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

Introduction

Here I will be running a comparison of differential methylation data from guthrie and fresh blood samples.

Here are the files that I’m using:

  • limma_blood_ADOS.csv

  • limma_guthrie_ADOS.csv

  • limma_buccal_ADOS.csv

suppressPackageStartupMessages({
  library("limma")
  library("parallel")
  library("mitch")
  library("IlluminaHumanMethylationEPICanno.ilm10b4.hg19")
  source("meth_functions.R")
  library("data.table")
  library("kableExtra")
  library("eulerr")
  library("GenomicRanges")
  library("HGNChelper")
  library("gplots")

  #data("dualmap850kEID")
})

#source("meth_functions.R")

Probe sets

Make a table that matches probes to genes, keeping in mind that old gene names need to be updated.

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$probe)
dim(gt)
## [1] 684970      2
#new.hgnc.table <- getCurrentHumanMap()
new.hgnc.table <- readRDS("new.hgnc.table.rds")
fix <- checkGeneSymbols(gt$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 symbols
fix2 <- fix[which(fix$x != fix$Suggested.Symbol),]
gt$gene <- fix$Suggested.Symbol
head(gt)
##     gene      probe
## 1 YTHDF1 cg18478105
## 2 EIF2S3 cg09835024
## 3   PKN3 cg14361672
## 4 CCDC57 cg01763666
## 5   INF2 cg12950382
## 6  CDC16 cg02115394
length(unique(fix2$x))
## [1] 3254
length(unique(gt$gene))
## [1] 23678
length(unique(fix2$x)) / length(unique(gt$gene))
## [1] 0.1374271

Load gene sets

Reactome version Sept 2023.

msigdb <- gmt_import("msigdb.v2023.2.Hs.symbols.gmt")

gobp <- msigdb[grep("^GOBP_",names(msigdb))]
names(gobp) <- gsub("GOBP_","",names(gobp))
names(gobp) <- gsub("_"," ",names(gobp))

reactome <- gmt_import("ReactomePathways.gmt")

Load limma data

gu_ados <- read.csv("limma_guthrie_ADOS.csv",row.names=1)
gu_diag <- read.csv("limma_guthrie_diagnosis.csv",row.names=1)
gu_iiq <- read.csv("limma_guthrie_iIQ.csv",row.names=1)
gu_ilan <- read.csv("limma_guthrie_ilanguage.csv",row.names=1)
gu_mot <- read.csv("limma_guthrie_motor.csv",row.names=1)

bl_ados <- read.csv("limma_blood_ADOS.csv",row.names=1)
bl_diag <- read.csv("limma_blood_diagnosis.csv",row.names=1)
bl_iiq <- read.csv("limma_blood_iIQ.csv",row.names=1)
bl_ilan <- read.csv("limma_blood_ilanguage.csv",row.names=1)
bl_mot <- read.csv("limma_blood_motor.csv",row.names=1)

buc_ados <- read.csv("limma_buccal_ADOS.csv",row.names=1)
buc_diag <- read.csv("limma_buccal_diagnosis.csv",row.names=1)
buc_iiq <- read.csv("limma_buccal_iIQ.csv",row.names=1)
buc_ilan <- read.csv("limma_buccal_ilanguage.csv",row.names=1)
buc_mot <- read.csv("limma_buccal_motor.csv",row.names=1)

