Source: https://github.com/markziemann/TODO

Introduction

Here we are establishing the methylation profile of ovarian cancer versus healthy tissue in order to understand whether this signature can be observed in the blood of individuals who will soon be diagnosed with the disease.

suppressPackageStartupMessages({
  library("mitch")
  library("HGNChelper")
  library("IlluminaHumanMethylation450kanno.ilmn12.hg19")
  library("IlluminaHumanMethylationEPICanno.ilm10b4.hg19")
  library("kableExtra")
  library("vioplot")
  library("beeswarm")
  library("RhpcBLASctl")
  library("eulerr")
  library("gplots")
})

RhpcBLASctl::blas_set_num_threads(1)

Load cancer data

dm <- readRDS("GSE199057_limma.rds")
head(dm)
##               logFC   AveExpr        t      P.Value    adj.P.Val        B
## cg18430990 2.258776 -1.647987 28.17418 3.225054e-63 2.504322e-57 132.9933
## cg16306898 3.444725 -2.803218 26.80747 2.061958e-60 8.005768e-55 126.6744
## cg16601494 3.973390 -2.205109 26.51770 8.338578e-60 2.158360e-54 125.3060
## cg17301223 2.883374 -1.007534 26.12827 5.536985e-59 1.074896e-53 123.4511
## cg03241244 3.747524 -1.779332 25.27802 3.676459e-57 5.709695e-52 119.3366
## cg16300300 2.629925 -1.230023 24.80718 3.896248e-56 5.042531e-51 117.0195
anno <- getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)

myann <- data.frame(anno[,c("UCSC_RefGene_Name","UCSC_RefGene_Group","Islands_Name","Relation_to_Island")])

head(myann)
##            UCSC_RefGene_Name UCSC_RefGene_Group              Islands_Name
## cg18478105            YTHDF1             TSS200   chr20:61846843-61848103
## cg09835024            EIF2S3            TSS1500    chrX:24072558-24073135
## cg14361672              PKN3            TSS1500  chr9:131464843-131465830
## cg01763666            CCDC57               Body                          
## cg12950382         INF2;INF2          Body;Body                          
## cg02115394       CDC16;CDC16      TSS200;TSS200 chr13:115000148-115000874
##            Relation_to_Island
## cg18478105             Island
## cg09835024             Island
## cg14361672            N_Shore
## cg01763666            OpenSea
## cg12950382            OpenSea
## cg02115394             Island
gp <- myann[,"UCSC_RefGene_Name",drop=FALSE]
gp2 <- strsplit(gp$UCSC_RefGene_Name,";")
names(gp2) <- rownames(gp)
gp2 <- lapply(gp2,unique)
gt1 <- stack(gp2)
colnames(gt1) <- c("gene","probe")
gt1$probe <- as.character(gt1$probe)
dim(gt1)
## [1] 684970      2
head(gt1)
##     gene      probe
## 1 YTHDF1 cg18478105
## 2 EIF2S3 cg09835024
## 3   PKN3 cg14361672
## 4 CCDC57 cg01763666
## 5   INF2 cg12950382
## 6  CDC16 cg02115394

Load gene sets

gs1 <- mitch::gmt_import("../../ReactomePathways_2024-12-13.gmt")
gs2 <- mitch::gmt_import("../../c5.go.v2024.1.Hs.symbols.gmt")
names(gs2) <- gsub("_"," ",names(gs2))

gs <- c(gs1,gs2)

gs3 <- mitch::gmt_import("../../go_2024-11.gmt")
gs3 <- gs3[which(lapply(gs3,length)>5)]
length(gs3)
## [1] 6929

Update deprecated gene symbols

new.hgnc.table <- getCurrentHumanMap()
## Using the already downloaded hgnc_complete_set.txt file.
fix <- checkGeneSymbols(gt1$gene,map=new.hgnc.table)
## Warning in checkGeneSymbols(gt1$gene, map = new.hgnc.table): Human gene symbols
## should be all upper-case except for the 'orf' in open reading frames. The case
## of some letters was corrected.
## Warning in checkGeneSymbols(gt1$gene, map = new.hgnc.table): x contains
## non-approved gene symbols
fix2 <- fix[which(fix$x != fix$Suggested.Symbol),]
length(unique(fix2$x))
## [1] 3300
gt1$gene <- fix$Suggested.Symbol
head(gt1)
##     gene      probe
## 1 YTHDF1 cg18478105
## 2 EIF2S3 cg09835024
## 3   PKN3 cg14361672
## 4 CCDC57 cg01763666
## 5   INF2 cg12950382
## 6  CDC16 cg02115394

Run mitch (cancer v normal)

y <- mitch_import(dm,DEtype="limma",geneTable=gt1)
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 776521
## Note: no. genes in output = 22227
## Warning in mitch_import(dm, DEtype = "limma", geneTable = gt1): Warning: less than half of the input genes are also in the
##         output
head(y)
##                  x
## A1BG      1.256876
## A1BG-AS1 -1.418138
## A1CF      2.954785
## A2M      -4.988589
## A2M-AS1  -2.232500
## A2ML1    -3.565671
y <- y[order(y$x),,drop=FALSE]

head(y)
##                  x
## APOOP5   -15.82582
## OR5J2    -15.37975
## FAM133CP -15.35387
## CDRT15P3 -15.14757
## MIR622   -14.64271
## OR56B4   -14.55324
tail(y)
##                   x
## MIR552     12.41409
## HOXA2      12.67896
## SNX29P1    13.01832
## MIR4487    13.75684
## ZNF793-AS1 13.84461
## MIR4730    16.07294
res <- mitch_calc(y,gs,priority="effect",cores=12,minsetsize=5)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
mres <- res$enrichment_result

