393 0.003988694 0.6854154660 1.550521 0.3502816 0.066268831
## 1055 0.119628477 0.0904227571 1.521502 0.3576982 0.002546242
## 448 0.020386595 0.0008252684 1.475699 0.3465885 0.017612813
## 1578 0.502456985 0.0785428557 1.454509 0.3423921 0.302614298
## 421 0.241553120 0.0106665429 1.445577 0.3403565 0.361406601
## 123 0.870593671 0.1286437737 1.439684 0.3281687 0.357124482
## 165 0.007595294 0.3683571909 1.434760 0.3370802 0.252826170
## 1187 0.799168083 0.8122472020 1.410650 0.3270706 0.376658703
## 1420 0.097283008 0.7461672958 1.400691 0.3262996 0.642558893
## 1331 0.296631057 0.0793109758 1.396861 0.3245276 0.386526335
## 422 0.047602765 0.6668528760 1.358484 0.3201732 0.080020090
## 1234 0.519472803 0.9635018094 1.343887 0.3163733 0.357026617
## 213 0.220920575 0.4862385881 1.331837 0.3132589 0.240226520
## 337 0.008313992 0.2686176496 1.326620 0.3126598 0.022462336
## 1532 0.893704460 0.4352065260 1.325428 0.2946817 0.493520133
## 1001 0.731356272 0.1432586361 1.325190 0.2880779 0.054557873#numsig manova
nrow(subset(mtable2,p.adjustMANOVA<0.05))## [1] 382sig <- subset(mtable2,p.adjustMANOVA<0.05)Individual comparisons.
r1 <- lapply(l2,function(m) {
m2 <- mitch_import(x=m,DEtype="prescored",geneTable=gt,geneIDcol="CpG")
mres <- mitch_calc(x=m2,genesets=gs_symbols,minsetsize=5,cores=8, priority="effect")
mtable <- mres$enrichment_result
})## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 752722## Note: no. genes in output = 22007## Warning in mitch_import(x = m, DEtype = "prescored", geneTable = gt, geneIDcol = "CpG"): Warning: less than half of the input genes are also in the
## output## Note: Enrichments with large effect sizes may not be
## statistically significant.nup <- lapply(r1,function(r) {
ups <- subset(r,p.adjustANOVA<0.05 & s.dist>0)$set
length(ups)
} )
ndn <- lapply(r1,function(r) {
dns <- subset(r,p.adjustANOVA<0.05 & s.dist<0)$set
length(dns)
} )
nums <- cbind(unlist(ndn),unlist(nup))
colnames(nums) <- c("dn","up")
nums## dn up
## alzheimers 1 0
## breast_cancer 11 53
## chronic_pain 8 93
## CKD 1 7
## colorectal_cancer 115 4
## COPD 306 94
## covid_hospitalisation 0 0
## diabetes 69 10
## heart_disease 11 0
## ibd 8 6
## liver_cirrhosis 85 379
## long_covid 4 15
## lung_cancer 2 4
## osteoarthritis 1 3
## ovarian_cancer 44 126
## parkinsons 5 48
## prostate_cancer 5 16
## rheumatoid_arthritis 1 5
## stroke 20 10rownames(nums) <- gsub("ibd","IBD",rownames(nums))
rownames(nums) <- gsub("_"," ",rownames(nums))
par(mar = c(5.1, 10.1, 4.1, 2.1))
barplot(t(nums),beside=TRUE,horiz=TRUE,las=1,
legend.text = c("down","up"),xlab="no. pathways")
abline(v=seq(0,200,50),lty=2,lwd=0.5,col="gray")
pdf("fig8c.pdf",height=6,width=4)
