Multi-EWAS enrichment example
The GMEA team
2024-07-21
Source: https://github.com/markziemann/gmea/
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
GO sets 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")
}
getOption('timeout')## [1] 60options(timeout=180)
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_","",myfiles1))
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("c5.go.v2023.2.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## 21.644 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()## 372.899 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## 110.023 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## 155.456 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## 216.195 sec elapsedmtable1 <- mres1$enrichment_result
head(mtable1,20)## set
## 7530 GOCC_ALPHA_BETA_T_CELL_RECEPTOR_COMPLEX
## 7602 GOCC_B_CELL_RECEPTOR_COMPLEX
## 2468 GOBP_MICROGLIAL_CELL_ACTIVATION_INVOLVED_IN_IMMUNE_RESPONSE
## 2469 GOBP_MICROGLIAL_CELL_MEDIATED_CYTOTOXICITY
## 7854 GOCC_FC_RECEPTOR_COMPLEX
## 8066 GOCC_MULTIVESICULAR_BODY_LUMEN
## 8778 GOMF_CCR1_CHEMOKINE_RECEPTOR_BINDING
## 9129 GOMF_HEMOGLOBIN_ALPHA_BINDING
## 9790 GOMF_PROTEINASE_ACTIVATED_RECEPTOR_ACTIVITY
## 8593 GOMF_ALKANE_1_MONOOXYGENASE_ACTIVITY
## 7031 GOBP_STEM_CELL_FATE_SPECIFICATION
## 9206 GOMF_IGG_BINDING
## 9243 GOMF_INTERLEUKIN_2_RECEPTOR_BINDING
## 7500 GOBP_XENOBIOTIC_GLUCURONIDATION
## 1557 GOBP_FOREBRAIN_NEURON_FATE_COMMITMENT
## 1537 GOBP_FLAVONOID_GLUCURONIDATION
## 9792 GOMF_PROTEIN_ARGININE_DEIMINASE_ACTIVITY
## 1638 GOBP_GLIAL_CELL_FATE_SPECIFICATION
## 815 GOBP_CEREBELLAR_PURKINJE_CELL_GRANULE_CELL_PRECURSOR_CELL_SIGNALING_INVOLVED_IN_REGULATION_OF_GRANULE_CELL_PRECURSOR_CELL_PROLIFERATION
## 5063 GOBP_PROXIMAL_DISTAL_PATTERN_FORMATION_INVOLVED_IN_NEPHRON_DEVELOPMENT
## setSize pMANOVA s.alzheimers s.breast_cancer s.chronic_pain
## 7530 5 8.306666e-07 -0.4578856 -0.01768930 -0.45937642
## 7602 5 1.813229e-03 -0.4789746 -0.20818107 0.51578947
## 2468 5 6.565220e-04 -0.7558949 0.31526225 0.60030906
## 2469 5 1.126888e-04 -0.1648577 0.14118716 0.75969457
## 7854 5 1.407369e-05 -0.6021816 0.29084629 0.37892919
## 8066 6 3.461516e-04 -0.8896414 0.89223217 -0.36748330
## 8778 5 1.497541e-03 -0.5642578 -0.53658758 0.52558858
## 9129 5 5.128000e-06 -0.2866648 -0.05695846 -0.65730388
## 9790 5 2.363170e-03 0.6366330 -0.73311517 -0.05197709
## 8593 5 8.015867e-04 -0.1444232 0.56174893 -0.13426052
## 7031 6 2.617250e-03 0.3192885 -0.43543475 0.80369074
## 9206 7 2.677852e-06 -0.3904416 -0.14909091 0.32938961
## 9243 5 6.522409e-04 0.6367603 -0.52313426 -0.21352604
## 7500 6 9.385950e-05 -0.4821902 -0.28913837 -0.43585898
## 1557 7 1.899548e-03 0.1503117 -0.48776623 0.30851948
## 1537 5 4.371169e-03 -0.3988546 -0.22457958 -0.34667757
## 9792 5 2.425725e-02 -0.4425052 0.47341151 -0.40299973
## 1638 7 1.615875e-03 -0.2029740 -0.66303896 0.58357143
## 815 5 3.692451e-02 -0.2792110 -0.28030179 0.54920462
## 5063 5 6.958514e-03 0.1820744 -0.46021271 0.46770294
## s.CKD s.colorectal_can s.COPD s.diabetes s.heart_disease
## 7530 0.79643669 0.35034997 0.7049359 0.90944460 0.77552950
## 7602 0.49404600 -0.18878284 0.1141169 0.56902100 0.56427597
## 2468 -0.55674939 -0.78885556 -0.2107263 -0.31935279 -0.08868285
## 2469 -0.72926098 -0.39561858 -0.5693119 -0.43094264 -0.26724843
## 7854 -0.18700118 -0.48141078 -0.1086628 0.59700027 0.45666758
## 8066 -0.01534779 0.01107525 0.6101995 0.02901383 -0.28301744
## 8778 0.03708754 -0.78961913 -0.1316789 0.01336242 0.21197164
## 9129 0.15224071 -0.79103718 0.5051177 -0.80483592 -0.40374511
## 9790 -0.23757840 -0.60956277 -0.6116898 -0.59156440 -0.11149895
## 8593 0.43554222 -0.12442505 0.9386783 -0.08275611 0.13455140
## 7031 0.22829265 -0.47850855 -0.5288547 0.20924806 0.37096799
## 9206 -0.71309091 -0.43596104 -0.5394935 -0.55381818 -0.18510390
## 9243 0.34725934 -0.65808563 -0.1405145 -0.26426688 -0.37020271
## 7500 0.06928473 0.11460085 0.6397739 -0.31775828 -0.56767117
## 1557 0.57774026 -0.32950649 -0.4150000 0.27553247 0.68118182
## 1537 -0.04643214 0.22781565 0.6595946 -0.49584583 -0.49410054
## 9792 -0.26023089 0.38372875 0.3660576 0.20759931 -0.31968003
## 1638 0.27902597 -0.36433766 -0.5231299 0.27874026 0.44442857
## 815 0.70686301 -0.71513499 -0.2251432 -0.05263158 0.51226252
## 5063 0.05161349 -0.30335424 -0.2616126 -0.24415962 0.60787201
## s.lung_cancer s.osteoarthritis s.parkinsons s.prostate_cance
## 7530 0.58687392 0.13418780 0.24415962 0.46088537
## 7602 0.36943914 0.67579311 -0.25414053 0.44366876
## 2468 -0.51906190 0.46781202 -0.48408327 0.05979456
## 2469 0.08497409 0.28148350 -0.22463412 -0.69124625
## 7854 0.49710026 0.46553950 -0.07959276 -0.81408963
## 8066 -0.47209975 0.32930321 -0.14727361 0.56333803
## 8778 -0.58494682 0.65506772 -0.41061722 -0.12664303
## 9129 -0.19592764 -0.08348332 -0.06559404 0.55909463
## 9790 -0.43296064 -0.51728025 -0.22063449 -0.49200982
## 8593 -0.19883647 0.20563585 0.52995182 0.91846196
## 7031 0.16829538 -0.73352726 -0.39780010 -0.22720179
## 9206 0.15207792 -0.20963636 -0.25242857 -0.51103896
## 9243 -0.37689301 -0.28999182 -0.41070812 -0.53127897
## 7500 0.12547914 -0.31495538 0.60835114 0.38233111
## 1557 0.46785714 -0.72359740 -0.09380519 -0.44133766
## 1537 0.01968912 -0.29060994 0.54293246 0.33993273
## 9792 -0.60929006 0.78845559 -0.36673030 0.10071812
## 1638 0.50555844 -0.54033766 -0.10271429 -0.49964935
## 815 -0.01227161 -0.39630943 -0.47153895 -0.44059631
## 5063 0.53564221 -0.73449686 -0.09388237 -0.60061813
## s.rheumatoid_art s.stroke p.alzheimers p.breast_cancer p.chronic_pain
## 7530 0.82739751 0.703990546 0.0762047435 0.9453886569 0.0752532046
## 7602 0.74642305 0.665357695 0.0636223362 0.4201561366 0.0457823802
## 2468 0.36436688 -0.242432506 0.0034185891 0.2221549176 0.0200862117
## 2469 0.41952550 -0.525806745 0.5232228073 0.5845682142 0.0032602373
## 7854 0.57371148 -0.246923007 0.0197014254 0.2600524753 0.1422748182
## 8066 -0.15322031 0.109904095 0.0001604508 0.0001535198 0.1190338937
## 8778 -0.03725116 -0.589364603 0.0288809886 0.0377146482 0.0418161961
## 9129 -0.24654122 0.466757567 0.2669669079 0.8254337317 0.0109127437
## 9790 -0.02476139 -0.060849014 0.0136853026 0.0045238282 0.8404857347
## 8593 0.24221434 0.335878556 0.5759873915 0.0296008671 0.6031320521
## 7031 0.36425617 -0.419132464 0.1756110068 0.0647314859 0.0006506709
## 9206 0.23087013 -0.834935065 0.0736298199 0.4945584863 0.1312563495
## 9243 -0.70569948 -0.044159622 0.0136664856 0.0427812360 0.4083230425
## 7500 -0.48143266 0.576746512 0.0408093396 0.2200127267 0.0644711400
## 1557 0.04393506 -0.001064935 0.4910311333 0.0254258819 0.1574967746
## 1537 -0.56414871 0.548950095 0.1224585251 0.3844904763 0.1794419765
## 9792 -0.37252977 -0.236051268 0.0866057967 0.0667620637 0.1186208859
## 1638 0.08616883 0.085285714 0.3523958294 0.0023808366 0.0074974758
## 815 -0.09097355 0.170111808 0.2796039016 0.2777296095 0.0334361075
## 5063 -0.30351786 -0.037614762 0.4807761510 0.0747236754 0.0701153892
## p.CKD p.colorectal_can p.COPD p.diabetes p.heart_disease
## 7530 0.002039674 0.174877587 0.006333990 0.0004280458 0.002669736
## 7602 0.055724319 0.464759218 0.658562645 0.0275556637 0.028875828
## 2468 0.031081516 0.002250397 0.414496393 0.2162137992 0.731290697
## 2469 0.004740024 0.125521465 0.027476455 0.0951566125 0.300722646
## 7854 0.468983864 0.062286347 0.673916806 0.0207821748 0.076989485
## 8066 0.948092331 0.962529765 0.009637970 0.9020504668 0.229933930
## 8778 0.885804517 0.002228304 0.610118062 0.9587329806 0.411743667
## 9129 0.555507422 0.002187800 0.050457595 0.0018274559 0.117940895
## 9790 0.357578378 0.018246697 0.017845326 