head(mres,30) %>% kbl() %>% kable_paper("hover", full_width = F)
set setSize pANOVA s.dist p.adjustANOVA
11171 GOMF HEMOGLOBIN ALPHA BINDING 5 0.0006314 -0.8824948 0.0096044
4475 GOBP MESENCHYME MIGRATION 5 0.0011311 0.8406624 0.0152103
3609 GOBP FOREBRAIN NEURON FATE COMMITMENT 7 0.0001198 0.8394239 0.0025607
2260 GOBP ANTIGEN PROCESSING AND PRESENTATION ENDOGENOUS LIPID ANTIGEN VIA MHC CLASS IB 5 0.0017977 -0.8060841 0.0218120
11356 GOMF LIPID ANTIGEN BINDING 5 0.0017977 -0.8060841 0.0218120
11617 GOMF OLFACTORY RECEPTOR ACTIVITY 353 0.0000000 -0.7823111 0.0000000
12216 GOMF TYPE I INTERFERON RECEPTOR BINDING 14 0.0000004 -0.7818909 0.0000199
11615 GOMF ODORANT BINDING 101 0.0000000 -0.7812483 0.0000000
540 Expression and translocation of olfactory receptors 354 0.0000000 -0.7719287 0.0000000
5612 GOBP NORADRENERGIC NEURON DIFFERENTIATION 10 0.0000256 0.7685646 0.0007626
10817 GOMF CCR6 CHEMOKINE RECEPTOR BINDING 7 0.0004560 -0.7649865 0.0074237
2555 GOBP CARDIAC SEPTUM CELL DIFFERENTIATION 5 0.0031015 0.7636756 0.0328625
5511 GOBP NEURAL PLATE REGIONALIZATION 6 0.0012181 0.7624619 0.0160982
1147 Olfactory Signaling Pathway 361 0.0000000 -0.7617404 0.0000000
11258 GOMF INHIBITORY MHC CLASS I RECEPTOR ACTIVITY 9 0.0000888 -0.7543133 0.0020565
8274 GOBP REGULATION OF SKELETAL MUSCLE TISSUE REGENERATION 8 0.0002684 0.7438904 0.0049144
2875 GOBP CEREBRAL CORTEX REGIONALIZATION 7 0.0007489 0.7356821 0.0109187
8877 GOBP SENSORY PERCEPTION OF SMELL 379 0.0000000 -0.7322834 0.0000000
6699 GOBP POSITIVE REGULATION OF SKELETAL MUSCLE TISSUE REGENERATION 5 0.0049515 0.7256413 0.0471092
590 Ficolins bind to repetitive carbohydrate structures on the target cell surface 5 0.0062444 -0.7061471 0.0554551
8896 GOBP SERINE PHOSPHORYLATION OF STAT PROTEIN 20 0.0000000 -0.7055523 0.0000030
9030 GOBP SPINAL CORD OLIGODENDROCYTE CELL DIFFERENTIATION 5 0.0066247 0.7011070 0.0579121
160 Beta defensins 28 0.0000000 -0.7008617 0.0000000
5510 GOBP NEURAL PLATE PATTERN SPECIFICATION 8 0.0006486 0.6962285 0.0098291
1494 Regulation of gene expression in early pancreatic precursor cells 8 0.0007008 0.6919078 0.0104528
2134 GOBP ADENYLATE CYCLASE INHIBITING SEROTONIN RECEPTOR SIGNALING PATHWAY 7 0.0015597 0.6903690 0.0195992
428 Developmental Cell Lineages 5 0.0078873 0.6860589 0.0657684
429 Developmental Lineage of Pancreatic Acinar Cells 5 0.0078873 0.6860589 0.0657684
11199 GOMF HISTONE H3K36 TRIMETHYLTRANSFERASE ACTIVITY 5 0.0081572 0.6831248 0.0671535
10689 GOMF ARYL SULFOTRANSFERASE ACTIVITY 7 0.0018058 0.6810081 0.0218268 
updf <- head(subset(mres,p.adjustANOVA<0.05 & s.dist > 0),15)

dndf <- head(subset(mres,p.adjustANOVA<0.05 & s.dist < 0),15)

up <- updf$s.dist
names(up) <- updf$set
dn <- dndf$s.dist
names(dn) <- dndf$set
top <- c(up,dn)
top <- top[order(top)]

par(mar = c(5.1, 28.1, 4.1, 2.1))
barplot(top,horiz=TRUE,las=1,xlab="ES")

alldn <- subset(mres,p.adjustANOVA<0.05 & s.dist < 0)$set

allup <- subset(mres,p.adjustANOVA<0.05 & s.dist > 0)$set

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

Get incidence data

incid <- readRDS("../../multi_ewas_incid.rds")
incid2 <- incid[,grep("colorectal_cancer",colnames(incid) ),drop=FALSE]
head(incid2)
##          colorectal_cancer
## A1BG          0.0020387679
## A1BG-AS1      0.0029418062
## A1CF         -0.0015391277
## A2M          -0.0004250785
## A2M-AS1       0.0057066000
## A2ML1         0.0000405520
ires <- mitch_calc(incid2,genesets=gs,minsetsize=5,cores=12,priority="effect")
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
ires <- ires$enrichment_result

head(ires,30) %>% kbl(caption="incidence") %>% kable_paper("hover", full_width = F)
incidence
set setSize pANOVA s.dist p.adjustANOVA
10546 GOMF 3 KETO STEROL REDUCTASE ACTIVITY 5 0.0030439 -0.7651668 0.1618577
5502 GOBP NEURAL FOLD FORMATION 7 0.0005572 -0.7532727 0.0556352
9577 GOCC ATP BINDING CASSETTE ABC TRANSPORTER COMPLEX 6 0.0015604 0.7456328 0.1079425
7047 GOBP PROTEIN O LINKED FUCOSYLATION 5 0.0049818 0.7251341 0.2117199
9546 GOCC ALPHA BETA T CELL RECEPTOR COMPLEX 5 0.0056256 -0.7149714 0.2239424
7661 GOBP REGULATION OF GLUCOSYLCERAMIDE CATABOLIC PROCESS 5 0.0056341 0.7148441 0.2239424
10646 GOMF AMINOACYL TRNA HYDROLASE ACTIVITY 5 0.0069067 -0.6975366 0.2526645
6469 GOBP POSITIVE REGULATION OF MITOTIC SISTER CHROMATID SEGREGATION 6 0.0033116 -0.6923776 0.1687671
3619 GOBP FORMATION OF TRANSLATION PREINITIATION COMPLEX 6 0.0036689 -0.6848552 0.1802443
6374 GOBP POSITIVE REGULATION OF INTRINSIC APOPTOTIC SIGNALING PATHWAY BY P53 CLASS MEDIATOR 7 0.0021552 -0.6695584 0.1350517
9497 GOBP VOLUNTARY MUSCULOSKELETAL MOVEMENT 7 0.0022130 0.6678312 0.1360538
11172 GOMF HEXOKINASE ACTIVITY 5 0.0100326 0.6648487 0.2969173
9181 GOBP THELARCHE 5 0.0118466 -0.6498682 0.3283293
5929 GOBP PH REDUCTION 6 0.0065930 0.6404103 0.2476292
9769 GOCC CYTOPLASMIC SIDE OF ROUGH ENDOPLASMIC RETICULUM MEMBRANE 5 0.0137392 -0.6362694 0.3485038
10172 GOCC OUTER DENSE FIBER 6 0.0078073 0.6271079 0.2710223
10914 GOMF CYTOSKELETON NUCLEAR MEMBRANE ANCHOR ACTIVITY 5 0.0157290 0.6236524 0.3646299
9843 GOCC EQUATORIAL MICROTUBULE ORGANIZING CENTER 5 0.0159001 0.6226343 0.3663884
6368 GOBP POSITIVE REGULATION OF INTRACELLULAR LIPID TRANSPORT 5 0.0159554 0.6223071 0.3669752
570 FLT3 signaling through SRC family kinases 6 0.0084289 -0.6210021 0.2801000
581 FasL/ CD95L signaling 5 0.0177403 -0.6122534 0.3847555
7466 GOBP REGULATION OF CHRONIC INFLAMMATORY RESPONSE 8 0.0030838 -0.6041411 0.1618577
10781 GOMF CARBOHYDRATE PROTON SYMPORTER ACTIVITY 7 0.0058062 0.6020390 0.2292967
10020 GOCC MANNOSYLTRANSFERASE COMPLEX 5 0.0200787 -0.6003454 0.4022942
2008 Zinc efflux and compartmentalization by the SLC30 family 5 0.0214272 -0.5940187 0.4131383
3717 GOBP GLUCOSYLCERAMIDE CATABOLIC PROCESS 8 0.0036435 0.5935724 0.1797158
2260 GOBP ANTIGEN PROCESSING AND PRESENTATION ENDOGENOUS LIPID ANTIGEN VIA MHC CLASS IB 5 0.0216555 -0.5929825 0.4144367
11348 GOMF LIPID ANTIGEN BINDING 5 0.0216555 -0.5929825 0.4144367
9960 GOCC INNER DYNEIN ARM 5 0.0228792 0.5875829 0.4253280
8029 GOBP REGULATION OF OOCYTE MATURATION 7 0.0072085 0.5864416 0.2587551 
updf <- head(subset(ires,p.adjustANOVA<0.05 & s.dist > 0),15)