par(mar = c(5.1, 10.1, 4.1, 2.1))
barplot(t(nums),beside=TRUE,horiz=TRUE,las=1,
legend.text = c("down","up"),xlab="no. pathways")
abline(v=seq(0,200,50),lty=2,lwd=0.5,col="gray")
dev.off()## png
## 2r2 <- lapply(r1,function(r) {
ups <- head(subset(r,p.adjustANOVA<0.05 & s.dist > 0.3)$set,3)
dns <- head(subset(r,p.adjustANOVA<0.05 & s.dist < -0.3)$set,3)
list("ups"=ups,"dns"=dns)
} )
r2## $alzheimers
## $alzheimers$ups
## character(0)
##
## $alzheimers$dns
## character(0)
##
##
## $breast_cancer
## $breast_cancer$ups
## [1] "REACTOME_SARS_COV_2_MODULATES_HOST_TRANSLATION_MACHINERY"
## [2] "REACTOME_CALNEXIN_CALRETICULIN_CYCLE"
## [3] "REACTOME_SARS_COV_1_ACTIVATES_MODULATES_INNATE_IMMUNE_RESPONSES"
##
## $breast_cancer$dns
## [1] "REACTOME_METAL_SEQUESTRATION_BY_ANTIMICROBIAL_PROTEINS"
##
##
## $chronic_pain
## $chronic_pain$ups
## [1] "REACTOME_SIGNALING_BY_CSF3_G_CSF"
## [2] "REACTOME_INSULIN_RECEPTOR_RECYCLING"
## [3] "REACTOME_ONCOGENE_INDUCED_SENESCENCE"
##
## $chronic_pain$dns
## [1] "REACTOME_CLASS_C_3_METABOTROPIC_GLUTAMATE_PHEROMONE_RECEPTORS"
##
##
## $CKD
## $CKD$ups
## [1] "REACTOME_CHEMOKINE_RECEPTORS_BIND_CHEMOKINES"
##
## $CKD$dns
## character(0)
##
##
## $colorectal_cancer
## $colorectal_cancer$ups
## character(0)
##
## $colorectal_cancer$dns
## [1] "REACTOME_SARS_COV_1_MODULATES_HOST_TRANSLATION_MACHINERY"
## [2] "REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION"
## [3] "REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE"
##
##
## $COPD
## $COPD$ups
## [1] "REACTOME_ORGANIC_ANION_TRANSPORT"
## [2] "REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE"
## [3] "REACTOME_HIGHLY_SODIUM_PERMEABLE_POSTSYNAPTIC_ACETYLCHOLINE_NICOTINIC_RECEPTORS"
##
## $COPD$dns
## [1] "REACTOME_RUNX1_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_INTERLEUKIN_SIGNALING"
## [2] "REACTOME_SYNTHESIS_OF_GDP_MANNOSE"
## [3] "REACTOME_TLR3_MEDIATED_TICAM1_DEPENDENT_PROGRAMMED_CELL_DEATH"
##
##
## $covid_hospitalisation
## $covid_hospitalisation$ups
## character(0)
##
## $covid_hospitalisation$dns
## character(0)
##
##
## $diabetes
## $diabetes$ups
## [1] "REACTOME_STRIATED_MUSCLE_CONTRACTION"
## [2] "REACTOME_REGULATION_OF_TP53_ACTIVITY_THROUGH_ACETYLATION"
##
## $diabetes$dns
## [1] "REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION"
## [2] "REACTOME_SARS_COV_1_MODULATES_HOST_TRANSLATION_MACHINERY"
## [3] "REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE"
##
##
## $heart_disease
## $heart_disease$ups
## character(0)
##
## $heart_disease$dns
## character(0)
##
##
## $ibd
## $ibd$ups
## [1] "REACTOME_CHEMOKINE_RECEPTORS_BIND_CHEMOKINES"
##
## $ibd$dns
## [1] "REACTOME_INTERLEUKIN_12_SIGNALING"