0.0219712078 0.665915183
## 8593 0.091677435 0.629937152 0.000277537 0.7486205531 0.602347157
## 7031 0.332851147 0.042372520 0.024868736 0.3747554964 0.115574357
## 9206 0.001085080 0.045772654 0.013439417 0.0111630918 0.396395393
## 9243 0.178713060 0.010818468 0.586359305 0.3061475835 0.151692703
## 7500 0.768839108 0.626883759 0.006646936 0.1776900042 0.016034983
## 1557 0.008116668 0.131119578 0.057244859 0.2068053532 0.001800919
## 1537 0.857309319 0.377677485 0.010638538 0.0548382173 0.055697293
## 9792 0.313593808 0.137289940 0.156329231 0.4214561380 0.215743498
## 1638 0.201106524 0.095058458 0.016533477 0.2015682526 0.041721856
## 815 0.006192031 0.005614667 0.383298569 0.8385046427 0.047285409
## 5063 0.841586779 0.240113104 0.311031347 0.3444166241 0.018571352
## p.lung_cancer p.osteoarthritis p.parkinsons p.prostate_cance
## 7530 0.02304433 0.6033283479 0.344416624 0.0742999617
## 7602 0.15253884 0.0088682149 0.325053979 0.0857808347
## 2468 0.04442401 0.0700500414 0.060847527 0.8168921835
## 2469 0.74211973 0.2757088010 0.384375031 0.0074296397
## 7854 0.05422760 0.0714218226 0.757923110 0.0016172679
## 8066 0.04521443 0.1624507532 0.532160683 0.0168605646
## 8778 0.02349824 0.0111864606 0.111814897 0.6238479644
## 9129 0.44803194 0.7464871406 0.799494392 0.0303792234
## 9790 0.09361737 0.0451592893 0.392897148 0.0567411243
## 8593 0.44132107 0.4258610254 0.040146049 0.0003749814
## 7031 0.47529949 0.0018592906 0.091516560 0.3351664720
## 9206 0.48595175 0.3368150929 0.247459968 0.0192056496
## 9243 0.14443113 0.2614552515 0.111735580 0.0396494053
## 7500 0.59454467 0.1815454989 0.009859677 0.1048400293
## 1557 0.03206175 0.0009143411 0.667357485 0.0431638898
## 1537 0.93922606 0.2604399550 0.035508130 0.1880547885
## 9792 0.01829872 0.0022620496 0.155569592 0.6965263483
## 1638 0.02053639 0.0132946893 0.637929567 0.0220605885
## 815 0.96209907 0.1248626167 0.067847177 0.0879731154
## 5063 0.03805320 0.0044485257 0.716198954 0.0200222549
## p.rheumatoid_art p.stroke s.dist SD p.adjustMANOVA
## 7530 0.001353798 0.0064046957 2.217695 0.4566315 2.156992e-05
## 7602 0.003844266 0.0099755836 1.824333 0.4085021 1.885279e-02
## 2468 0.158251016 0.3478403904 1.742234 0.4559562 8.005812e-03
## 2469 0.104250566 0.0417313136 1.720768 0.4454221 1.768281e-03
## 7854 0.026302168 0.3389834914 1.719685 0.4737908 2.785067e-04
## 8066 0.515733374 0.6410761515 1.697687 0.4705788 4.641516e-03
## 8778 0.885304190 0.0224689559 1.695447 0.4374281 1.604329e-02
## 9129 0.339730830 0.0706838353 1.695050 0.4483233 1.118648e-04
## 9790 0.923612622 0.8137219592 1.691189 0.3598489 2.356490e-02
## 8593 0.348274400 0.1933764391 1.686244 0.3831494 9.426475e-03
## 7031 0.122309621 0.0754122511 1.662604 0.4577017 2.559086e-02
## 9206 0.290162624 0.0001302616 1.660161 0.3483112 6.156654e-05
## 9243 0.006277396 0.8642234564 1.637702 0.3705200 7.973729e-03
## 7500 0.041126927 0.0144201968 1.597551 0.4422202 1.509869e-03
## 1557 0.840469948 0.9961070631 1.567864 0.4348461 1.957128e-02
## 1537 0.028911971 0.0335181305 1.556794 0.4300375 3.855296e-02
## 9792 0.149136045 0.3606771631 1.551975 0.4273906 1.421397e-01
## 1638 0.692997389 0.6959860572 1.550259 0.4273933 1.706091e-02
## 815 0.724628665 0.5100684069 1.547902 0.4225251 1.920563e-01
## 5063 0.239859613 0.8841925166 1.531482 0.4154246 5.588113e-02#numsig manova
nrow(subset(mtable1,p.adjustMANOVA<0.05))## [1] 1233sig <- subset(mtable1,p.adjustMANOVA<0.05)Individual comparisons.
r1 <- lapply(1:length(l1),function(i) {
name=names(l1[i])
m <- l1[[i]]
m2 <- mitch_import(x=m,DEtype="prescored",geneTable=gt,geneIDcol="CpG")
mres <- mitch_calc(x=m2,genesets=gs_symbols,minsetsize=5,cores=2, priority="significance")
mtable <- mres$enrichment_result
#mitch_report(res=mres,outfile=paste("mitchreport_prev_",name,".html",sep="",overwrite=TRUE))
})## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.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
## [1,] 15 25
## [2,] 26 10
## [3,] 27 131
## [4,] 47 169
## [5,] 250 24
## [6,] 759 166
## [7,] 36 328
## [8,] 80 191
## [9,] 42 38
## [10,] 336 98
## [11,] 135 11
## [12,] 80 20
## [13,] 2 65
## [14,] 83 14rownames(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## [[1]]
## [[1]]$ups
## character(0)
##
## [[1]]$dns
## [1] "GOBP_CALCIUM_ION_REGULATED_EXOCYTOSIS"
## [2] "GOCC_MULTIVESICULAR_BODY_LUMEN"
##
##
## [[2]]
## [[2]]$ups
## [1] "GOCC_MULTIVESICULAR_BODY_LUMEN"
##
## [[2]]$dns
## [1] "GOBP_NEURON_FATE_COMMITMENT"
## [2] "GOBP_EMBRYONIC_FORELIMB_MORPHOGENESIS"
##
##
## [[3]]
## [[3]]$ups
## [1] "GOBP_GRANULOCYTE_ACTIVATION"
## [2] "GOBP_FOREBRAIN_REGIONALIZATION"
## [3] "GOBP_NEURON_FATE_SPECIFICATION"
## [4] "GOBP_TELENCEPHALON_REGIONALIZATION"
## [5] "GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_OR_POLYSACCHARIDE_ANTIGEN_VIA_MHC_CLASS_II"
##
## [[3]]$dns
## [1] "GOBP_PLASMA_MEMBRANE_FUSION"
## [2] "GOMF_P_TYPE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY"
## [3] "GOBP_REGULATION_OF_XENOBIOTIC_DETOXIFICATION_BY_TRANSMEMBRANE_EXPORT_ACROSS_THE_PLASMA_MEMBRANE"
## [4] "GOMF_STRUCTURAL_CONSTITUENT_OF_SKIN_EPIDERMIS"
## [5] "GOMF_HIGH_VOLTAGE_GATED_CALCIUM_CHANNEL_ACTIVITY"
##
##
## [[4]]
## [[4]]$ups
## [1] "GOCC_RIBOSOMAL_SUBUNIT"
## [2] "GOCC_RIBOSOME"
## [3] "GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME"
## [4] "GOBP_CYTOPLASMIC_TRANSLATION"
## [5] "GOCC_LARGE_RIBOSOMAL_SUBUNIT"
##
## [[4]]$dns
## [1] "GOBP_INTESTINAL_CHOLESTEROL_ABSORPTION"
## [2] "GOBP_SENSORY_PERCEPTION_OF_BITTER_TASTE"
## [3] "GOBP_I_KAPPAB_PHOSPHORYLATION"
## [4] "GOMF_STRUCTURAL_CONSTITUENT_OF_SKIN_EPIDERMIS"
## [5] "GOBP_LIPID_DIGESTION"
##
##
## [[5]]
## [[5]]$ups
## [1] "GOBP_SENSORY_PERCEPTION_OF_TASTE"
## [2] "GOBP_DETECTION_OF_CHEMICAL_STIMULUS_INVOLVED_IN_SENSORY_PERCEPTION_OF_TASTE"
## [3] "GOBP_SENSORY_PERCEPTION_OF_BITTER_TASTE"
## [4] "GOMF_STRUCTURAL_CONSTITUENT_OF_SKIN_EPIDERMIS"
## [5] "GOCC_PHOTORECEPTOR_DISC_MEMBRANE"
##
## [[5]]$dns
## [1] "GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT"
## [2] "GOBP_MONOCYTE_CHEMOTAXIS"
## [3] "GOBP_NEURON_FATE_COMMITMENT"
## [4] "GOBP_REGULATION_OF_MONOCYTE_CHEMOTAXIS"
## [5] "GOBP_FOREBRAIN_REGIONALIZATION"
##
##
## [[6]]
## [[6]]$ups
## [1] "GOBP_DETECTION_OF_STIMULUS_INVOLVED_IN_SENSORY_PERCEPTION"
## [2] "GOBP_SENSORY_PERCEPTION_OF_SMELL"
## [3] "GOBP_SENSORY_PERCEPTION_OF_CHEMICAL_STIMULUS"
## [4] "GOMF_OLFACTORY_RECEPTOR_ACTIVITY"
## [5] "GOBP_KERATINIZATION"
##
## [[6]]$dns
## [1] "GOBP_NEURON_FATE_COMMITMENT"
## [2] "GOCC_SPECIFIC_GRANULE_MEMBRANE"
## [3] "GOMF_STRUCTURAL_CONSTITUENT_OF_CHROMATIN"
## [4] "GOCC_TERTIARY_GRANULE_MEMBRANE"
## [5] "GOCC_AZUROPHIL_GRANULE_MEMBRANE"
##
##
## [[7]]
## [[7]]$ups
## [1] "GOCC_IMMUNOLOGICAL_SYNAPSE"
## [2] "GOBP_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS_NONSENSE_MEDIATED_DECAY"
## [3] "GOBP_REGULATION_OF_INTRINSIC_APOPTOTIC_SIGNALING_PATHWAY_BY_P53_CLASS_MEDIATOR"
## [4] "GOCC_ALPHA_BETA_T_CELL_RECEPTOR_COMPLEX"
## [5] "GOMF_OXIDOREDUCTASE_ACTIVITY_ACTING_ON_A_HEME_GROUP_OF_DONORS"
##
## [[7]]$dns
## [1] "GOBP_KERATINIZATION"
## [2] "GOMF_STRUCTURAL_CONSTITUENT_OF_SKIN_EPIDERMIS"
## [3] "GOCC_CATENIN_COMPLEX"
## [4] "GOBP_REGULATION_OF_PODOSOME_ASSEMBLY"
## [5] "GOBP_PODOSOME_ASSEMBLY"
##
##
## [[8]]
## [[8]]$ups
## [1] "GOCC_RIBOSOMAL_SUBUNIT"
## [2] "GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME"
## [3] "GOCC_RIBOSOME"
## [4] "GOCC_LARGE_RIBOSOMAL_SUBUNIT"
## [5] "GOBP_CYTOPLASMIC_TRANSLATION"
##
## [[8]]$dns
## [1] "GOCC_RNAI_EFFECTOR_COMPLEX"
## [2] "GOBP_INTERMEDIATE_FILAMENT_ORGANIZATION"
## [3] "GOMF_STRUCTURAL_CONSTITUENT_OF_SKIN_EPIDERMIS"
## [4] "GOBP_SENSORY_PERCEPTION_OF_BITTER_TASTE"