dndf <- head(subset(ires,p.adjustANOVA<0.05 & s.dist < 0),15)

up <- updf$s.dist
names(up) <- updf$set
dn <- dndf$s.dist
names(dn) <- dndf$set
top <- c(up,dn)
top <- top[order(top)]

par(mar = c(5.1, 28.1, 4.1, 2.1))
barplot(top,horiz=TRUE,las=1,xlab="ES")

idn <- subset(ires,p.adjustANOVA<0.05 & s.dist < 0)$set

iup <- subset(ires,p.adjustANOVA<0.05 & s.dist > 0)$set

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

Intersect sig pw

l <- list("cup"=allup,"cdn"=alldn,"idn"=idn,"iup"=iup)

plot(euler(l),quantities = TRUE)

intersect(allup,iup)
## character(0)
intersect(alldn,idn)
## [1] "Beta defensins"                                     
## [2] "Response of EIF2AK4 (GCN2) to amino acid deficiency"

Cancer specific genes

down100 <- rownames(head(y,100))
down200 <- rownames(head(y,200))
down500 <- rownames(head(y,500))
down1000 <- rownames(head(y,1000))
down2000 <- rownames(head(y,2000))
down5000 <- rownames(head(y,5000))

up100 <- rownames(tail(y,100))
up200 <- rownames(tail(y,200))
up500 <- rownames(tail(y,500))
up1000 <- rownames(tail(y,1000))
up2000 <- rownames(tail(y,2000))
up5000 <- rownames(tail(y,5000))

cancer_genes <- list("down100"=down100,"up100"=up100,
  "down200"=down200,"up200"=up200,
  "down500"=down500,"up500"=up500,
  "down1000"=down1000,"up1000"=up1000,
  "down2000"=down2000,"up2000"=up2000,
  "down5000"=down5000,"up5000"=up5000)

ocres <- mitch_calc(incid2,genesets=cancer_genes,minsetsize=5,cores=12,priority="effect")
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
ocres$enrichment_result %>% kbl(caption="cancer gene signature deteted before diagnosis") %>% kable_paper("hover", full_width = F)
cancer gene signature deteted before diagnosis
set setSize pANOVA s.dist p.adjustANOVA
1 down100 98 0.0000059 -0.2647631 0.0000118
3 down200 192 0.0000001 -0.2273312 0.0000001
2 up100 95 0.0018016 0.1852703 0.0021619
5 down500 479 0.0000000 -0.1726888 0.0000000
7 down1000 962 0.0000000 -0.1133745 0.0000000
12 up5000 4918 0.0000000 0.1095730 0.0000000
4 up200 192 0.0269012 0.0926112 0.0278072
10 up2000 1946 0.0000000 0.0752149 0.0000001
8 up1000 967 0.0011691 0.0616378 0.0015588
6 up500 476 0.0278072 0.0588582 0.0278072
9 down2000 1930 0.0005617 -0.0474581 0.0008789
11 down5000 4877 0.0005859 -0.0322139 0.0008789

Two dimensional analysis

m <- merge(y,incid2,by=0)
rownames(m) <- m$Row.names
m[,1] <- NULL
colnames(m) <- c("cancer","incidence")
head(m)
##             cancer     incidence
## A1BG      1.256876  0.0020387679
## A1BG-AS1 -1.418138  0.0029418062
## A1CF      2.954785 -0.0015391277
## A2M      -4.988589 -0.0004250785
## A2M-AS1  -2.232500  0.0057066000
## A2ML1    -3.565671  0.0000405520
m2 <- mitch_calc(m,genesets=gs,minsetsize=5,cores=12,priority="effect")
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
m2res <- m2$enrichment_result
m2res$FDR.cancer <- p.adjust(m2res$p.cancer,method="BH")
m2res$FDR.incidence <- p.adjust(m2res$p.incidence,method="BH")