## [2] "REACTOME_GENE_AND_PROTEIN_EXPRESSION_BY_JAK_STAT_SIGNALING_AFTER_INTERLEUKIN_12_STIMULATION"
## [3] "REACTOME_INTERLEUKIN_12_FAMILY_SIGNALING"
##
##
## $liver_cirrhosis
## $liver_cirrhosis$ups
## [1] "REACTOME_BETA_OXIDATION_OF_OCTANOYL_COA_TO_HEXANOYL_COA"
## [2] "REACTOME_BETA_OXIDATION_OF_DECANOYL_COA_TO_OCTANOYL_COA_COA"
## [3] "REACTOME_ZINC_INFLUX_INTO_CELLS_BY_THE_SLC39_GENE_FAMILY"
##
## $liver_cirrhosis$dns
## [1] "REACTOME_CYP2E1_REACTIONS"
## [2] "REACTOME_DIGESTION_OF_DIETARY_LIPID"
## [3] "REACTOME_ERYTHROCYTES_TAKE_UP_OXYGEN_AND_RELEASE_CARBON_DIOXIDE"
##
##
## $long_covid
## $long_covid$ups
## [1] "REACTOME_NEGATIVE_REGULATION_OF_FGFR1_SIGNALING"
##
## $long_covid$dns
## [1] "REACTOME_CLASS_C_3_METABOTROPIC_GLUTAMATE_PHEROMONE_RECEPTORS"
## [2] "REACTOME_SENSORY_PERCEPTION_OF_TASTE"
##
##
## $lung_cancer
## $lung_cancer$ups
## character(0)
##
## $lung_cancer$dns
## character(0)
##
##
## $osteoarthritis
## $osteoarthritis$ups
## character(0)
##
## $osteoarthritis$dns
## character(0)
##
##
## $ovarian_cancer
## $ovarian_cancer$ups
## [1] "REACTOME_PROSTANOID_LIGAND_RECEPTORS"
## [2] "REACTOME_ATF6_ATF6_ALPHA_ACTIVATES_CHAPERONE_GENES"
## [3] "REACTOME_INTERLEUKIN_6_SIGNALING"
##
## $ovarian_cancer$dns
## [1] "REACTOME_ORGANIC_ANION_TRANSPORT" "REACTOME_ACTIVATION_OF_C3_AND_C5"
## [3] "REACTOME_PREDNISONE_ADME"
##
##
## $parkinsons
## $parkinsons$ups
## [1] "REACTOME_SCAVENGING_BY_CLASS_A_RECEPTORS"
## [2] "REACTOME_BETA_DEFENSINS"
## [3] "REACTOME_SIRT1_NEGATIVELY_REGULATES_RRNA_EXPRESSION"
##
## $parkinsons$dns
## [1] "REACTOME_PASSIVE_TRANSPORT_BY_AQUAPORINS"
## [2] "REACTOME_AQUAPORIN_MEDIATED_TRANSPORT"
##
##
## $prostate_cancer
## $prostate_cancer$ups
## [1] "REACTOME_SARS_COV_1_MODULATES_HOST_TRANSLATION_MACHINERY"
## [2] "REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION"
## [3] "REACTOME_SARS_COV_2_MODULATES_HOST_TRANSLATION_MACHINERY"
##
## $prostate_cancer$dns
## [1] "REACTOME_SIGNALING_BY_CYTOSOLIC_FGFR1_FUSION_MUTANTS"
## [2] "REACTOME_FGFR1_MUTANT_RECEPTOR_ACTIVATION"
## [3] "REACTOME_SIGNALING_BY_FGFR1_IN_DISEASE"
##
##
## $rheumatoid_arthritis
## $rheumatoid_arthritis$ups
## character(0)
##
## $rheumatoid_arthritis$dns
## character(0)
##
##
## $stroke
## $stroke$ups
## [1] "REACTOME_SYNTHESIS_OF_LIPOXINS_LX"
## [2] "REACTOME_SIGNALING_BY_NODAL"
## [3] "REACTOME_THE_ROLE_OF_NEF_IN_HIV_1_REPLICATION_AND_DISEASE_PATHOGENESIS"
##
## $stroke$dns
## [1] "REACTOME_EUKARYOTIC_TRANSLATION_INITIATION"
## [2] "REACTOME_SARS_COV_2_MODULATES_HOST_TRANSLATION_MACHINERY"
## [3] "REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE"gsets <- unique(unname(unlist(r2)))
gsets## [1] "REACTOME_SARS_COV_2_MODULATES_HOST_TRANSLATION_MACHINERY"