## [5] "GOBP_NEGATIVE_REGULATION_OF_VASCULAR_ENDOTHELIAL_GROWTH_FACTOR_PRODUCTION"
##
##
## [[9]]
## [[9]]$ups
## character(0)
##
## [[9]]$dns
## [1] "GOMF_STRUCTURAL_CONSTITUENT_OF_SKIN_EPIDERMIS"
##
##
## [[10]]
## [[10]]$ups
## [1] "GOBP_EOSINOPHIL_MIGRATION" "GOMF_CCR_CHEMOKINE_RECEPTOR_BINDING"
## [3] "GOMF_CHEMOKINE_RECEPTOR_BINDING" "GOBP_LYMPHOCYTE_CHEMOTAXIS"
## [5] "GOMF_CHEMOKINE_ACTIVITY"
##
## [[10]]$dns
## [1] "GOBP_NEURON_FATE_COMMITMENT"
## [2] "GOBP_EMBRYONIC_APPENDAGE_MORPHOGENESIS"
## [3] "GOBP_EMBRYONIC_SKELETAL_SYSTEM_MORPHOGENESIS"
## [4] "GOBP_CELL_FATE_SPECIFICATION"
## [5] "GOBP_CELL_FATE_DETERMINATION"
##
##
## [[11]]
## [[11]]$ups
## [1] "GOMF_OLFACTORY_RECEPTOR_ACTIVITY"
## [2] "GOMF_MHC_CLASS_I_RECEPTOR_ACTIVITY"
## [3] "GOMF_ALDITOL_NADPPLUS_1_OXIDOREDUCTASE_ACTIVITY"
##
## [[11]]$dns
## [1] "GOBP_MIRNA_MEDIATED_GENE_SILENCING_BY_MRNA_DESTABILIZATION"
## [2] "GOMF_NEUROPEPTIDE_RECEPTOR_BINDING"
## [3] "GOMF_CHEMOKINE_ACTIVITY"
## [4] "GOBP_POSITIVE_REGULATION_OF_SMOOTH_MUSCLE_CELL_DIFFERENTIATION"
## [5] "GOBP_POSITIVE_REGULATION_OF_EPIDERMIS_DEVELOPMENT"
##
##
## [[12]]
## [[12]]$ups
## [1] "GOBP_URONIC_ACID_METABOLIC_PROCESS"
## [2] "GOBP_CELLULAR_GLUCURONIDATION"
## [3] "GOMF_ALKANE_1_MONOOXYGENASE_ACTIVITY"
##
## [[12]]$dns
## [1] "GOBP_NEURON_FATE_COMMITMENT"
## [2] "GOBP_CEREBRAL_CORTEX_NEURON_DIFFERENTIATION"
## [3] "GOBP_CELL_DIFFERENTIATION_IN_SPINAL_CORD"
## [4] "GOBP_NEUROTRANSMITTER_REUPTAKE"
## [5] "GOBP_NEPHRIC_DUCT_MORPHOGENESIS"
##
##
## [[13]]
## [[13]]$ups
## [1] "GOMF_C_C_CHEMOKINE_BINDING"
## [2] "GOBP_GRANULOCYTE_ACTIVATION"
## [3] "GOMF_CHEMOKINE_BINDING"
## [4] "GOMF_G_PROTEIN_COUPLED_CHEMOATTRACTANT_RECEPTOR_ACTIVITY"
## [5] "GOCC_IMMUNOLOGICAL_SYNAPSE"
##
## [[13]]$dns
## character(0)
##
##
## [[14]]
## [[14]]$ups
## [1] "GOMF_OXYGEN_BINDING"
## [2] "GOMF_ARACHIDONIC_ACID_MONOOXYGENASE_ACTIVITY"
##
## [[14]]$dns
## [1] "GOMF_IGG_BINDING"
## [2] "GOBP_MULTIVESICULAR_BODY_SORTING_PATHWAY"
## [3] "GOBP_FATTY_ACID_DERIVATIVE_BIOSYNTHETIC_PROCESS"gsets <- unique(unname(unlist(r2)))
gsets## [1] "GOBP_CALCIUM_ION_REGULATED_EXOCYTOSIS"
## [2] "GOCC_MULTIVESICULAR_BODY_LUMEN"
## [3] "GOBP_NEURON_FATE_COMMITMENT"
## [4] "GOBP_EMBRYONIC_FORELIMB_MORPHOGENESIS"
## [5] "GOBP_GRANULOCYTE_ACTIVATION"
## [6] "GOBP_FOREBRAIN_REGIONALIZATION"
## [7] "GOBP_NEURON_FATE_SPECIFICATION"
## [8] "GOBP_TELENCEPHALON_REGIONALIZATION"
## [9] "GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_OR_POLYSACCHARIDE_ANTIGEN_VIA_MHC_CLASS_II"
## [10] "GOBP_PLASMA_MEMBRANE_FUSION"
## [11] "GOMF_P_TYPE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY"
## [12] "GOBP_REGULATION_OF_XENOBIOTIC_DETOXIFICATION_BY_TRANSMEMBRANE_EXPORT_ACROSS_THE_PLASMA_MEMBRANE"
## [13] "GOMF_STRUCTURAL_CONSTITUENT_OF_SKIN_EPIDERMIS"
## [14] "GOMF_HIGH_VOLTAGE_GATED_CALCIUM_CHANNEL_ACTIVITY"
## [15] "GOCC_RIBOSOMAL_SUBUNIT"
## [16] "GOCC_RIBOSOME"
## [17] "GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME"
## [18] "GOBP_CYTOPLASMIC_TRANSLATION"
## [19] "GOCC_LARGE_RIBOSOMAL_SUBUNIT"
## [20] "GOBP_INTESTINAL_CHOLESTEROL_ABSORPTION"
## [21] "GOBP_SENSORY_PERCEPTION_OF_BITTER_TASTE"
## [22] "GOBP_I_KAPPAB_PHOSPHORYLATION"
## [23] "GOBP_LIPID_DIGESTION"
## [24] "GOBP_SENSORY_PERCEPTION_OF_TASTE"
## [25] "GOBP_DETECTION_OF_CHEMICAL_STIMULUS_INVOLVED_IN_SENSORY_PERCEPTION_OF_TASTE"
## [26] "GOCC_PHOTORECEPTOR_DISC_MEMBRANE"
## [27] "GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT"
## [28] "GOBP_MONOCYTE_CHEMOTAXIS"
## [29] "GOBP_REGULATION_OF_MONOCYTE_CHEMOTAXIS"
## [30] "GOBP_DETECTION_OF_STIMULUS_INVOLVED_IN_SENSORY_PERCEPTION"
## [31] "GOBP_SENSORY_PERCEPTION_OF_SMELL"
## [32] "GOBP_SENSORY_PERCEPTION_OF_CHEMICAL_STIMULUS"
## [33] "GOMF_OLFACTORY_RECEPTOR_ACTIVITY"
## [34] "GOBP_KERATINIZATION"
## [35] "GOCC_SPECIFIC_GRANULE_MEMBRANE"
## [36] "GOMF_STRUCTURAL_CONSTITUENT_OF_CHROMATIN"
## [37] "GOCC_TERTIARY_GRANULE_MEMBRANE"
## [38] "GOCC_AZUROPHIL_GRANULE_MEMBRANE"
## [39] "GOCC_IMMUNOLOGICAL_SYNAPSE"
## [40] "GOBP_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS_NONSENSE_MEDIATED_DECAY"
## [41] "GOBP_REGULATION_OF_INTRINSIC_APOPTOTIC_SIGNALING_PATHWAY_BY_P53_CLASS_MEDIATOR"
## [42] "GOCC_ALPHA_BETA_T_CELL_RECEPTOR_COMPLEX"
## [43] "GOMF_OXIDOREDUCTASE_ACTIVITY_ACTING_ON_A_HEME_GROUP_OF_DONORS"
## [44] "GOCC_CATENIN_COMPLEX"
## [45] "GOBP_REGULATION_OF_PODOSOME_ASSEMBLY"
## [46] "GOBP_PODOSOME_ASSEMBLY"
## [47] "GOCC_RNAI_EFFECTOR_COMPLEX"
## [48] "GOBP_INTERMEDIATE_FILAMENT_ORGANIZATION"
## [49] "GOBP_NEGATIVE_REGULATION_OF_VASCULAR_ENDOTHELIAL_GROWTH_FACTOR_PRODUCTION"
## [50] "GOBP_EOSINOPHIL_MIGRATION"
## [51] "GOMF_CCR_CHEMOKINE_RECEPTOR_BINDING"
## [52] "GOMF_CHEMOKINE_RECEPTOR_BINDING"
## [53] "GOBP_LYMPHOCYTE_CHEMOTAXIS"
## [54] "GOMF_CHEMOKINE_ACTIVITY"
## [55] "GOBP_EMBRYONIC_APPENDAGE_MORPHOGENESIS"
## [56] "GOBP_EMBRYONIC_SKELETAL_SYSTEM_MORPHOGENESIS"
## [57] "GOBP_CELL_FATE_SPECIFICATION"
## [58] "GOBP_CELL_FATE_DETERMINATION"
## [59] "GOMF_MHC_CLASS_I_RECEPTOR_ACTIVITY"
## [60] "GOMF_ALDITOL_NADPPLUS_1_OXIDOREDUCTASE_ACTIVITY"
## [61] "GOBP_MIRNA_MEDIATED_GENE_SILENCING_BY_MRNA_DESTABILIZATION"
## [62] "GOMF_NEUROPEPTIDE_RECEPTOR_BINDING"
## [63] "GOBP_POSITIVE_REGULATION_OF_SMOOTH_MUSCLE_CELL_DIFFERENTIATION"
## [64] "GOBP_POSITIVE_REGULATION_OF_EPIDERMIS_DEVELOPMENT"
## [65] "GOBP_URONIC_ACID_METABOLIC_PROCESS"
## [66] "GOBP_CELLULAR_GLUCURONIDATION"
## [67] "GOMF_ALKANE_1_MONOOXYGENASE_ACTIVITY"
## [68] "GOBP_CEREBRAL_CORTEX_NEURON_DIFFERENTIATION"
## [69] "GOBP_CELL_DIFFERENTIATION_IN_SPINAL_CORD"
## [70] "GOBP_NEUROTRANSMITTER_REUPTAKE"
## [71] "GOBP_NEPHRIC_DUCT_MORPHOGENESIS"
## [72] "GOMF_C_C_CHEMOKINE_BINDING"
## [73] "GOMF_CHEMOKINE_BINDING"
## [74] "GOMF_G_PROTEIN_COUPLED_CHEMOATTRACTANT_RECEPTOR_ACTIVITY"
## [75] "GOMF_OXYGEN_BINDING"
## [76] "GOMF_ARACHIDONIC_ACID_MONOOXYGENASE_ACTIVITY"
## [77] "GOMF_IGG_BINDING"
## [78] "GOBP_MULTIVESICULAR_BODY_SORTING_PATHWAY"
## [79] "GOBP_FATTY_ACID_DERIVATIVE_BIOSYNTHETIC_PROCESS"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$set=NULL
head(top,2)## s.alzheimers s.breast_cancer
## GOCC_ALPHA_BETA_T_CELL_RECEPTOR_COMPLEX -0.4578856 -0.0176893
## GOCC_MULTIVESICULAR_BODY_LUMEN -0.8896414 0.8922322
## s.chronic_pain s.CKD
## GOCC_ALPHA_BETA_T_CELL_RECEPTOR_COMPLEX -0.4593764 0.79643669
## GOCC_MULTIVESICULAR_BODY_LUMEN -0.3674833 -0.01534779
## s.colorectal_can s.COPD s.diabetes
## GOCC_ALPHA_BETA_T_CELL_RECEPTOR_COMPLEX 0.35034997 0.7049359 0.90944460
## GOCC_MULTIVESICULAR_BODY_LUMEN 0.01107525 0.6101995 0.02901383
## s.heart_disease s.lung_cancer
## GOCC_ALPHA_BETA_T_CELL_RECEPTOR_COMPLEX 0.7755295 0.5868739
## GOCC_MULTIVESICULAR_BODY_LUMEN -0.2830174 -0.4720998
## s.osteoarthritis s.parkinsons
## GOCC_ALPHA_BETA_T_CELL_RECEPTOR_COMPLEX 0.1341878 0.2441596
## GOCC_MULTIVESICULAR_BODY_LUMEN 0.3293032 -0.1472736
## s.prostate_cance s.rheumatoid_art
## GOCC_ALPHA_BETA_T_CELL_RECEPTOR_COMPLEX 0.4608854 0.8273975
## GOCC_MULTIVESICULAR_BODY_LUMEN 0.5633380 -0.1532203
## s.stroke
## GOCC_ALPHA_BETA_T_CELL_RECEPTOR_COMPLEX 0.7039905
## GOCC_MULTIVESICULAR_BODY_LUMEN 0.1099041rownames(top) <- gsub("REACTOME_","",rownames(top))
rownames(top) <- gsub("_"," ",rownames(top))
colnames(top) <- gsub("^s.","",colnames(top))
colnames(top) <- gsub("_"," ",colnames(top))
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(as.matrix(top),scale="none",trace="none",margins=c(6,25),
col=colfunc(25),cexRow=0.6,cexCol=0.8)
pdf("fig8b.pdf",height=9,width=7)
heatmap.2(as.matrix(top),scale="none",trace="none",margins=c(6,22),
col=colfunc(25),cexRow=0.5,cexCol=0.8)
dev.off()## png
## 2Mitch calc incidence
Multi-comparison for incidence.