m2resf <- subset(m2res,FDR.cancer < 0.05 | FDR.incidence < 0.05)
dim(m2res)
## [1] 12277    12
dim(m2resf)
## [1] 1418   12
m2resf <- subset(m2resf,p.adjustMANOVA < 0.05)
dim(m2resf)
## [1] 1161   12
head(m2resf,30) %>% kbl(caption="Top hits in 2D analysis") %>% kable_paper("hover", full_width = F)
Top hits in 2D analysis
set setSize pMANOVA s.cancer s.incidence p.cancer p.incidence s.dist SD p.adjustMANOVA FDR.cancer FDR.incidence
2260 GOBP ANTIGEN PROCESSING AND PRESENTATION ENDOGENOUS LIPID ANTIGEN VIA MHC CLASS IB 5 0.0008678 -0.8080942 -0.5935210 0.0017507 0.0215366 1.0026382 0.1517261 0.0122735 0.0215802 0.4109459
11343 GOMF LIPID ANTIGEN BINDING 5 0.0008678 -0.8080942 -0.5935210 0.0017507 0.0215366 1.0026382 0.1517261 0.0122735 0.0215802 0.4109459
3608 GOBP FOREBRAIN NEURON FATE COMMITMENT 7 0.0001105 0.8410743 -0.3375966 0.0001162 0.1219190 0.9062987 0.8334461 0.0023636 0.0025195 0.7066229
11159 GOMF HEMOGLOBIN ALPHA BINDING 5 0.0027991 -0.8849113 -0.0366747 0.0006100 0.8870671 0.8856709 0.5997938 0.0305108 0.0093968 0.9873547
4473 GOBP MESENCHYME MIGRATION 5 0.0028310 0.8423689 -0.2072638 0.0011050 0.4222073 0.8674928 0.7422024 0.0307300 0.0150909 0.8974098
160 Beta defensins 26 0.0000000 -0.6935426 -0.4157082 0.0000000 0.0002429 0.8085880 0.1964586 0.0000000 0.0000001 0.0271574
11604 GOMF OLFACTORY RECEPTOR ACTIVITY 338 0.0000000 -0.7880687 -0.0975139 0.0000000 0.0020610 0.7940788 0.4882960 0.0000000 0.0000000 0.1304279
12202 GOMF TYPE I INTERFERON RECEPTOR BINDING 14 0.0000014 -0.7836668 0.1104098 0.0000004 0.4744410 0.7914063 0.6322076 0.0000509 0.0000192 0.9092831
5505 GOBP NEURAL PLATE REGIONALIZATION 6 0.0043234 0.7644643 0.1990327 0.0011823 0.3985201 0.7899492 0.3998206 0.0424966 0.0159162 0.8946374
11602 GOMF ODORANT BINDING 99 0.0000000 -0.7802393 -0.1055901 0.0000000 0.0694382 0.7873516 0.4770490 0.0000000 0.0000000 0.6080546
540 Expression and translocation of olfactory receptors 339 0.0000000 -0.7772028 -0.0929688 0.0000000 0.0032618 0.7827435 0.4838265 0.0000000 0.0000000 0.1677343
2134 GOBP ADENYLATE CYCLASE INHIBITING SEROTONIN RECEPTOR SIGNALING PATHWAY 7 0.0025777 0.6911052 0.3501776 0.0015417 0.1086226 0.7747586 0.2410722 0.0287430 0.0195137 0.6855090
10679 GOMF ARYL SULFOTRANSFERASE ACTIVITY 7 0.0026396 0.6826831 0.3646361 0.0017593 0.0947881 0.7739610 0.2248932 0.0292641 0.0216205 0.6597018
5606 GOBP NORADRENERGIC NEURON DIFFERENTIATION 10 0.0001158 0.7696057 -0.0531124 0.0000250 0.7711842 0.7714362 0.5817496 0.0024424 0.0007481 0.9723824
1147 Olfactory Signaling Pathway 346 0.0000000 -0.7665025 -0.0833837 0.0000000 0.0076934 0.7710246 0.4830379 0.0000000 0.0000000 0.2651502
11245 GOMF INHIBITORY MHC CLASS I RECEPTOR ACTIVITY 9 0.0003971 -0.7576984 -0.1335128 0.0000826 0.4879489 0.7693715 0.4413658 0.0065084 0.0019497 0.9150066
8265 GOBP REGULATION OF SKELETAL MUSCLE TISSUE REGENERATION 8 0.0011493 0.7453454 -0.0379096 0.0002611 0.8527006 0.7463089 0.5538449 0.0153541 0.0047988 0.9821274
8868 GOBP SENSORY PERCEPTION OF SMELL 364 0.0000000 -0.7356773 -0.0943359 0.0000000 0.0019912 0.7417010 0.4534969 0.0000000 0.0000000 0.1293446
2875 GOBP CEREBRAL CORTEX REGIONALIZATION 7 0.0032663 0.7374531 0.0164727 0.0007271 0.9398398 0.7376371 0.5098101 0.0342150 0.0107165 0.9934062
3534 GOBP FAS SIGNALING PATHWAY 8 0.0010780 0.6384275 -0.3544652 0.0017645 0.0825353 0.7302296 0.7020811 0.0145761 0.0216239 0.6392363
11191 GOMF HISTONE H3K4 TRIMETHYLTRANSFERASE ACTIVITY 10 0.0006348 0.5595382 0.4616283 0.0021827 0.0114745 0.7253852 0.0692327 0.0095865 0.0253285 0.3206668
5504 GOBP NEURAL PLATE PATTERN SPECIFICATION 8 0.0023720 0.6980469 0.1793488 0.0006277 0.3797139 0.7207187 0.3667749 0.0267905 0.0096328 0.8853886
411 Defensins 32 0.0000000 -0.6417100 -0.3077475 0.0000000 0.0025848 0.7116883 0.2361471 0.0000000 0.0000000 0.1489825
1494 Regulation of gene expression in early pancreatic precursor cells 8 0.0018825 0.6928219 -0.1560874 0.0006894 0.4445746 0.7101870 0.6002696 0.0225041 0.0103980 0.9005912
8887 GOBP SERINE PHOSPHORYLATION OF STAT PROTEIN 20 0.0000003 -0.7080952 -0.0272076 0.0000000 0.8331676 0.7086177 0.4814602 0.0000129 0.0000027 0.9781253
6359 GOBP POSITIVE REGULATION OF INTERLEUKIN 23 PRODUCTION 7 0.0042604 -0.6541573 0.2545745 0.0027231 0.2434640 0.7019472 0.6425704 0.0419786 0.0300102 0.8163502
1192 POU5F1 (OCT4), SOX2, NANOG activate genes related to proliferation 10 0.0014855 0.6494524 -0.0637550 0.0003755 0.7270131 0.6525742 0.5043138 0.0189185 0.0063859 0.9664638
8864 GOBP SENSORY PERCEPTION OF CHEMICAL STIMULUS 439 0.0000000 -0.6407528 -0.0622601 0.0000000 0.0253052 0.6437705 0.4090561 0.0000000 0.0000000 0.4429419
11213 GOMF HMG BOX DOMAIN BINDING 13 0.0002294 0.6091455 -0.1972530 0.0001427 0.2181620 0.6402867 0.5702098 0.0042217 0.0029599 0.7969063
11751 GOMF PHOSPHATIDYLINOSITOL TRANSFER ACTIVITY 10 0.0029493 0.6027382 0.2026652 0.0009641 0.2671102 0.6358982 0.2828943 0.0317624 0.0134322 0.8333688 
mx <- as.matrix(m2resf[1:30,4:5])

rownames(mx) <- head(m2resf,30)$set
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(mx,trace="none",scale="none",mar=c(5,25),cexCol="0.9",col=colfunc)

mitch_report(res=m2,outfile="2dmitch.html",overwrite=TRUE)
## Dataset saved as " /tmp/RtmpUUu8R3/2dmitch.rds ".
## 
## 
## processing file: mitch.Rmd
## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]
## 9/34                             
## 10/34 [contourplot]
## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]
## 15/34                             
## 16/34 [input_geneset_metrics3]
## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]
## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]
## 30/34                             
## 31/34 [detailed_geneset_reports2d]
## 32/34                             
## 33/34 [session_info]              
## 34/34
## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/cancer_biomarkers/colorectal/GSE199057/mitch.knit.md
## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/cancer_biomarkers/colorectal/GSE199057/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpUUu8R3/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpUUu8R3/rmarkdown-str3241b0546b83e5.html
## 
## Output created: /tmp/RtmpUUu8R3/mitch_report.html
## [1] TRUE