## [2] "REACTOME_CALNEXIN_CALRETICULIN_CYCLE"
## [3] "REACTOME_SARS_COV_1_ACTIVATES_MODULATES_INNATE_IMMUNE_RESPONSES"
## [4] "REACTOME_METAL_SEQUESTRATION_BY_ANTIMICROBIAL_PROTEINS"
## [5] "REACTOME_SIGNALING_BY_CSF3_G_CSF"
## [6] "REACTOME_INSULIN_RECEPTOR_RECYCLING"
## [7] "REACTOME_ONCOGENE_INDUCED_SENESCENCE"
## [8] "REACTOME_CLASS_C_3_METABOTROPIC_GLUTAMATE_PHEROMONE_RECEPTORS"
## [9] "REACTOME_CHEMOKINE_RECEPTORS_BIND_CHEMOKINES"
## [10] "REACTOME_SARS_COV_1_MODULATES_HOST_TRANSLATION_MACHINERY"
## [11] "REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION"
## [12] "REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE"
## [13] "REACTOME_ORGANIC_ANION_TRANSPORT"
## [14] "REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE"
## [15] "REACTOME_HIGHLY_SODIUM_PERMEABLE_POSTSYNAPTIC_ACETYLCHOLINE_NICOTINIC_RECEPTORS"
## [16] "REACTOME_RUNX1_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_INTERLEUKIN_SIGNALING"
## [17] "REACTOME_SYNTHESIS_OF_GDP_MANNOSE"
## [18] "REACTOME_TLR3_MEDIATED_TICAM1_DEPENDENT_PROGRAMMED_CELL_DEATH"
## [19] "REACTOME_STRIATED_MUSCLE_CONTRACTION"
## [20] "REACTOME_REGULATION_OF_TP53_ACTIVITY_THROUGH_ACETYLATION"
## [21] "REACTOME_INTERLEUKIN_12_SIGNALING"
## [22] "REACTOME_GENE_AND_PROTEIN_EXPRESSION_BY_JAK_STAT_SIGNALING_AFTER_INTERLEUKIN_12_STIMULATION"
## [23] "REACTOME_INTERLEUKIN_12_FAMILY_SIGNALING"
## [24] "REACTOME_BETA_OXIDATION_OF_OCTANOYL_COA_TO_HEXANOYL_COA"
## [25] "REACTOME_BETA_OXIDATION_OF_DECANOYL_COA_TO_OCTANOYL_COA_COA"
## [26] "REACTOME_ZINC_INFLUX_INTO_CELLS_BY_THE_SLC39_GENE_FAMILY"
## [27] "REACTOME_CYP2E1_REACTIONS"
## [28] "REACTOME_DIGESTION_OF_DIETARY_LIPID"
## [29] "REACTOME_ERYTHROCYTES_TAKE_UP_OXYGEN_AND_RELEASE_CARBON_DIOXIDE"
## [30] "REACTOME_NEGATIVE_REGULATION_OF_FGFR1_SIGNALING"
## [31] "REACTOME_SENSORY_PERCEPTION_OF_TASTE"
## [32] "REACTOME_PROSTANOID_LIGAND_RECEPTORS"
## [33] "REACTOME_ATF6_ATF6_ALPHA_ACTIVATES_CHAPERONE_GENES"
## [34] "REACTOME_INTERLEUKIN_6_SIGNALING"
## [35] "REACTOME_ACTIVATION_OF_C3_AND_C5"
## [36] "REACTOME_PREDNISONE_ADME"
## [37] "REACTOME_SCAVENGING_BY_CLASS_A_RECEPTORS"
## [38] "REACTOME_BETA_DEFENSINS"
## [39] "REACTOME_SIRT1_NEGATIVELY_REGULATES_RRNA_EXPRESSION"
## [40] "REACTOME_PASSIVE_TRANSPORT_BY_AQUAPORINS"
## [41] "REACTOME_AQUAPORIN_MEDIATED_TRANSPORT"
## [42] "REACTOME_SIGNALING_BY_CYTOSOLIC_FGFR1_FUSION_MUTANTS"
## [43] "REACTOME_FGFR1_MUTANT_RECEPTOR_ACTIVATION"
## [44] "REACTOME_SIGNALING_BY_FGFR1_IN_DISEASE"
## [45] "REACTOME_SYNTHESIS_OF_LIPOXINS_LX"
## [46] "REACTOME_SIGNALING_BY_NODAL"
## [47] "REACTOME_THE_ROLE_OF_NEF_IN_HIV_1_REPLICATION_AND_DISEASE_PATHOGENESIS"
## [48] "REACTOME_EUKARYOTIC_TRANSLATION_INITIATION"par(mar = c(5.1, 4.1, 4.1, 2.1))Make a heatmap with these gene sets.
top <- mtable2[which(mtable2$set %in% gsets),]
top <- top[,c(1,4:17)]
rownames(top) <- top$set
top$set=NULL
head(top,2)## s.alzheimers