tic()
mres2 <- mitch_calc(x=m2,genesets=gs_symbols,minsetsize=5,cores=8, priority="effect")## Note: Enrichments with large effect sizes may not be
## statistically significant.toc() #29.5s## 252.689 sec elapsedmtable2 <- mres2$enrichment_result
head(mtable2,20)## set
## 7530 GOCC_ALPHA_BETA_T_CELL_RECEPTOR_COMPLEX
## 208 GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_ENDOGENOUS_LIPID_ANTIGEN_VIA_MHC_CLASS_IB
## 9310 GOMF_LIPID_ANTIGEN_BINDING
## 2469 GOBP_MICROGLIAL_CELL_MEDIATED_CYTOTOXICITY
## 5063 GOBP_PROXIMAL_DISTAL_PATTERN_FORMATION_INVOLVED_IN_NEPHRON_DEVELOPMENT
## 7500 GOBP_XENOBIOTIC_GLUCURONIDATION
## 7602 GOCC_B_CELL_RECEPTOR_COMPLEX
## 8778 GOMF_CCR1_CHEMOKINE_RECEPTOR_BINDING
## 1537 GOBP_FLAVONOID_GLUCURONIDATION
## 9288 GOMF_LARGE_RIBOSOMAL_SUBUNIT_RRNA_BINDING
## 9792 GOMF_PROTEIN_ARGININE_DEIMINASE_ACTIVITY
## 9790 GOMF_PROTEINASE_ACTIVATED_RECEPTOR_ACTIVITY
## 8781 GOMF_CCR6_CHEMOKINE_RECEPTOR_BINDING
## 7791 GOCC_EGG_COAT
## 9634 GOMF_O_PALMITOYLTRANSFERASE_ACTIVITY
## 3769 GOBP_PEPTIDE_MODIFICATION
## 5215 GOBP_REGULATION_OF_AMACRINE_CELL_DIFFERENTIATION
## 8455 GOCC_TROPONIN_COMPLEX
## 5276 GOBP_REGULATION_OF_BINDING_OF_SPERM_TO_ZONA_PELLUCIDA
## 7031 GOBP_STEM_CELL_FATE_SPECIFICATION
## setSize pMANOVA s.alzheimers s.breast_cancer s.chronic_pain
## 7530 5 2.967828e-10 0.2978457 0.40050904 0.3838014726
## 208 5 9.617044e-06 -0.3270794 -0.31298973 -0.6218707390
## 9310 5 9.617044e-06 -0.3270794 -0.31298973 -0.6218707390
## 2469 5 4.288610e-03 0.7246069 0.34245978 0.5989273702
## 5063 5 3.019845e-03 0.1081174 0.03914190 0.4442323425
## 7500 6 1.407061e-04 0.1882793 -0.22258079 -0.3994818417
## 7602 5 4.868775e-03 0.4322153 -0.17492955 0.7985274066
## 8778 5 3.933367e-03 0.4438142 0.56113081 -0.0002908826
## 1537 5 6.362728e-03 0.2569766 -0.36736660 -0.4634487774
## 9288 5 2.774158e-02 -0.3303518 0.01641669 0.5125715844
## 9792 5 2.387506e-02 -0.3413144 -0.13493319 -0.0125443142
## 9790 5 1.029881e-02 0.6654668 -0.28295609 -0.3616580311
## 8781 5 4.200578e-03 -0.3023180 0.12547950 -0.3192255250
## 7791 5 1.456380e-02 -0.6824289 -0.09031906 -0.0689573675
## 9634 5 5.537396e-02 0.2025998 -0.22592492 0.2079447323
## 3769 5 6.971777e-02 0.4957913 -0.44615944 -0.0205799473
## 5215 6 1.431455e-02 -0.4479342 0.35974122 0.2117176492
## 8455 9 5.829701e-08 -0.5954784 0.16638886 0.1814912467
## 5276 5 4.586549e-02 -0.4940460 -0.04615944 0.0317425689
## 7031 6 5.657710e-03 0.2360650 0.29760768 0.0006969380
## s.CKD s.colorectal_can s.COPD s.covid_hospital s.diabetes
## 7530 0.84801382 -0.71497137 0.64074175 0.53398782 0.04792292
## 208 0.21521680 -0.59298246 0.33196982 -0.20352695 -0.48119262
## 9310 0.21521680 -0.59298246 0.33196982 -0.20352695 -0.48119262
## 2469 -0.58685574 -0.10451777 -0.21230797 0.47310245 0.17954731
## 5063 0.39492773 -0.04386874 -0.09015544 0.04146896 -0.71271703
## 7500 0.56936806 -0.24701907 0.33122737 -0.30295593 -0.13961487
## 7602 -0.28179256 -0.32640669 0.15520407 -0.19952732 0.39354604
## 8778 0.55289519 -0.54920462 0.19370966 0.43692392 -0.78736479
## 1537 0.51308063 -0.12984274 0.28844650 -0.21036269 -0.21298064
## 9288 -0.19234615 -0.46903009 -0.53562403 -0.42321607 -0.60983547
## 9792 0.03116080 0.19683665 0.69560949 0.04143260 0.46075811
## 9790 0.07239342 0.27995637 -0.37183892 0.04052359 -0.13160622
## 8781 0.39027361 -0.38603763 -0.08161076 0.12467957 -0.42041633
## 7791 0.41767112 -0.15349514 0.57569312 0.54196891 0.18727388
## 9634 -0.21334424 0.02674302 -0.14220525 0.07922916 0.33626034
## 3769 -0.52027997 0.48111990 -0.40461776 -0.36416689 0.51371693
## 5215 -0.12075209 -0.12816084 -0.29363817 0.24107995 -0.22673212
## 8455 0.51366286 0.51774404 0.33094928 -0.05552020 0.56000040
## 5276 0.32486138 0.32126170 0.58738297 0.05055904 -0.20558131
## 7031 0.32534885 -0.04424041 -0.02131721 0.33556050 -0.30256200
## s.heart_disease s.ibd s.liver_cirrhosi s.long_covid s.lung_cancer
## 7530 -0.75942187 -0.19670939 0.65446778 0.452049814 -0.82961549
## 208 -0.73044269 0.23219707 -0.85104990 -0.052431597 -0.76313062
## 9310 -0.73044269 0.23219707 -0.85104990 -0.052431597 -0.76313062
## 2469 0.37096628 -0.13289701 -0.08262885 -0.051468048 0.19370966
## 5063 -0.34182347 -0.57160258 -0.42703391 0.241814381 0.40874466
## 7500 -0.57657985 0.37121040 -0.47855401 -0.682059906 -0.56971653
## 7602 0.18654668 -0.19492773 0.49259158 -0.146732115 -0.22036179
## 8778 -0.14193255 -0.04797746 -0.13155168 -0.443314244 0.41343514
## 1537 -0.51722571 0.35300427 -0.57805654 -0.651413508 -0.50420871
## 9288 -0.38865558 0.21623489 0.05606763 0.359694573 0.28324698
## 9792 0.04663212 0.56478502 -0.55349514 -0.072938824 -0.03481502
## 9790 0.56960276 -0.60710844 0.12371603 -0.008162894 -0.39892737
## 8781 -0.52004363 0.01541678 0.17958367 -0.189819107 0.59265521
## 7791 0.22372512 0.11137169 -0.38140169 0.336933006 0.26219435
## 9634 0.40976275 -0.29142805 0.39505500 -0.281592582 -0.41832561
## 3769 0.58492864 0.14113262 0.30300882 -0.104026907 -0.19932733
## 5215 -0.02821084 -0.55836856 0.19426390 0.596260776 -0.13699377
## 8455 0.18308735 0.02894203 -0.40741077 0.595347052 -0.18818883
## 5276 -0.12240705 0.45232252 -0.22210708 -0.200527225 -0.08959186
## 7031 -0.12070663 -0.14058452 -0.11378271 0.599684863 0.50315895
## s.osteoarthritis s.ovarian_cancer s.parkinsons s.prostate_cance
## 7530 0.69371875 -0.13804200 0.51509863 -0.35422234
## 208 -0.38094719 -0.07066630 0.65199527 -0.25675848
## 9310 -0.38094719 -0.07066630 0.65199527 -0.25675848
## 2469 -0.32775202 0.45948550 -0.69153713 -0.83563312
## 5063 -0.72338878 0.07190255 0.37374784 0.07550223
## 7500 0.04531612 -0.64889475 -0.43485902 0.03449843
## 7602 0.59287338 0.44208708 -0.17176620 -0.58305609
## 8778 -0.17005727 -0.25664940 0.45792201 -0.14513226
## 1537 0.12651577 -0.61730752 -0.39616399 -0.04837742
## 9288 -0.05410417 0.14467776 0.69520953 0.38816471
## 9792 0.48481047 -0.74209617 0.33793292 0.49246432
## 9790 -0.79550950 0.15614944 -0.02014362 -0.33911463
## 8781 0.24539587 -0.66175802 0.66388510 0.08011999
## 7791 0.09113717 -0.41901645 -0.27088447 0.15949459
## 9634 -0.58698300 0.12715208 -0.02457958 -0.79329152
## 3769 -0.26366694 0.23661485 -0.46112172 -0.26272157
## 5215 -0.30891020 0.83231065 0.21805069 0.05931549
## 8455 0.56131365 -0.49031730 0.01208191 -0.14613450
## 5276 0.14364149 -0.76342151 -0.04479593 0.03946914
## 7031 -0.40164841 0.60198779 0.19518810 -0.58888232
## s.rheumatoid_art s.stroke p.alzheimers p.breast_cancer p.chronic_pain
## 7530 0.63025180 0.78125625 0.248757684 0.12091536 0.137215461
## 208 0.48008363 0.17274793 0.205305462 0.22550562 0.016029495
## 9310 0.48008363 0.17274793 0.205305462 0.22550562 