A few top genes

mrank <- m2$ranked_profile

mrank <- as.data.frame(cbind(mrank,rowMedians(mrank)))

mrank <- mrank[order(mrank$V3),]

head(mrank,50) %>% kbl(caption="common hypomethylated genes") %>% kable_paper("hover", full_width = F)
common hypomethylated genes
cancer incidence V3
MIR3616 -10022 -10781 -10401.5
MIR622 -10028 -10771 -10399.5
IFNA8 -10003 -10773 -10388.0
SPART-AS1 -10024 -10711 -10367.5
MIR873 -9969 -10739 -10354.0
OR10T2 -9998 -10706 -10352.0
LINC01289 -9940 -10763 -10351.5
CCT8L2 -9872 -10783 -10327.5
MIR4535 -10008 -10636 -10322.0
MIR218-2 -9932 -10698 -10315.0
OR5A2 -9958 -10661 -10309.5
LINC01524 -9949 -10644 -10296.5
OR2M7 -10009 -10563 -10286.0
OR4A47 -9954 -10617 -10285.5
LINC00326 -9864 -10686 -10275.0
CDRT15P3 -10029 -10513 -10271.0
MIR7515 -10017 -10508 -10262.5
ANKRD62P1-PARP4P3 -9796 -10718 -10257.0
LINC01142 -9986 -10527 -10256.5
LINC01493 -10010 -10503 -10256.5
MIR588 -9716 -10779 -10247.5
FAM133CP -10030 -10442 -10236.0
GABRG3-AS1 -9991 -10474 -10232.5
CD1E -9824 -10624 -10224.0
LINC00701 -9693 -10742 -10217.5
MIR548A3 -9957 -10477 -10217.0
TRIM49D2 -9741 -10687 -10214.0
MIR6075 -9667 -10756 -10211.5
CHRFAM7A -9734 -10681 -10207.5
LINC01467 -9845 -10570 -10207.5
MIR5571 -9643 -10766 -10204.5
UBQLNL -9774 -10623 -10198.5
IFNA16 -10014 -10376 -10195.0
OR10AG1 -9802 -10579 -10190.5
LCE1F -10023 -10342 -10182.5
MIR4643 -9644 -10719 -10181.5
DMBT1L1 -9886 -10445 -10165.5
LCE2D -9903 -10426 -10164.5
OR2M1P -9788 -10541 -10164.5
BLID -9878 -10443 -10160.5
LINC01050 -9680 -10635 -10157.5
MIR520F -9679 -10622 -10150.5
APCS -9590 -10699 -10144.5
KRTAP21-2 -9922 -10364 -10143.0
DEFB118 -9973 -10312 -10142.5
LINC01241 -9982 -10297 -10139.5
PPP3R2 -9767 -10509 -10138.0
SNORD115-39 -9671 -10602 -10136.5
SLC9A9-AS1 -9935 -10330 -10132.5
MIR5584 -9510 -10749 -10129.5 
tail(mrank,50) %>% kbl(caption="common hypermethylated genes") %>% kable_paper("hover", full_width = F)
common hypermethylated genes
cancer incidence V3
EPN2-IT1 11054 11100 11077.0
MIR3115 11559 10622 11090.5
MOB2 11640 10543 11091.5
UBE2Q1-AS1 11451 10746 11098.5
SNORA71A 11388 10818 11103.0
MIR629 11329 10895 11112.0
IL11RA 11521 10704 11112.5
MIR602 11594 10639 11116.5
QRFP 11319 10925 11122.0
CIMIP2B 11658 10601 11129.5
MIR1827 11173 11091 11132.0
MIR6784 11235 11030 11132.5
MIR106B 11526 10753 11139.5
SNORA74B 11690 10605 11147.5
RNF216-IT1 11491 10813 11152.0
SNORD114-30 11360 10958 11159.0
ZSWIM8-AS1 11489 10832 11160.5
MIR6851 11281 11066 11173.5
MIR4691 11351 10997 11174.0
AGER 11731 10638 11184.5
MIR6807 11586 10786 11186.0
MIR8089 11404 10976 11190.0
MIR765 11590 10794 11192.0
MIR6509 11333 11067 11200.0
ZBED10P 11774 10648 11211.0
LINC01504 11635 10801 11218.0
NPTN-IT1 11601 10879 11240.0
MUSTN1 11436 11064 11250.0
MIR6882 11768 10734 11251.0
MIR6856 11571 10944 11257.5
MIR6720 11871 10662 11266.5
SNORD7 11461 11081 11271.0
UCN 11733 10809 11271.0
MIR6775 11471 11115 11293.0
MIR199A2 11843 10752 11297.5
LGALS7 11524 11074 11299.0
MIR326 11878 10732 11305.0
MIR197 11751 10860 11305.5
MIR4647 11510 11134 11322.0
MIR5692B 11868 10778 11323.0
MIR6751 11778 10920 11349.0
MIR8085 11851 10871 11361.0
MIR4304 11633 11110 11371.5
MIR6823 11854 10896 11375.0
MIR4718 11624 11129 11376.5
MIR28 11829 10956 11392.5
MIR4505 11738 11072 11405.0
MIR6736 11703 11116 11409.5
LCAT 11883 10943 11413.0
MIR1271 11863 11048 11455.5

Run mitch (healthy control v normal (CRC diagnosed))

dm <- readRDS("GSE199057_2_limma.rds")

y <- mitch_import(dm,DEtype="limma",geneTable=gt1)
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 776521
## Note: no. genes in output = 22227
## Warning in mitch_import(dm, DEtype = "limma", geneTable = gt1): Warning: less than half of the input genes are also in the
##         output
head(y)
##                   x
## A1BG     -0.1624750
## A1BG-AS1 -0.3989024
## A1CF      0.9616946
## A2M      -0.7927841
## A2M-AS1  -0.6770865
## A2ML1     1.4917204
y <- y[order(y$x),,drop=FALSE]

head(y)
##                   x
## MIR199A2  -9.514949
## MIR3619   -8.915660
## LINC00942 -8.816906
## MAB21L2   -8.624217
## MIR8085   -8.390826
## MIR5006   -8.389382
tail(y)
##                  x
## MIR548AQ  8.499405
## ZNF676    8.553380
## MIR4531   9.020449
## MIR550B1  9.596144
## MIR548AL  9.652990
## UNC5B-AS1 9.738375
res <- mitch_calc(y,gs,priority="effect",cores=12,minsetsize=5)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
mres <- res$enrichment_result