## REACTOME_ORGANIC_ANION_TRANSPORT -0.3196437
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE -0.4371057
## s.breast_cancer
## REACTOME_ORGANIC_ANION_TRANSPORT -0.03148805
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE -0.15631306
## s.chronic_pain
## REACTOME_ORGANIC_ANION_TRANSPORT -0.04161440
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE 0.07602945
## s.CKD
## REACTOME_ORGANIC_ANION_TRANSPORT 0.3998182
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE 0.1052450
## s.colorectal_can
## REACTOME_ORGANIC_ANION_TRANSPORT 0.1451504
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE 0.1036815
## s.COPD
## REACTOME_ORGANIC_ANION_TRANSPORT 0.8133079
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE 0.7047723
## s.covid_hospital
## REACTOME_ORGANIC_ANION_TRANSPORT 0.4819198
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE 0.3204981
## s.diabetes
## REACTOME_ORGANIC_ANION_TRANSPORT 0.3390419
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE 0.3819107
## s.heart_disease
## REACTOME_ORGANIC_ANION_TRANSPORT -0.1704391
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE -0.3193164
## s.ibd
## REACTOME_ORGANIC_ANION_TRANSPORT 0.1874557
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE 0.8092901
## s.liver_cirrhosi
## REACTOME_ORGANIC_ANION_TRANSPORT -0.4103809
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE -0.3919098
## s.long_covid
## REACTOME_ORGANIC_ANION_TRANSPORT 0.3076084
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE -0.1098082
## s.lung_cancer
## REACTOME_ORGANIC_ANION_TRANSPORT -0.5129170
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE 0.4106899
## s.osteoarthritis
## REACTOME_ORGANIC_ANION_TRANSPORT 0.5996909
## REACTOME_FICOLINS_BIND_TO_REPETITIVE_CARBOHYDRATE_STRUCTURES_ON_THE_TARGET_CELL_SURFACE 0.1280793rownames(top) <- gsub("REACTOME_","",rownames(top))
rownames(top) <- gsub("_"," ",rownames(top))
colnames(top) <- gsub("^s.","",colnames(top))
colnames(top) <- gsub("_"," ",colnames(top))
colnames(top) <- gsub("ibd","IBD",colnames(top))
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(as.matrix(top),scale="none",trace="none",margins=c(6,22),
col=colfunc(25),cexRow=0.6,cexCol=0.7)
pdf("fig8d.pdf",height=9,width=7)
heatmap.2(as.matrix(top),scale="none",trace="none",margins=c(6,20),
col=colfunc(25),cexRow=0.5,cexCol=0.8)
dev.off()## png
## 2Session information
save.image("multi_ewas_example.Rdata")
sessionInfo()## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] png_0.1-8
## [2] gridExtra_2.3
## [3] missMethyl_1.36.0
## [4] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
## [5] beeswarm_0.4.0
## [6] kableExtra_1.3.4
## [7] gplots_3.1.3
## [8] mitch_1.15.0
## [9] tictoc_1.2
## [10] HGNChelper_0.8.1
## [11] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [12] IlluminaHumanMethylation450kmanifest_0.4.0
## [13] minfi_1.48.0
## [14] bumphunter_1.44.0
## [15] locfit_1.5-9.8
## [16] iterators_1.0.14