0.016029495
## 2469 -0.32407963 0.20147259 0.005013531 0.18479283 0.020374321
## 5063 -0.84105081 0.07952004 0.675459852 0.87952600 0.085383533
## 7500 0.31472812 0.06354257 0.424491631 0.34508916 0.090154006
## 7602 -0.02665212 0.68415599 0.094183576 0.49816024 0.001984834
## 8778 0.47666576 0.04117807 0.085678162 0.02978058 0.999101270
## 1537 0.29064630 -0.07188437 0.319683817 0.15485360 0.072703230
## 9288 -0.29613671 -0.46119444 0.200808072 0.94931204 0.047152062
## 9792 0.25630397 0.33620580 0.186266048 0.60131768 0.961257482
## 9790 -0.35680393 -0.32037088 0.009963399 0.27320463 0.161367217
## 8781 0.26128534 0.54647759 0.241722932 0.62703912 0.216396893
## 7791 0.76133079 -0.28424689 0.008222481 0.72653000 0.789449571
## 9634 -0.80967185 0.13238796 0.432724911 0.38164903 0.420683978
## 3769 0.24085083 0.40809017 0.054864895 0.08403624 0.936482050
## 5215 -0.64627365 -0.23979213 0.057415442 0.12701027 0.369144675
## 8455 0.21798951 0.11629340 0.001976611 0.38738833 0.345773342
## 5276 0.85184983 -0.26048541 0.055724319 0.85813845 0.902172418
## 7031 -0.72640638 -0.02480190 0.316654207 0.20679716 0.997641228
## p.CKD p.colorectal_can p.COPD p.covid_hospital p.diabetes
## 7530 0.001022773 0.005625600 0.013089209 0.03865187 0.852779549
## 208 0.404621645 0.021655454 0.198611108 0.43062199 0.062405034
## 9310 0.404621645 0.021655454 0.198611108 0.43062199 0.062405034
## 2469 0.023048573 0.685677927 0.411002094 0.06694017 0.486886687
## 5063 0.126183019 0.865109229 0.727005600 0.87242322 0.005778196
## 7500 0.015721492 0.294719770 0.160009731 0.19875419 0.553700816
## 7602 0.275181941 0.206238927 0.547838104 0.43973574 0.127514269
## 8778 0.032265935 0.033436108 0.453187703 0.09065251 0.002294111
## 1537 0.046933121 0.615108741 0.264005451 0.41530216 0.409521330
## 9288 0.456373776 0.069323713 0.038059732 0.10123836 0.018194802
## 9792 0.903956598 0.445928593 0.007063258 0.87253412 0.074379973
## 9790 0.779223585 0.278322207 0.149891596 0.87530752 0.610315370
## 8781 0.130710799 0.134940410 0.751984577 0.62923710 0.103517042
## 7791 0.105790732 0.552254509 0.025787581 0.03583595 0.468335861
## 9634 0.408722239 0.917520399 0.581862225 0.75899465 0.192870629
## 3769 0.043927157 0.062444638 0.117148699 0.15847950 0.046660649
## 5215 0.608503831 0.586691144 0.212917673 0.30648271 0.336166535
## 8455 0.007617060 0.007149591 0.085564983 0.77302809 0.003621616
## 5276 0.208395039 0.213480763 0.022925703 0.84478155 0.425983766
## 7031 0.167555380 0.851143185 0.927950431 0.15461430 0.199338673
## p.heart_disease p.ibd p.liver_cirrhosi p.long_covid p.lung_cancer
## 7530 0.003271376 0.44622272 0.0112609266 0.080024487 0.001313946
## 208 0.004672764 0.36857255 0.0009809045 0.839109868 0.003122823
## 9310 0.004672764 0.36857255 0.0009809045 0.839109868 0.003122823
## 2469 0.150850146 0.60681737 0.7489940988 0.842027283 0.453187703
## 5063 0.185610218 0.02685953 0.0981956471 0.349070979 0.113458722
## 7500 0.014448508 0.11533667 0.0423529173 0.003810332 0.015657786
## 7602 0.470064970 0.45035213 0.0564490453 0.569901693 0.393482341
## 8778 0.582586470 0.85261392 0.6104633702 0.086031515 0.109376655
## 1537 0.045181959 0.17163292 0.0251852562 0.011646892 0.050873683
## 9288 0.132314099 0.40240256 0.8281209751 0.163654747 0.272711828
## 9792 0.856701390 0.02873164 0.0320790646 0.777603871 0.892758121
## 9790 0.027397444 0.01871962 0.6318890379 0.974783574 0.122390372
## 8781 0.044023193 0.95239556 0.4867984691 0.462311796 0.021727968
## 7791 0.386301880 0.66627342 0.1396897671 0.191981807 0.309956559
## 9634 0.112562646 0.25910038 0.1260609498 0.275522772 0.105245096
## 3769 0.023502562 0.58471334 0.2406488717 0.687075816 0.440194317
## 5215 0.904748244 0.01785311 0.4099173409 0.011425267 0.561169461
## 8455 0.341548041 0.88049013 0.0343003463 0.001981162 0.328265800
## 5276 0.635499260 0.07984259 0.3897462276 0.437446985 0.728644576
## 7031 0.608638760 0.55095024 0.6293462654 0.010960894 0.032808348
## p.osteoarthritis p.ovarian_cancer p.parkinsons p.prostate_cance
## 7530 0.007219984 0.5929645344 0.046073576 0.170159095
## 208 0.140162229 0.7843589772 0.011572482 0.320094834
## 9310 0.140162229 0.7843589772 0.011572482 0.320094834
## 2469 0.204375072 0.0751839623 0.007404694 0.001211211
## 5063 0.005087430 0.7806821487 0.147810974 0.770004260
## 7500 0.847567551 0.0059100155 0.065086199 0.883655974
## 7602 0.021679602 0.0869038275 0.505962469 0.023951116
## 8778 0.510204064 0.3203004723 0.076181419 0.574115294
## 1537 0.624196655 0.0168222629 0.125001097 0.851399480
## 9288 0.834050944 0.5753150251 0.007096155 0.132803493
## 9792 0.060460812 0.0040542958 0.190666156 0.056512830
## 9790 0.002064437 0.5454025095 0.937825962 0.189119868
## 8781 0.341979179 0.0103852095 0.010141386 0.756370207
## 7791 0.724153596 0.1046715722 0.294194061 0.536827629
## 9634 0.023018863 0.6224540444 0.924171771 0.002124797
## 3769 0.307246977 0.3595316074 0.074151556 0.308984654
## 5215 0.190071561 0.0004139711 0.354996557 0.801345718
## 8455 0.003543336 0.0108564572 0.949955208 0.447766610
## 5276 0.578054833 0.0031114362 0.862286426 0.878526573
## 7031 0.088422759 0.0106580808 0.407693445 0.012485719
## p.rheumatoid_art p.stroke s.dist SD p.adjustMANOVA
## 7530 0.0146586664 0.002481473 2.481886 0.5459289 1.036309e-08
## 208 0.0630112555 0.503534226 2.042330 0.4411952 1.607998e-04
## 9310 0.0630112555 0.503534226 2.042330 0.4411952 1.607998e-04
## 2469 0.2094919805 0.435289388 1.870952 0.4408620 3.321482e-02
## 5063 0.0011251185 0.758137381 1.767848 0.4090072 2.481609e-02
## 7500 0.1818608063 0.787516908 1.748228 0.3835745 1.781638e-03
## 7602 0.9177997559 0.008061467 1.740831 0.3979147 3.654617e-02
## 8778 0.0649102972 0.873310514 1.711486 0.4004272 3.086064e-02
## 1537 0.2603803168 0.780736184 1.708672 0.3700332 4.521763e-02
## 9288 0.2514831982 0.074105942 1.683992 0.3925347 1.426252e-01
## 9792 0.3209522294 0.192942828 1.681577 0.3804905 1.273544e-01
## 9790 0.1670668575 0.214753062 1.662727 0.3753188 6.762971e-02
## 8781 0.3116369937 0.034323922 1.646742 0.3876947 3.268146e-02
## 7791 0.0031941293 0.271022449 1.634065 0.3785010 8.857584e-02
## 9634 0.0017146700 0.608195791 1.627284 0.3699823 2.333243e-01
## 3769 0.3509947557 0.114037731 1.625987 0.3827579 2.742901e-01
## 5215 0.0061138609 0.309073833 1.625006 0.3823339 8.732983e-02
## 8455 0.2574548429 0.545761971 1.612936 0.3627795 1.474283e-06
## 5276 0.0009701404 0.313120735 1.601081 0.3768928 2.045553e-01
## 7031 0.0020587850 0.916212941 1.598014 0.3752068 4.111082e-02#numsig manova
nrow(subset(mtable2,p.adjustMANOVA<0.05))## [1] 1474sig <- subset(mtable2,p.adjustMANOVA<0.05)Individual comparisons.