head(mres,30) %>% kbl() %>% kable_paper("hover", full_width = F)
set setSize pANOVA s.dist p.adjustANOVA
1852 Tachykinin receptors bind tachykinins 5 0.0009366 -0.8543965 0.0411617
4229 GOBP LOBAR BRONCHUS EPITHELIUM DEVELOPMENT 5 0.0014257 0.8235442 0.0540350
9148 GOBP TACHYKININ RECEPTOR SIGNALING PATHWAY 8 0.0000643 -0.8157883 0.0059908
2281 GOBP AORTA SMOOTH MUSCLE TISSUE MORPHOGENESIS 6 0.0005679 -0.8123997 0.0293256
7494 GOBP REGULATION OF CORTICOTROPIN SECRETION 5 0.0016606 -0.8120961 0.0588183
11080 GOMF GALACTOSIDE BINDING 5 0.0017026 0.8102061 0.0595628
10817 GOMF CCR6 CHEMOKINE RECEPTOR BINDING 7 0.0003397 0.7819210 0.0195152
4228 GOBP LOBAR BRONCHUS DEVELOPMENT 6 0.0009886 0.7763977 0.0427852
7660 GOBP REGULATION OF GLUCOCORTICOID RECEPTOR SIGNALING PATHWAY 7 0.0008842 -0.7256397 0.0398075
4475 GOBP MESENCHYME MIGRATION 5 0.0055922 -0.7154712 0.1236969
9186 GOBP THELARCHE 5 0.0058851 0.7111691 0.1277978
11609 GOMF N ACETYLLACTOSAMINIDE BETA 1 3 N ACETYLGLUCOSAMINYLTRANSFERASE ACTIVITY 6 0.0025940 0.7100341 0.0792276
6277 GOBP POSITIVE REGULATION OF FEAR RESPONSE 7 0.0011895 -0.7073936 0.0484930
4995 GOBP NEGATIVE REGULATION OF EPINEPHRINE SECRETION 5 0.0065346 -0.7022770 0.1343094
3019 GOBP CORTISOL SECRETION 5 0.0065636 -0.7018990 0.1346800
2905 GOBP CHONDROBLAST DIFFERENTIATION 5 0.0069105 -0.6974890 0.1387849
11171 GOMF HEMOGLOBIN ALPHA BINDING 5 0.0071338 0.6947529 0.1423473
3406 GOBP EPINEPHRINE SECRETION 7 0.0014907 -0.6932365 0.0548490
9127 GOBP SYNAPTIC TRANSMISSION GLYCINERGIC 5 0.0086810 -0.6776708 0.1562197
10548 GOMF 3 CHLOROALLYL ALDEHYDE DEHYDROGENASE ACTIVITY 5 0.0114118 0.6532625 0.1806855
5692 GOBP N ACYLETHANOLAMINE METABOLIC PROCESS 5 0.0120197 0.6485465 0.1858298
6148 GOBP POSITIVE REGULATION OF CEREBELLAR GRANULE CELL PRECURSOR PROLIFERATION 6 0.0059761 -0.6480356 0.1290899
2555 GOBP CARDIAC SEPTUM CELL DIFFERENTIATION 5 0.0120888 -0.6480245 0.1863091
10490 GOCC TROPONIN COMPLEX 9 0.0008734 0.6406417 0.0394660
6103 GOBP POSITIVE REGULATION OF CD8 POSITIVE ALPHA BETA T CELL ACTIVATION 8 0.0017721 0.6381700 0.0605841
12216 GOMF TYPE I INTERFERON RECEPTOR BINDING 14 0.0000356 0.6380562 0.0037719
5367 GOBP NEGATIVE REGULATION OF SERINE TYPE PEPTIDASE ACTIVITY 5 0.0136803 0.6366664 0.2006172
9520 GOBP XENOBIOTIC GLUCURONIDATION 7 0.0036313 0.6347692 0.0978776
10113 GOCC NEUROFIBRILLARY TANGLE 5 0.0142661 -0.6327783 0.2042418
6889 GOBP PROGESTERONE BIOSYNTHETIC PROCESS 6 0.0073085 0.6323298 0.1442457 
updf <- head(subset(mres,p.adjustANOVA<0.05 & s.dist > 0),15)

dndf <- head(subset(mres,p.adjustANOVA<0.05 & s.dist < 0),15)

up <- updf$s.dist
names(up) <- updf$set
dn <- dndf$s.dist
names(dn) <- dndf$set
top <- c(up,dn)
top <- top[order(top)]

par(mar = c(5.1, 28.1, 4.1, 2.1))
barplot(top,horiz=TRUE,las=1,xlab="ES")

alldn <- subset(mres,p.adjustANOVA<0.05 & s.dist < 0)$set

allup <- subset(mres,p.adjustANOVA<0.05 & s.dist > 0)$set

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

Intersect sig pw

l <- list("cup"=allup,"cdn"=alldn,"idn"=idn,"iup"=iup)

plot(euler(l),quantities = TRUE)

intersect(allup,iup)
## character(0)
intersect(alldn,idn)
## [1] "GOCC CHROMOSOMAL REGION"                
## [2] "GOCC NUCLEAR PROTEIN CONTAINING COMPLEX"
## [3] "GOCC CATALYTIC COMPLEX"

Cancer specific genes

down100 <- rownames(head(y,100))
down200 <- rownames(head(y,200))
down500 <- rownames(head(y,500))
down1000 <- rownames(head(y,1000))
down2000 <- rownames(head(y,2000))
down5000 <- rownames(head(y,5000))

up100 <- rownames(tail(y,100))
up200 <- rownames(tail(y,200))
up500 <- rownames(tail(y,500))
up1000 <- rownames(tail(y,1000))
up2000 <- rownames(tail(y,2000))
up5000 <- rownames(tail(y,5000))

cancer_genes <- list("down100"=down100,"up100"=up100,
  "down200"=down200,"up200"=up200,
  "down500"=down500,"up500"=up500,
  "down1000"=down1000,"up1000"=up1000,
  "down2000"=down2000,"up2000"=up2000,
  "down5000"=down5000,"up5000"=up5000)

ocres <- mitch_calc(incid2,genesets=cancer_genes,minsetsize=5,cores=12,priority="effect")
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
ocres$enrichment_result %>% kbl(caption="cancer gene signature deteted before diagnosis") %>% kable_paper("hover", full_width = F)
cancer gene signature deteted before diagnosis
set setSize pANOVA s.dist p.adjustANOVA
2 up100 94 0.0000006 -0.2974194 0.0000012
4 up200 190 0.0000025 -0.1980727 0.0000043
7 down1000 969 0.0000000 0.1700285 0.0000000
3 down200 186 0.0000726 0.1686555 0.0000968
1 down100 92 0.0054919 0.1674811 0.0059912
5 down500 475 0.0000000 0.1591991 0.0000000
9 down2000 1951 0.0000000 0.1556511 0.0000000
11 down5000 4910 0.0000000 0.1496950 0.0000000
12 up5000 4908 0.0000000 -0.0703598 0.0000000
6 up500 486 0.0141215 -0.0649932 0.0141215
8 up1000 976 0.0009764 -0.0623285 0.0011717
10 up2000 1957 0.0000220 -0.0580115 0.0000330