## [17] foreach_1.5.2
## [18] Biostrings_2.70.1
## [19] XVector_0.42.0
## [20] SummarizedExperiment_1.32.0
## [21] Biobase_2.62.0
## [22] MatrixGenerics_1.14.0
## [23] matrixStats_1.2.0
## [24] GenomicRanges_1.54.1
## [25] GenomeInfoDb_1.38.2
## [26] IRanges_2.36.0
## [27] S4Vectors_0.40.2
## [28] BiocGenerics_0.48.1
## [29] eulerr_7.0.0
## [30] limma_3.58.1
##
## loaded via a namespace (and not attached):
## [1] splines_4.3.2 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
## [9] lifecycle_1.0.4 lattice_0.22-5
## [11] MASS_7.3-60 base64_2.0.1
## [13] scrime_1.3.5 magrittr_2.0.3
## [15] sass_0.4.8 rmarkdown_2.25
## [17] jquerylib_0.1.4 yaml_2.3.8
## [19] httpuv_1.6.13 doRNG_1.8.6
## [21] askpass_1.2.0 DBI_1.1.3
## [23] RColorBrewer_1.1-3 abind_1.4-5
## [25] zlibbioc_1.48.0 rvest_1.0.3
## [27] quadprog_1.5-8 purrr_1.0.2
## [29] RCurl_1.98-1.13 rappdirs_0.3.3
## [31] GenomeInfoDbData_1.2.11 genefilter_1.84.0
## [33] annotate_1.80.0 svglite_2.1.3
## [35] DelayedMatrixStats_1.24.0 codetools_0.2-19
## [37] DelayedArray_0.28.0 xml2_1.3.6
## [39] tidyselect_1.2.0 beanplot_1.3.1
## [41] BiocFileCache_2.10.1 webshot_0.5.5
## [43] illuminaio_0.44.0 GenomicAlignments_1.38.0
## [45] jsonlite_1.8.8 multtest_2.58.0
## [47] ellipsis_0.3.2 survival_3.5-7
## [49] systemfonts_1.0.5 tools_4.3.2
## [51] progress_1.2.3 Rcpp_1.0.11
## [53] glue_1.6.2 SparseArray_1.2.2
## [55] xfun_0.41 dplyr_1.1.4
## [57] HDF5Array_1.30.0 fastmap_1.1.1
## [59] GGally_2.2.0 rhdf5filters_1.14.1
## [61] fansi_1.0.6 openssl_2.1.1
## [63] caTools_1.18.2 digest_0.6.33
## [65] R6_2.5.1 mime_0.12
## [67] colorspace_2.1-0 gtools_3.9.5
## [69] biomaRt_2.58.0 RSQLite_2.3.4
## [71] utf8_1.2.4 tidyr_1.3.0
## [73] generics_0.1.3 data.table_1.14.10
## [75] rtracklayer_1.62.0 prettyunits_1.2.0
## [77] httr_1.4.7 htmlwidgets_1.6.4
## [79] S4Arrays_1.2.0 ggstats_0.5.1
## [81] pkgconfig_2.0.3 gtable_0.3.4
## [83] blob_1.2.4 siggenes_1.76.0
## [85] htmltools_0.5.7 echarts4r_0.4.5
## [87] scales_1.3.0 rstudioapi_0.15.0
## [89] knitr_1.45 reshape2_1.4.4
## [91] tzdb_0.4.0 rjson_0.2.21
## [93] nlme_3.1-163 curl_5.2.0
## [95] org.Hs.eg.db_3.18.0 cachem_1.0.8
## [97] rhdf5_2.46.1 stringr_1.5.1
## [99] KernSmooth_2.23-22 AnnotationDbi_1.64.1
## [101] restfulr_0.0.15 GEOquery_2.70.0
## [103] pillar_1.9.0 grid_4.3.2
## [105] reshape_0.8.9 vctrs_0.6.5
## [107] promises_1.2.1 dbplyr_2.4.0
## [109] xtable_1.8-4 evaluate_0.23
## [111] readr_2.1.4 GenomicFeatures_1.54.1
## [113] cli_3.6.2 compiler_4.3.2
## [115] Rsamtools_2.18.0 rlang_1.1.2
## [117] crayon_1.5.2 rngtools_1.5.2
## [119] nor1mix_1.3-2 mclust_6.0.1
## [121] plyr_1.8.9 stringi_1.8.3
## [123] viridisLite_0.4.2 BiocParallel_1.36.0
## [125] munsell_0.5.0 Matrix_1.6-1.1
## [127] hms_1.1.3 sparseMatrixStats_1.14.0
## [129] bit64_4.0.5 ggplot2_3.4.4
## [131] Rhdf5lib_1.24.1 KEGGREST_1.42.0
## [133] statmod_1.5.0 shiny_1.8.0
## [135] highr_0.10 memoise_2.0.1
## [137] bslib_0.6.1 bit_4.0.5