r1 <- lapply(1:length(l2),function(i) {
name=names(l2[i])
m <- l2[[i]]
m2 <- mitch_import(x=m,DEtype="prescored",geneTable=gt,geneIDcol="CpG")
mres <- mitch_calc(x=m2,genesets=gs_symbols,minsetsize=5,cores=2, priority="significance")
mtable <- mres$enrichment_result
#mitch_report(res=mres,outfile=paste("mitchreport_incid_",name,".html",sep="",overwrite=TRUE))
})## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.## 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: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.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)$set
length(dns)
} )
nums <- cbind(unlist(ndn),unlist(nup))
colnames(nums) <- c("dn","up")
nums## dn up
## [1,] 4 6
## [2,] 52 191
## [3,] 31 207
## [4,] 0 7
## [5,] 48 2
## [6,] 511 309
## [7,] 13 4
## [8,] 42 44
## [9,] 19 51
## [10,] 48 22
## [11,] 513 892
## [12,] 21 75
## [13,] 41 102
## [14,] 0 0
## [15,] 123 445
## [16,] 4 16
## [17,] 30 23
## [18,] 119 32
## [19,] 32 37rownames(nums) <- gsub("ibd","IBD",rownames(nums))
rownames(nums) <- gsub("_"," ",rownames(nums))
par(mar = c(5.1, 10.1, 4.1, 2.1))
barplot(t(nums),beside=TRUE,horiz=TRUE,las=1,
legend.text = c("down","up"),xlab="no. pathways")
abline(v=seq(0,200,50),lty=2,lwd=0.5,col="gray")
pdf("fig8c.pdf",height=6,width=4)
par(mar = c(5.1, 10.1, 4.1, 2.1))
barplot(t(nums),beside=TRUE,horiz=TRUE,las=1,
legend.text = c("down","up"),xlab="no. pathways")
abline(v=seq(0,200,50),lty=2,lwd=0.5,col="gray")
dev.off()## png
## 2r2 <- lapply(r1,function(r) {
ups <- head(subset(r,p.adjustANOVA<0.05 & s.dist > 0.3)$set,3)
dns <- head(subset(r,p.adjustANOVA<0.05 & s.dist < -0.3)$set,3)
list("ups"=ups,"dns"=dns)
} )
r2## [[1]]
## [[1]]$ups
## character(0)
##
## [[1]]$dns
## character(0)
##
##
## [[2]]
## [[2]]$ups
## [1] "GOBP_T_CELL_CYTOKINE_PRODUCTION"
## [2] "GOBP_FORELIMB_MORPHOGENESIS"
## [3] "GOBP_T_HELPER_1_TYPE_IMMUNE_RESPONSE"
##
## [[2]]$dns
## [1] "GOBP_KERATINIZATION"
## [2] "GOMF_METALLOCARBOXYPEPTIDASE_ACTIVITY"
## [3] "GOBP_NEGATIVE_REGULATION_OF_LEUKOCYTE_ADHESION_TO_VASCULAR_ENDOTHELIAL_CELL"
##
##
## [[3]]
## [[3]]$ups
## [1] "GOBP_MEGAKARYOCYTE_DIFFERENTIATION"
## [2] "GOBP_POSITIVE_REGULATION_OF_DNA_RECOMBINATION"
## [3] "GOCC_PROTON_TRANSPORTING_TWO_SECTOR_ATPASE_COMPLEX"
##
## [[3]]$dns
## [1] "GOMF_ODORANT_BINDING"
## [2] "GOBP_SENSORY_PERCEPTION_OF_BITTER_TASTE"
## [3] "GOMF_HIGH_VOLTAGE_GATED_CALCIUM_CHANNEL_ACTIVITY"
##
##
## [[4]]
## [[4]]$ups
## character(0)
##
## [[4]]$dns
## character(0)
##
##
## [[5]]
## [[5]]$ups
## character(0)
##
## [[5]]$dns
## [1] "GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME"
## [2] "GOCC_RIBOSOMAL_SUBUNIT"
## [3] "GOBP_CYTOPLASMIC_TRANSLATION"
##
##
## [[6]]
## [[6]]$ups
## [1] "GOMF_STRUCTURAL_CONSTITUENT_OF_SKIN_EPIDERMIS"
## [2] "GOBP_KERATINIZATION"
## [3] "GOMF_LIGAND_GATED_MONOATOMIC_CATION_CHANNEL_ACTIVITY"
##
## [[6]]$dns
## [1] "GOCC_ENDOPLASMIC_RETICULUM_PROTEIN_CONTAINING_COMPLEX"
## [2] "GOMF_STRUCTURAL_CONSTITUENT_OF_CHROMATIN"
## [3] "GOCC_CATALYTIC_STEP_2_SPLICEOSOME"
##
##
## [[7]]
## [[7]]$ups
## [1] "GOMF_C_C_CHEMOKINE_BINDING"
##
## [[7]]$dns
## character(0)
##
##
## [[8]]
## [[8]]$ups
## [1] "GOBP_SENSORY_PERCEPTION_OF_TASTE"
## [2] "GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY"
##
## [[8]]$dns
## [1] "GOCC_RIBOSOMAL_SUBUNIT"
## [2] "GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME"
## [3] "GOBP_CYTOPLASMIC_TRANSLATION"
##
##
## [[9]]
## [[9]]$ups
## [1] "GOBP_RESPONSE_TO_ZINC_ION_STARVATION"
##
## [[9]]$dns
## [1] "GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_ENDOGENOUS_ANTIGEN"
## [2] "GOCC_T_CELL_RECEPTOR_COMPLEX"
## [3] "GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_VIA_MHC_CLASS_IB"
##
##
## [[10]]
## [[10]]$ups
## [1] "GOBP_RESPONSE_TO_CHEMOKINE" "GOMF_CHEMOKINE_ACTIVITY"
## [3] "GOMF_CHEMOKINE_RECEPTOR_BINDING"
##
## [[10]]$dns
## character(0)
##
##
## [[11]]
## [[11]]$ups
## [1] "GOCC_CHROMOSOME_CENTROMERIC_REGION"
## [2] "GOCC_SPLICEOSOMAL_COMPLEX"
## [3] "GOBP_DNA_TEMPLATED_TRANSCRIPTION_ELONGATION"
##
## [[11]]$dns
## [1] "GOMF_VOLTAGE_GATED_CHANNEL_ACTIVITY"
## [2] "GOMF_VOLTAGE_GATED_MONOATOMIC_CATION_CHANNEL_ACTIVITY"
## [3] "GOMF_POTASSIUM_CHANNEL_ACTIVITY"
##
##
## [[12]]
## [[12]]$ups
## [1] "GOBP_EMBRYONIC_DIGESTIVE_TRACT_DEVELOPMENT"
## [2] "GOBP_FOREBRAIN_REGIONALIZATION"
## [3] "GOBP_DOPAMINERGIC_NEURON_DIFFERENTIATION"
##
## [[12]]$dns
## [1] "GOMF_TASTE_RECEPTOR_ACTIVITY"
## [2] "GOMF_BITTER_TASTE_RECEPTOR_ACTIVITY"
## [3] "GOBP_CALCIUM_DEPENDENT_CELL_CELL_ADHESION_VIA_PLASMA_MEMBRANE_CELL_ADHESION_MOLECULES"
##
##
## [[13]]
## [[13]]$ups
## [1] "GOBP_POSITIVE_REGULATION_OF_BIOMINERAL_TISSUE_DEVELOPMENT"
## [2] "GOBP_REGULATION_OF_ADENYLATE_CYCLASE_ACTIVITY"
## [3] "GOBP_ACTIVATION_OF_ADENYLATE_CYCLASE_ACTIVITY"
##
## [[13]]$dns
## [1] "GOCC_T_CELL_RECEPTOR_COMPLEX"
## [2] "GOMF_GROWTH_HORMONE_RECEPTOR_BINDING"
##
##
## [[14]]
## [[14]]$ups
## character(0)
##
## [[14]]$dns
## character(0)
##
##
## [[15]]
## [[15]]$ups
## [1] "GOBP_DOPAMINERGIC_NEURON_DIFFERENTIATION"
## [2] "GOMF_PROSTANOID_RECEPTOR_ACTIVITY"
## [3] "GOBP_TELENCEPHALON_REGIONALIZATION"
##
## [[15]]$dns
## [1] "GOBP_DETECTION_OF_STIMULUS_INVOLVED_IN_SENSORY_PERCEPTION"
## [2] "GOBP_SENSORY_PERCEPTION_OF_CHEMICAL_STIMULUS"
## [3] "GOMF_OLFACTORY_RECEPTOR_ACTIVITY"
##
##
## [[16]]
## [[16]]$ups
## character(0)
##
## [[16]]$dns
## [1] "GOMF_NEUROPEPTIDE_ACTIVITY"
##
##
## [[17]]
## [[17]]$ups
## [1] "GOCC_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT"
## [2] "GOCC_SMALL_SUBUNIT_PROCESSOME"
## [3] "GOCC_SMALL_RIBOSOMAL_SUBUNIT"
##
## [[17]]$dns
## [1] "GOBP_NEUTROPHIL_DEGRANULATION"
## [2] "GOBP_NEUTROPHIL_ACTIVATION_INVOLVED_IN_IMMUNE_RESPONSE"
##
##
## [[18]]
## [[18]]$ups
## [1] "GOBP_KERATINIZATION"
## [2] "GOMF_STRUCTURAL_CONSTITUENT_OF_SKIN_EPIDERMIS"
## [3] "GOBP_CALCIUM_ION_IMPORT_ACROSS_PLASMA_MEMBRANE"
##
## [[18]]$dns
## [1] "GOBP_NEURON_FATE_COMMITMENT" "GOBP_FOREBRAIN_REGIONALIZATION"
## [3] "GOBP_URETER_DEVELOPMENT"
##
##
## [[19]]
## [[19]]$ups
## [1] "GOCC_IMMUNOLOGICAL_SYNAPSE"
## [2] "GOMF_LIGAND_GATED_CALCIUM_CHANNEL_ACTIVITY"
##
## [[19]]$dns
## [1] "GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT"
## [2] "GOMF_C_ACYLTRANSFERASE_ACTIVITY"
## [3] "GOCC_BOX_H_ACA_RNP_COMPLEX"gsets <- unique(unname(unlist(r2)))
gsets## [1] "GOBP_T_CELL_CYTOKINE_PRODUCTION"
## [2] "GOBP_FORELIMB_MORPHOGENESIS"
## [3] "GOBP_T_HELPER_1_TYPE_IMMUNE_RESPONSE"
## [4] "GOBP_KERATINIZATION"
## [5] "GOMF_METALLOCARBOXYPEPTIDASE_ACTIVITY"
## [6] "GOBP_NEGATIVE_REGULATION_OF_LEUKOCYTE_ADHESION_TO_VASCULAR_ENDOTHELIAL_CELL"
## [7] "GOBP_MEGAKARYOCYTE_DIFFERENTIATION"
## [8] "GOBP_POSITIVE_REGULATION_OF_DNA_RECOMBINATION"
## [9] "GOCC_PROTON_TRANSPORTING_TWO_SECTOR_ATPASE_COMPLEX"
## [10] "GOMF_ODORANT_BINDING"
## [11] "GOBP_SENSORY_PERCEPTION_OF_BITTER_TASTE"
## [12] "GOMF_HIGH_VOLTAGE_GATED_CALCIUM_CHANNEL_ACTIVITY"
## [13] "GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME"
## [14] "GOCC_RIBOSOMAL_SUBUNIT"
## [15] "GOBP_CYTOPLASMIC_TRANSLATION"
## [16] "GOMF_STRUCTURAL_CONSTITUENT_OF_SKIN_EPIDERMIS"
## [17] "GOMF_LIGAND_GATED_MONOATOMIC_CATION_CHANNEL_ACTIVITY"
## [18] "GOCC_ENDOPLASMIC_RETICULUM_PROTEIN_CONTAINING_COMPLEX"
## [19] "GOMF_STRUCTURAL_CONSTITUENT_OF_CHROMATIN"
## [20] "GOCC_CATALYTIC_STEP_2_SPLICEOSOME"
## [21] "GOMF_C_C_CHEMOKINE_BINDING"
## [22] "GOBP_SENSORY_PERCEPTION_OF_TASTE"
## [23] "GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY"
## [24] "GOBP_RESPONSE_TO_ZINC_ION_STARVATION"
## [25] "GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_ENDOGENOUS_ANTIGEN"
## [26] "GOCC_T_CELL_RECEPTOR_COMPLEX"
## [27] "GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_VIA_MHC_CLASS_IB"
## [28] "GOBP_RESPONSE_TO_CHEMOKINE"
## [29] "GOMF_CHEMOKINE_ACTIVITY"
## [30] "GOMF_CHEMOKINE_RECEPTOR_BINDING"
## [31] "GOCC_CHROMOSOME_CENTROMERIC_REGION"
## [32] "GOCC_SPLICEOSOMAL_COMPLEX"
## [33] "GOBP_DNA_TEMPLATED_TRANSCRIPTION_ELONGATION"
## [34] "GOMF_VOLTAGE_GATED_CHANNEL_ACTIVITY"
## [35] "GOMF_VOLTAGE_GATED_MONOATOMIC_CATION_CHANNEL_ACTIVITY"
## [36] "GOMF_POTASSIUM_CHANNEL_ACTIVITY"
## [37] "GOBP_EMBRYONIC_DIGESTIVE_TRACT_DEVELOPMENT"
## [38] "GOBP_FOREBRAIN_REGIONALIZATION"
## [39] "GOBP_DOPAMINERGIC_NEURON_DIFFERENTIATION"
## [40] "GOMF_TASTE_RECEPTOR_ACTIVITY"
## [41] "GOMF_BITTER_TASTE_RECEPTOR_ACTIVITY"
## [42] "GOBP_CALCIUM_DEPENDENT_CELL_CELL_ADHESION_VIA_PLASMA_MEMBRANE_CELL_ADHESION_MOLECULES"
## [43] "GOBP_POSITIVE_REGULATION_OF_BIOMINERAL_TISSUE_DEVELOPMENT"
## [44] "GOBP_REGULATION_OF_ADENYLATE_CYCLASE_ACTIVITY"
## [45] "GOBP_ACTIVATION_OF_ADENYLATE_CYCLASE_ACTIVITY"
## [46] "GOMF_GROWTH_HORMONE_RECEPTOR_BINDING"
## [47] "GOMF_PROSTANOID_RECEPTOR_ACTIVITY"
## [48] "GOBP_TELENCEPHALON_REGIONALIZATION"
## [49] "GOBP_DETECTION_OF_STIMULUS_INVOLVED_IN_SENSORY_PERCEPTION"
## [50] "GOBP_SENSORY_PERCEPTION_OF_CHEMICAL_STIMULUS"
## [51] "GOMF_OLFACTORY_RECEPTOR_ACTIVITY"
## [52] "GOMF_NEUROPEPTIDE_ACTIVITY"
## [53] "GOCC_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT"
## [54] "GOCC_SMALL_SUBUNIT_PROCESSOME"
## [55] "GOCC_SMALL_RIBOSOMAL_SUBUNIT"
## [56] "GOBP_NEUTROPHIL_DEGRANULATION"
## [57] "GOBP_NEUTROPHIL_ACTIVATION_INVOLVED_IN_IMMUNE_RESPONSE"
## [58] "GOBP_CALCIUM_ION_IMPORT_ACROSS_PLASMA_MEMBRANE"
## [59] "GOBP_NEURON_FATE_COMMITMENT"
## [60] "GOBP_URETER_DEVELOPMENT"
## [61] "GOCC_IMMUNOLOGICAL_SYNAPSE"
## [62] "GOMF_LIGAND_GATED_CALCIUM_CHANNEL_ACTIVITY"
## [63] "GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT"
## [64] "GOMF_C_ACYLTRANSFERASE_ACTIVITY"
## [65] "GOCC_BOX_H_ACA_RNP_COMPLEX"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(1,4:22)]
rownames(top) <- top$set
top$set=NULL
head(top,2)## s.alzheimers s.breast_cancer
## GOCC_T_CELL_RECEPTOR_COMPLEX 0.20664377 0.1277341
## GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY -0.01209256 -0.2702005
## s.chronic_pain s.CKD
## GOCC_T_CELL_RECEPTOR_COMPLEX 0.06777373 0.4299844
## GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY -0.04396963 -0.1331818
## s.colorectal_can s.COPD
## GOCC_T_CELL_RECEPTOR_COMPLEX -0.4088178 0.34126090
## GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY 0.4612265 -0.04248761
## s.covid_hospital s.diabetes
## GOCC_T_CELL_RECEPTOR_COMPLEX 0.5241919 -0.1622051
## GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY -0.2009820 0.6802655
## s.heart_disease s.ibd
## GOCC_T_CELL_RECEPTOR_COMPLEX -0.6168815 -0.2074482
## GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY 0.1515388 -0.1240533
## s.liver_cirrhosi s.long_covid
## GOCC_T_CELL_RECEPTOR_COMPLEX 0.2825736 0.1664650
## GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY 0.5511297 -0.2108469
## s.lung_cancer s.osteoarthritis
## GOCC_T_CELL_RECEPTOR_COMPLEX -0.5723869 0.2850987
## GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY -0.4617993 -0.1061145
## s.ovarian_cancer s.parkinsons
## GOCC_T_CELL_RECEPTOR_COMPLEX -0.1095963 0.3905611
## GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY -0.1200982 -0.5388189
## s.prostate_cance
## GOCC_T_CELL_RECEPTOR_COMPLEX -0.1413323
## GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY -0.4407692
## s.rheumatoid_art s.stroke
## GOCC_T_CELL_RECEPTOR_COMPLEX 0.2725778 0.5711068
## GOMF_HISTONE_H3K4_TRIMETHYLTRANSFERASE_ACTIVITY -0.2124290 0.2552439rownames(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=0.7)