Two dimensional analysis

m <- merge(y,incid2,by=0)
rownames(m) <- m$Row.names
m[,1] <- NULL
colnames(m) <- c("cancer","incidence")
head(m)
##              cancer     incidence
## A1BG     -0.1624750  0.0020387679
## A1BG-AS1 -0.3989024  0.0029418062
## A1CF      0.9616946 -0.0015391277
## A2M      -0.7927841 -0.0004250785
## A2M-AS1  -0.6770865  0.0057066000
## A2ML1     1.4917204  0.0000405520
m2 <- mitch_calc(m,genesets=gs,minsetsize=5,cores=12,priority="effect")
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
m2res <- m2$enrichment_result
m2res$FDR.cancer <- p.adjust(m2res$p.cancer,method="BH")
m2res$FDR.incidence <- p.adjust(m2res$p.incidence,method="BH")

m2resf <- subset(m2res,FDR.cancer < 0.05 | FDR.incidence < 0.05)
dim(m2res)
## [1] 12277    12
dim(m2resf)
## [1] 415  12
m2resf <- subset(m2resf,p.adjustMANOVA < 0.05)
dim(m2resf)
## [1] 362  12
head(m2resf,30) %>% kbl(caption="Top hits in 2D analysis") %>% kable_paper("hover", full_width = F)
Top hits in 2D analysis
set setSize pMANOVA s.cancer s.incidence p.cancer p.incidence s.dist SD p.adjustMANOVA FDR.cancer FDR.incidence
2281 GOBP AORTA SMOOTH MUSCLE TISSUE MORPHOGENESIS 6 0.0013838 -0.8141845 -0.1643852 0.0005521 0.4856175 0.8306135 0.4594774 0.0414377 0.0284820 0.9141557
9139 GOBP TACHYKININ RECEPTOR SIGNALING PATHWAY 8 0.0003243 -0.8173314 0.1352104 0.0000623 0.5078190 0.8284398 0.6735487 0.0139254 0.0059307 0.9200781
10480 GOCC TROPONIN COMPLEX 9 0.0000307 0.6420694 0.5181957 0.0008504 0.0070995 0.8250939 0.0875919 0.0021324 0.0391036 0.2557285
11191 GOMF HISTONE H3K4 TRIMETHYLTRANSFERASE ACTIVITY 10 0.0003623 -0.6118383 0.4616283 0.0008062 0.0114745 0.7664508 0.7590555 0.0151291 0.0377786 0.3206668
3406 GOBP EPINEPHRINE TRANSPORT 11 0.0002087 -0.6111708 -0.2999946 0.0004476 0.0849129 0.6808278 0.2200348 0.0102470 0.0245216 0.6410204
1553 SARS-CoV-1 modulates host translation machinery 33 0.0000000 0.4164995 -0.5362220 0.0000345 0.0000001 0.6789741 0.6736758 0.0000003 0.0036507 0.0000258
613 Formation of the anterior neural plate 10 0.0009364 -0.6247809 -0.1979007 0.0006227 0.2785190 0.6553746 0.3018499 0.0309032 0.0314584 0.8363468
12202 GOMF TYPE I INTERFERON RECEPTOR BINDING 14 0.0000901 0.6393750 0.1104098 0.0000343 0.4744410 0.6488380 0.3740349 0.0054198 0.0036507 0.9092831
9773 GOCC CYTOSOLIC SMALL RIBOSOMAL SUBUNIT 38 0.0000000 0.3545104 -0.5071718 0.0001554 0.0000001 0.6187898 0.6093013 0.0000006 0.0114746 0.0000174
8887 GOBP SERINE PHOSPHORYLATION OF STAT PROTEIN 20 0.0000122 0.6125377 -0.0272076 0.0000021 0.8331676 0.6131416 0.4523682 0.0009818 0.0004447 0.9781253
1749 Specification of the neural plate border 16 0.0000672 -0.5890966 -0.1604013 0.0000450 0.2666376 0.6105435 0.3031334 0.0041640 0.0045236 0.8333688
2522 GOBP CARBON DIOXIDE TRANSPORT 15 0.0005142 0.5802803 -0.0706350 0.0000995 0.6357525 0.5845635 0.4602666 0.0195454 0.0082555 0.9462818
1889 Trafficking of GluR2-containing AMPA receptors 15 0.0010699 -0.4908233 0.3074786 0.0009961 0.0392167 0.5791810 0.5644847 0.0345671 0.0426981 0.5186298
1985 Viral mRNA Translation 83 0.0000000 0.2765060 -0.4916169 0.0000133 0.0000000 0.5640414 0.5431449 0.0000000 0.0018008 0.0000000
1212 Peptide chain elongation 83 0.0000000 0.2764850 -0.4892403 0.0000133 0.0000000 0.5619609 0.5414495 0.0000000 0.0018008 0.0000000
537 Eukaryotic Translation Termination 86 0.0000000 0.2908287 -0.4760722 0.0000031 0.0000000 0.5578763 0.5422808 0.0000000 0.0006167 0.0000000
12069 GOMF STRUCTURAL CONSTITUENT OF SKIN EPIDERMIS 36 0.0000000 0.4415354 0.3291830 0.0000045 0.0006299 0.5507404 0.0794451 0.0000018 0.0008293 0.0589553
389 Defective GALNT3 causes HFTC 18 0.0002455 0.5389072 0.0684299 0.0000753 0.6152305 0.5432344 0.3326776 0.0114605 0.0067447 0.9434817
535 Eukaryotic Translation Elongation 87 0.0000000 0.2634266 -0.4736059 0.0000216 0.0000000 0.5419373 0.5211606 0.0000000 0.0025713 0.0000000
1559 SARS-CoV-2 modulates host translation machinery 45 0.0000000 0.3147740 -0.4364706 0.0002583 0.0000004 0.5381350 0.5312101 0.0000039 0.0164821 0.0000901
619 Formation of the ternary complex, and subsequently, the 43S complex 45 0.0000000 0.2389816 -0.4742749 0.0055380 0.0000000 0.5310827 0.5043485 0.0000044 0.1230485 0.0000110
1613 Selenocysteine synthesis 86 0.0000000 0.2599420 -0.4581841 0.0000308 0.0000000 0.5267850 0.5077918 0.0000000 0.0034659 0.0000000
601 Formation of a pool of free 40S subunits 93 0.0000000 0.2185428 -0.4741562 0.0002695 0.0000000 0.5220967 0.4898121 0.0000000 0.0167954 0.0000000
1121 Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC) 88 0.0000000 0.2687791 -0.4392909 0.0000131 0.0000000 0.5149939 0.5006811 0.0000000 0.0018008 0.0000000
7382 GOBP REGULATION OF CD8 POSITIVE ALPHA BETA T CELL ACTIVATION 20 0.0006601 0.4697425 -0.2078714 0.0002755 0.1075319 0.5136814 0.4791454 0.0234883 0.0170837 0.6808506
9769 GOCC CYTOSOLIC LARGE RIBOSOMAL SUBUNIT 51 0.0000000 0.1824002 -0.4788838 0.0242250 0.0000000 0.5124446 0.4675984 0.0000013 0.2638957 0.0000011
2473 GOBP C21 STEROID HORMONE BIOSYNTHETIC PROCESS 20 0.0002081 0.4758241 0.1798146 0.0002292 0.1638758 0.5086668 0.2093103 0.0102470 0.0150505 0.7556604
1531 Response of EIF2AK4 (GCN2) to amino acid deficiency 94 0.0000000 0.2483127 -0.4416287 0.0000316 0.0000000 0.5066509 0.4878623 0.0000000 0.0035061 0.0000000
878 L13a-mediated translational silencing of Ceruloplasmin expression 102 0.0000000 0.2139250 -0.4582322 0.0001886 0.0000000 0.5057081 0.4752869 0.0000000 0.0132307 0.0000000
669 GTP hydrolysis and joining of the 60S ribosomal subunit 103 0.0000000 0.2127330 -0.4587180 0.0001907 0.0000000 0.5056456 0.4747875 0.0000000 0.0132992 0.0000000 
mx <- as.matrix(m2resf[1:30,4:5])

rownames(mx) <- head(m2resf,30)$set
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(mx,trace="none",scale="none",mar=c(5,25),cexCol="0.9",col=colfunc)

mitch_report(res=m2,outfile="2dmitch2.html",overwrite=TRUE)
## Dataset saved as " /tmp/RtmpUUu8R3/2dmitch2.rds ".
## 
## 
## processing file: mitch.Rmd
## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]
## 9/34                             
## 10/34 [contourplot]
## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]
## 15/34                             
## 16/34 [input_geneset_metrics3]
## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]
## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]
## 30/34                             
## 31/34 [detailed_geneset_reports2d]
## 32/34                             
## 33/34 [session_info]              
## 34/34
## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/cancer_biomarkers/colorectal/GSE199057/mitch.knit.md
## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/cancer_biomarkers/colorectal/GSE199057/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpUUu8R3/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpUUu8R3/rmarkdown-str3241b042acef56.html
## 
## Output created: /tmp/RtmpUUu8R3/mitch_report.html
## [1] TRUE

Session information

sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 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] pkgload_1.4.0                                      
##  [2] GGally_2.2.1                                       
##  [3] ggplot2_3.5.1                                      
##  [4] reshape2_1.4.4                                     
##  [5] gtools_3.9.5                                       
##  [6] tibble_3.2.1                                       
##  [7] dplyr_1.1.4                                        
##  [8] echarts4r_0.4.5                                    
##  [9] gplots_3.2.0                                       
## [10] eulerr_7.0.2                                       
## [11] RhpcBLASctl_0.23-42                                
## [12] beeswarm_0.4.0                                     
## [13] vioplot_0.5.0                                      
## [14] zoo_1.8-12                                         
## [15] sm_2.2-6.0                                         
## [16] kableExtra_1.4.0                                   
## [17] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [18] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1 
## [19] minfi_1.50.0                                       
## [20] bumphunter_1.46.0                                  
## [21] locfit_1.5-9.10                                    
## [22] iterators_1.0.14                                   
## [23] foreach_1.5.2                                      
## [24] Biostrings_2.72.1                                  
## [25] XVector_0.44.0                                     
## [26] SummarizedExperiment_1.34.0                        
## [27] Biobase_2.64.0                                     
## [28] MatrixGenerics_1.16.0                              
## [29] matrixStats_1.4.1                                  
## [30] GenomicRanges_1.56.2                               
## [31] GenomeInfoDb_1.40.1                                
## [32] IRanges_2.38.1                                     
## [33] S4Vectors_0.42.1                                   
## [34] BiocGenerics_0.50.0                                
## [35] HGNChelper_0.8.15                                  
## [36] mitch_1.19.3                                       
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.4.1             later_1.4.0              
##   [3] BiocIO_1.14.0             bitops_1.0-9             
##   [5] polyclip_1.10-7           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_2.0.2              scrime_1.3.5             
##  [13] magrittr_2.0.3            sass_0.4.9               
##  [15] limma_3.60.6              rmarkdown_2.29           
##  [17] jquerylib_0.1.4           yaml_2.3.10              
##  [19] httpuv_1.6.15             doRNG_1.8.6              
##  [21] askpass_1.2.1             DBI_1.2.3                
##  [23] RColorBrewer_1.1-3        abind_1.4-8              
##  [25] zlibbioc_1.50.0           quadprog_1.5-8           
##  [27] purrr_1.0.2               RCurl_1.98-1.16          
##  [29] GenomeInfoDbData_1.2.12   genefilter_1.86.0        
##  [31] annotate_1.82.0           svglite_2.1.3            
##  [33] DelayedMatrixStats_1.26.0 codetools_0.2-20         
##  [35] DelayedArray_0.30.1       xml2_1.3.6               
##  [37] tidyselect_1.2.1          farver_2.1.2             
##  [39] UCSC.utils_1.0.0          beanplot_1.3.1           
##  [41] illuminaio_0.46.0         GenomicAlignments_1.40.0 
##  [43] jsonlite_1.8.9            multtest_2.60.0          
##  [45] survival_3.7-0            systemfonts_1.1.0        
##  [47] polylabelr_0.3.0          tools_4.4.1              
##  [49] Rcpp_1.0.13-1             glue_1.8.0               
##  [51] gridExtra_2.3             SparseArray_1.4.8        
##  [53] xfun_0.49                 HDF5Array_1.32.1         
##  [55] withr_3.0.2               fastmap_1.2.0            
##  [57] rhdf5filters_1.16.0       fansi_1.0.6              
##  [59] openssl_2.2.2             caTools_1.18.3           
##  [61] digest_0.6.37             R6_2.5.1                 
##  [63] mime_0.12                 colorspace_2.1-1         
##  [65] RSQLite_2.3.8             utf8_1.2.4               
##  [67] tidyr_1.3.1               generics_0.1.3           
##  [69] data.table_1.16.2         rtracklayer_1.64.0       
##  [71] httr_1.4.7                htmlwidgets_1.6.4        
##  [73] S4Arrays_1.4.1            ggstats_0.7.0            
##  [75] pkgconfig_2.0.3           gtable_0.3.6             
##  [77] blob_1.2.4                siggenes_1.78.0          
##  [79] htmltools_0.5.8.1         scales_1.3.0             
##  [81] png_0.1-8                 knitr_1.49               
##  [83] rstudioapi_0.17.1         tzdb_0.4.0               
##  [85] rjson_0.2.23              nlme_3.1-166             
##  [87] curl_6.0.1                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_1.3.1            xtable_1.8-4             
## [101] evaluate_1.0.1            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            labeling_0.4.3           
## [111] nor1mix_1.3-3             mclust_6.1.1             
## [113] plyr_1.8.9                stringi_1.8.4            
## [115] viridisLite_0.4.2         BiocParallel_1.38.0      
## [117] munsell_0.5.1             Matrix_1.7-1             
## [119] hms_1.1.3                 sparseMatrixStats_1.16.0 
## [121] bit64_4.5.2               Rhdf5lib_1.26.0          
## [123] KEGGREST_1.44.1           statmod_1.5.0            
## [125] shiny_1.9.1               memoise_2.0.1.9000       
## [127] bslib_0.8.0               bit_4.5.0                
## [129] splitstackshape_1.4.8