dev.off()## png
## 2Make a special heatmap for the cancer bioinformamtics symposium.
names(r1) <- names(l2)
r3 <- r1[grep("cancer",names(r1))]
r4 <- lapply(r3,function(r) {
r <- subset(r,p.adjustANOVA<0.05)
ups <- head(r[order(-r$s.dist),],3)$set
dns <- head(r[order(r$s.dist),],3)$set
list("ups"=ups,"dns"=dns)
} )
r4## $breast_cancer
## $breast_cancer$ups
## [1] "GOBP_RESPONSE_TO_SELENIUM_ION"
## [2] "GOBP_NEGATIVE_REGULATION_OF_IMMUNOGLOBULIN_PRODUCTION"
## [3] "GOBP_LATERAL_MESODERM_DEVELOPMENT"
##
## $breast_cancer$dns
## [1] "GOBP_NEGATIVE_REGULATION_OF_LEUKOCYTE_ADHESION_TO_VASCULAR_ENDOTHELIAL_CELL"
## [2] "GOMF_ATPASE_COUPLED_INORGANIC_ANION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY"
## [3] "GOBP_POSITIVE_REGULATION_OF_LIPOPROTEIN_PARTICLE_CLEARANCE"
##
##
## $colorectal_cancer
## $colorectal_cancer$ups
## [1] "GOCC_RNAI_EFFECTOR_COMPLEX"
## [2] "GOBP_REGULATORY_NCRNA_MEDIATED_GENE_SILENCING"
## [3] "GOCC_CHROMOSOME"
##
## $colorectal_cancer$dns
## [1] "GOCC_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT"
## [2] "GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT"
## [3] "GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME"
##
##
## $lung_cancer
## $lung_cancer$ups
## [1] "GOBP_METANEPHRIC_GLOMERULUS_VASCULATURE_DEVELOPMENT"
## [2] "GOBP_CARDIAC_CHAMBER_FORMATION"
## [3] "GOMF_BMP_RECEPTOR_BINDING"
##
## $lung_cancer$dns
## [1] "GOMF_GROWTH_HORMONE_RECEPTOR_BINDING"
## [2] "GOCC_T_CELL_RECEPTOR_COMPLEX"
## [3] "GOBP_PROTEIN_AUTOUBIQUITINATION"
##
##
## $ovarian_cancer
## $ovarian_cancer$ups
## [1] "GOBP_REGULATION_OF_AMACRINE_CELL_DIFFERENTIATION"
## [2] "GOBP_NEGATIVE_REGULATION_OF_MHC_CLASS_II_BIOSYNTHETIC_PROCESS"
## [3] "GOBP_REGULATION_OF_MESODERMAL_CELL_DIFFERENTIATION"
##
## $ovarian_cancer$dns
## [1] "GOBP_REGULATION_OF_ENAMEL_MINERALIZATION"
## [2] "GOMF_GUANYLATE_CYCLASE_REGULATOR_ACTIVITY"
## [3] "GOBP_INTESTINAL_CHOLESTEROL_ABSORPTION"
##
##
## $prostate_cancer
## $prostate_cancer$ups
## [1] "GOCC_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT"
## [2] "GOCC_SMALL_SUBUNIT_PROCESSOME"
## [3] "GOCC_SMALL_RIBOSOMAL_SUBUNIT"
##
## $prostate_cancer$dns
## [1] "GOBP_NEUTROPHIL_DEGRANULATION"
## [2] "GOBP_NEUTROPHIL_ACTIVATION_INVOLVED_IN_IMMUNE_RESPONSE"
## [3] "GOBP_NEUROPEPTIDE_SIGNALING_PATHWAY"gsets <- unique(unname(unlist(r4)))
gsets## [1] "GOBP_RESPONSE_TO_SELENIUM_ION"
## [2] "GOBP_NEGATIVE_REGULATION_OF_IMMUNOGLOBULIN_PRODUCTION"
## [3] "GOBP_LATERAL_MESODERM_DEVELOPMENT"
## [4] "GOBP_NEGATIVE_REGULATION_OF_LEUKOCYTE_ADHESION_TO_VASCULAR_ENDOTHELIAL_CELL"
## [5] "GOMF_ATPASE_COUPLED_INORGANIC_ANION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY"
## [6] "GOBP_POSITIVE_REGULATION_OF_LIPOPROTEIN_PARTICLE_CLEARANCE"
## [7] "GOCC_RNAI_EFFECTOR_COMPLEX"
## [8] "GOBP_REGULATORY_NCRNA_MEDIATED_GENE_SILENCING"
## [9] "GOCC_CHROMOSOME"
## [10] "GOCC_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT"
## [11] "GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT"
## [12] "GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME"
## [13] "GOBP_METANEPHRIC_GLOMERULUS_VASCULATURE_DEVELOPMENT"
## [14] "GOBP_CARDIAC_CHAMBER_FORMATION"
## [15] "GOMF_BMP_RECEPTOR_BINDING"
## [16] "GOMF_GROWTH_HORMONE_RECEPTOR_BINDING"
## [17] "GOCC_T_CELL_RECEPTOR_COMPLEX"
## [18] "GOBP_PROTEIN_AUTOUBIQUITINATION"
## [19] "GOBP_REGULATION_OF_AMACRINE_CELL_DIFFERENTIATION"
## [20] "GOBP_NEGATIVE_REGULATION_OF_MHC_CLASS_II_BIOSYNTHETIC_PROCESS"
## [21] "GOBP_REGULATION_OF_MESODERMAL_CELL_DIFFERENTIATION"
## [22] "GOBP_REGULATION_OF_ENAMEL_MINERALIZATION"
## [23] "GOMF_GUANYLATE_CYCLASE_REGULATOR_ACTIVITY"
## [24] "GOBP_INTESTINAL_CHOLESTEROL_ABSORPTION"
## [25] "GOCC_SMALL_SUBUNIT_PROCESSOME"
## [26] "GOCC_SMALL_RIBOSOMAL_SUBUNIT"
## [27] "GOBP_NEUTROPHIL_DEGRANULATION"
## [28] "GOBP_NEUTROPHIL_ACTIVATION_INVOLVED_IN_IMMUNE_RESPONSE"
## [29] "GOBP_NEUROPEPTIDE_SIGNALING_PATHWAY"top <- mtable2[which(mtable2$set %in% gsets),]
top <- top[,c(1,4:22)]
top <- top[,colnames(top) %in% c("set","s.breast_cancer","s.colorectal_can","s.lung_cancer","s.ovarian_cancer","s.prostate_cance")]
rownames(top) <- top$set
top$set=NULL
head(top,3)## s.breast_cancer
## GOBP_REGULATION_OF_AMACRINE_CELL_DIFFERENTIATION 0.3597412
## GOCC_T_CELL_RECEPTOR_COMPLEX 0.1277341
## GOBP_NEGATIVE_REGULATION_OF_MHC_CLASS_II_BIOSYNTHETIC_PROCESS 0.1936882
## s.colorectal_can
## GOBP_REGULATION_OF_AMACRINE_CELL_DIFFERENTIATION -0.1281608
## GOCC_T_CELL_RECEPTOR_COMPLEX -0.4088178
## GOBP_NEGATIVE_REGULATION_OF_MHC_CLASS_II_BIOSYNTHETIC_PROCESS 0.1383270
## s.lung_cancer
## GOBP_REGULATION_OF_AMACRINE_CELL_DIFFERENTIATION -0.1369938
## GOCC_T_CELL_RECEPTOR_COMPLEX -0.5723869
## GOBP_NEGATIVE_REGULATION_OF_MHC_CLASS_II_BIOSYNTHETIC_PROCESS 0.7355878
## s.ovarian_cancer
## GOBP_REGULATION_OF_AMACRINE_CELL_DIFFERENTIATION 0.8323107
## GOCC_T_CELL_RECEPTOR_COMPLEX -0.1095963
## GOBP_NEGATIVE_REGULATION_OF_MHC_CLASS_II_BIOSYNTHETIC_PROCESS 0.7460570
## s.prostate_cance
## GOBP_REGULATION_OF_AMACRINE_CELL_DIFFERENTIATION 0.05931549
## GOCC_T_CELL_RECEPTOR_COMPLEX -0.14133225
## GOBP_NEGATIVE_REGULATION_OF_MHC_CLASS_II_BIOSYNTHETIC_PROCESS -0.34486311rownames(top) <- gsub("REACTOME_","",rownames(top))
rownames(top) <- gsub("_"," ",rownames(top))
colnames(top) <- c("breast","colorectal","lung","ovarian","prostate")
head(top)## breast
## GOBP REGULATION OF AMACRINE CELL DIFFERENTIATION 0.3597412
## GOCC T CELL RECEPTOR COMPLEX 0.1277341
## GOBP NEGATIVE REGULATION OF MHC CLASS II BIOSYNTHETIC PROCESS 0.1936882
## GOBP NEGATIVE REGULATION OF LEUKOCYTE ADHESION TO VASCULAR ENDOTHELIAL CELL -0.6173271
## GOBP METANEPHRIC GLOMERULUS VASCULATURE DEVELOPMENT 0.2956632
## GOMF GUANYLATE CYCLASE REGULATOR ACTIVITY -0.1984295
## colorectal
## GOBP REGULATION OF AMACRINE CELL DIFFERENTIATION -0.12816084
## GOCC T CELL RECEPTOR COMPLEX -0.40881779
## GOBP NEGATIVE REGULATION OF MHC CLASS II BIOSYNTHETIC PROCESS 0.13832705
## GOBP NEGATIVE REGULATION OF LEUKOCYTE ADHESION TO VASCULAR ENDOTHELIAL CELL 0.40129610
## GOBP METANEPHRIC GLOMERULUS VASCULATURE DEVELOPMENT -0.21389823
## GOMF GUANYLATE CYCLASE REGULATOR ACTIVITY 0.05311605
## lung
## GOBP REGULATION OF AMACRINE CELL DIFFERENTIATION -0.13699377
## GOCC T CELL RECEPTOR COMPLEX -0.57238687
## GOBP NEGATIVE REGULATION OF MHC CLASS II BIOSYNTHETIC PROCESS 0.73558778
## GOBP NEGATIVE REGULATION OF LEUKOCYTE ADHESION TO VASCULAR ENDOTHELIAL CELL -0.07885731
## GOBP METANEPHRIC GLOMERULUS VASCULATURE DEVELOPMENT 0.71028679
## GOMF GUANYLATE CYCLASE REGULATOR ACTIVITY -0.14829083
## ovarian
## GOBP REGULATION OF AMACRINE CELL DIFFERENTIATION 0.83231065
## GOCC T CELL RECEPTOR COMPLEX -0.10959632
## GOBP NEGATIVE REGULATION OF MHC CLASS II BIOSYNTHETIC PROCESS 0.74605700
## GOBP NEGATIVE REGULATION OF LEUKOCYTE ADHESION TO VASCULAR ENDOTHELIAL CELL -0.08702822
## GOBP METANEPHRIC GLOMERULUS VASCULATURE DEVELOPMENT 0.41701771
## GOMF GUANYLATE CYCLASE REGULATOR ACTIVITY -0.65843902
## prostate
## GOBP REGULATION OF AMACRINE CELL DIFFERENTIATION 0.05931549
## GOCC T CELL RECEPTOR COMPLEX -0.14133225
## GOBP NEGATIVE REGULATION OF MHC CLASS II BIOSYNTHETIC PROCESS -0.34486311
## GOBP NEGATIVE REGULATION OF LEUKOCYTE ADHESION TO VASCULAR ENDOTHELIAL CELL 0.10381226
## GOBP METANEPHRIC GLOMERULUS VASCULATURE DEVELOPMENT 0.14075017
## GOMF GUANYLATE CYCLASE REGULATOR ACTIVITY 0.15383654saveRDS(top,file="cancerheat.Rds")
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(as.matrix(top),scale="none",trace="none",margins=c(10,25),
col=colfunc(25),cexRow=0.75,cexCol=1.2)
pdf("cancerheat.pdf",height=7,width=7)
heatmap.2(as.matrix(top),scale="none",trace="none",margins=c(10,25),
col=colfunc(25),cexRow=0.7,cexCol=1.2)
dev.off()## png
## 2Comparison of prevalence and incidence
diseases <- colnames(m2)[which(colnames(m2) %in% colnames(m1))]
dres <- lapply(diseases, function(d) {
mydisease <- d
df <- data.frame( m1[,which(colnames(m1)==mydisease)] ,
m2[,which(colnames(m2)==mydisease)] , row.names=rownames(m1))
colnames(df) <- c("prev","indic")
dres <- mitch_calc(df,genesets=gs_symbols,minsetsize=5,cores=6,priority="effect")
#subset(dres$enrichment_result,p.adjustMANOVA<0.05)
})## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.
## Note: Enrichments with large effect sizes may not be
## statistically significant.names(dres) <- diseases
a <- lapply(dres,function(d) {
sig <- nrow(subset(d$enrichment_result,p.adjustMANOVA<0.05))
same <- nrow(subset(d$enrichment_result, p.adjustMANOVA<0.05 & sign(s.prev) == sign(s.indic) ) )
diff = sig - same
return(c(sig,same,diff))
})
a <- do.call(rbind,a)
colnames(a) <- c("sig","same","different")
lapply(dres,function(d) {
subset(d$enrichment_result,p.adjustMANOVA<0.05) %>% kbl %>% kable_paper("hover", full_width = F)
})## $alzheimers
##
## $breast_cancer
##
## $chronic_pain
##
## $CKD
##
## $colorectal_cancer
##
## $COPD
##
## $diabetes
##
## $heart_disease
##
## $lung_cancer
##
## $osteoarthritis
##
## $parkinsons
##
## $prostate_cancer
##
## $rheumatoid_arthritis
##
## $strokelapply(1:length(dres),function(i) {
d <- dres[[i]]
dname <- names(dres)[i]
res <- d$enrichment_result
sig <- subset(res,p.adjustMANOVA<0.05)
P = signif(cor(res$s.prev,res$s.indic),3)
S = signif(cor(res$s.prev,res$s.indic,method="spearman"),3)
HEADER=paste("no sig=",nrow(sig),"P=",P,"S=",S)
plot(res$s.prev,res$s.indic,pch=19,cex=0.8,main=dname)
mtext(HEADER)
abline(v=0,h=0,lty=2,col="blue")
points(sig$s.prev,sig$s.indic,pch=19,col="red")
})
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## NULLSession information
save.image("multi_ewas_example.Rdata")
sessionInfo()## R version 4.4.1 (2024-06-14)
## 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] png_0.1-8
## [2] gridExtra_2.3
## [3] missMethyl_1.38.0
## [4] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
## [5] beeswarm_0.4.0
## [6] kableExtra_1.4.0
## [7] gplots_3.1.3.1
## [8] mitch_1.16.0
## [9] tictoc_1.2.1
## [10] HGNChelper_0.8.14
## [11] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [12] IlluminaHumanMethylation450kmanifest_0.4.0
## [13] minfi_1.50.0
## [14] bumphunter_1.46.0
## [15] locfit_1.5-9.10
## [16] iterators_1.0.14
## [17] foreach_1.5.2
## [18] Biostrings_2.72.0
## [19] XVector_0.44.0
## [20] SummarizedExperiment_1.34.0
## [21] Biobase_2.64.0
## [22] MatrixGenerics_1.16.0
## [23] matrixStats_1.3.0
## [24] GenomicRanges_1.56.0
## [25] GenomeInfoDb_1.40.0
## [26] IRanges_2.38.0
## [27] S4Vectors_0.42.0
## [28] BiocGenerics_0.50.0
## [29] eulerr_7.0.2
## [30] limma_3.60.0
##
## loaded via a namespace (and not attached):
## [1] splines_4.4.1 later_1.3.2
## [3] BiocIO_1.14.0 bitops_1.0-7
## [5] tibble_3.2.1 preprocessCore_1.66.0
## [7] XML_3.99-0.17 lifecycle_1.0.4
## [9] lattice_0.22-6 MASS_7.3-61
## [11] base64_2.0.1 scrime_1.3.5
## [13] magrittr_2.0.3 sass_0.4.9
## [15] rmarkdown_2.27 jquerylib_0.1.4
## [17] yaml_2.3.9 httpuv_1.6.15
## [19] doRNG_1.8.6 askpass_1.2.0
## [21] DBI_1.2.3 RColorBrewer_1.1-3
## [23] abind_1.4-5 zlibbioc_1.50.0
## [25] quadprog_1.5-8 purrr_1.0.2
## [27] RCurl_1.98-1.14 GenomeInfoDbData_1.2.12
## [29] genefilter_1.86.0 annotate_1.82.0
## [31] svglite_2.1.3 DelayedMatrixStats_1.26.0
## [33] codetools_0.2-20 DelayedArray_0.30.1
## [35] xml2_1.3.6 tidyselect_1.2.1
## [37] UCSC.utils_1.0.0 beanplot_1.3.1
## [39] illuminaio_0.46.0 GenomicAlignments_1.40.0
## [41] jsonlite_1.8.8 multtest_2.60.0
## [43] survival_3.7-0 systemfonts_1.1.0
## [45] tools_4.4.1 Rcpp_1.0.12
## [47] glue_1.7.0 SparseArray_1.4.3
## [49] xfun_0.45 dplyr_1.1.4
## [51] HDF5Array_1.32.0 fastmap_1.2.0
## [53] GGally_2.2.1 rhdf5filters_1.16.0
## [55] fansi_1.0.6 openssl_2.2.0
## [57] caTools_1.18.2 digest_0.6.36
## [59] R6_2.5.1 mime_0.12
## [61] colorspace_2.1-0 gtools_3.9.5
## [63] RSQLite_2.3.7 utf8_1.2.4
## [65] tidyr_1.3.1 generics_0.1.3
## [67] data.table_1.15.4 rtracklayer_1.64.0
## [69] httr_1.4.7 htmlwidgets_1.6.4
## [71] S4Arrays_1.4.0 ggstats_0.6.0
## [73] pkgconfig_2.0.3 gtable_0.3.5
## [75] blob_1.2.4 siggenes_1.78.0
## [77] htmltools_0.5.8.1 echarts4r_0.4.5
## [79] scales_1.3.0 knitr_1.48
## [81] rstudioapi_0.16.0 reshape2_1.4.4
## [83] tzdb_0.4.0 rjson_0.2.21
## [85] nlme_3.1-165 curl_5.2.1
## [87] org.Hs.eg.db_3.19.1 cachem_1.1.0
## [89] rhdf5_2.48.0 stringr_1.5.1
## [91] KernSmooth_2.23-24 AnnotationDbi_1.66.0
## [93] restfulr_0.0.15 GEOquery_2.72.0
## [95] pillar_1.9.0 grid_4.4.1
## [97] reshape_0.8.9 vctrs_0.6.5
## [99] promises_1.3.0 xtable_1.8-4
## [101] evaluate_0.24.0 readr_2.1.5
## [103] GenomicFeatures_1.56.0 cli_3.6.3
## [105] compiler_4.4.1 Rsamtools_2.20.0
## [107] rlang_1.1.4 crayon_1.5.3
## [109] rngtools_1.5.2 nor1mix_1.3-3
## [111] mclust_6.1.1 plyr_1.8.9
## [113] stringi_1.8.4 viridisLite_0.4.2
## [115] BiocParallel_1.38.0 munsell_0.5.1
## [117] Matrix_1.7-0 hms_1.1.3
## [119] sparseMatrixStats_1.16.0 bit64_4.0.5
## [121] ggplot2_3.5.1 Rhdf5lib_1.26.0
## [123] KEGGREST_1.44.0 statmod_1.5.0
## [125] shiny_1.8.1.1 highr_0.11
## [127] memoise_2.0.1.9000 bslib_0.7.0
## [129] bit_4.0.5 splitstackshape_1.4.8