Mitch import

m_gu_ados <- mitch_import(x=gu_ados, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 790658
## Note: no. genes in output = 22269
## Warning in mitch_import(x = gu_ados, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_gu_diag <- mitch_import(x=gu_diag, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 790658
## Note: no. genes in output = 22269
## Warning in mitch_import(x = gu_diag, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_gu_iiq <- mitch_import(x=gu_iiq, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 790658
## Note: no. genes in output = 22269
## Warning in mitch_import(x = gu_iiq, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_gu_ilan <- mitch_import(x=gu_ilan, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 790658
## Note: no. genes in output = 22269
## Warning in mitch_import(x = gu_ilan, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_gu_mot <- mitch_import(x=gu_mot, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 790658
## Note: no. genes in output = 22269
## Warning in mitch_import(x = gu_mot, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_bl_ados <- mitch_import(x=bl_ados, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 802647
## Note: no. genes in output = 22285
## Warning in mitch_import(x = bl_ados, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_bl_diag <- mitch_import(x=bl_diag, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 802647
## Note: no. genes in output = 22285
## Warning in mitch_import(x = bl_diag, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_bl_iiq <- mitch_import(x=bl_iiq, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 802647
## Note: no. genes in output = 22285
## Warning in mitch_import(x = bl_iiq, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_bl_ilan <- mitch_import(x=bl_ilan, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 802647
## Note: no. genes in output = 22285
## Warning in mitch_import(x = bl_ilan, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_bl_mot <- mitch_import(x=bl_mot, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 802647
## Note: no. genes in output = 22285
## Warning in mitch_import(x = bl_mot, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_buc_ados <- mitch_import(x=buc_ados, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 801260
## Note: no. genes in output = 22286
## Warning in mitch_import(x = buc_ados, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_buc_diag <- mitch_import(x=buc_diag, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 801260
## Note: no. genes in output = 22286
## Warning in mitch_import(x = buc_diag, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_buc_iiq <- mitch_import(x=buc_iiq, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 801260
## Note: no. genes in output = 22286
## Warning in mitch_import(x = buc_iiq, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_buc_ilan <- mitch_import(x=buc_ilan, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 801260
## Note: no. genes in output = 22286
## Warning in mitch_import(x = buc_ilan, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
m_buc_mot <- mitch_import(x=buc_mot, DEtype="limma", geneTable=gt )
## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.
## Note: Mean no. genes in input = 801260
## Note: no. genes in output = 22286
## Warning in mitch_import(x = buc_mot, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output

Histograms

hist(gu_ados$t,xlab="probe t",main="Guthrie ADOS",breaks=40)

hist(gu_diag$t,xlab="probe t",main="Guthrie diagnosis",breaks=40)

hist(gu_iiq$t,xlab="probe t",main="Guthrie inverse IQ",breaks=40)

hist(gu_ilan$t,xlab="probe t",main="Guthrie inverse language",breaks=40)

hist(gu_mot$t,xlab="probe t",main="Guthrie motor skill",breaks=40)

hist(bl_ados$t,xlab="probe t",main="Blood at assessment ADOS",breaks=40)

hist(bl_diag$t,xlab="probe t",main="Blood at assessment diagnosis",breaks=40)

hist(bl_iiq$t,xlab="probe t",main="Blood at assessment inverse IQ",breaks=40)

hist(bl_ilan$t,xlab="probe t",main="Blood at assessment language",breaks=40)

hist(bl_mot$t,xlab="probe t",main="Blood at assessment motor skill",breaks=40)

hist(buc_ados$t,xlab="probe t",main="Buccal ADOS",breaks=40)

hist(buc_diag$t,xlab="probe t",main="Buccal diagnosis",breaks=40)

hist(buc_iiq$t,xlab="probe t",main="Buccal inverse IQ",breaks=40)

hist(buc_ilan$t,xlab="probe t",main="Buccal language",breaks=40)

hist(buc_mot$t,xlab="probe t",main="Buccal motor skill",breaks=40)

hist(m_gu_ados[,1],breaks=40,xlab="t (gene level)",main="Guthrie ADOS")

hist(m_gu_diag[,1],breaks=40,xlab="t (gene level)",main="Guthrie diagnosis")

hist(m_gu_iiq[,1],breaks=40,xlab="t (gene level)",main="Guthrie inverse IQ")

hist(m_gu_ilan[,1],breaks=40,xlab="t (gene level)",main="Guthrie inverse language")

hist(m_gu_mot[,1],breaks=40,xlab="t (gene level)",main="Guthrie motor skills")

hist(m_bl_ados[,1],breaks=40,xlab="t (gene level)",main="Blood at assessment ADOS")

hist(m_bl_diag[,1],breaks=40,xlab="t (gene level)",main="Blood at assessment diagnosis")

hist(m_bl_iiq[,1],breaks=40,xlab="t (gene level)",main="Blood at assessment inverse IQ")

hist(m_bl_ilan[,1],breaks=40,xlab="t (gene level)",main="Blood at assessment inverse language")

hist(m_bl_mot[,1],breaks=40,xlab="t (gene level)",main="Blood at assessment motor skills")

hist(m_buc_ados[,1],breaks=40,xlab="t (gene level)",main="Buccal ADOS")

hist(m_buc_diag[,1],breaks=40,xlab="t (gene level)",main="Buccal diagnosis")

hist(m_buc_iiq[,1],breaks=40,xlab="t (gene level)",main="Buccal inverse IQ")

hist(m_buc_ilan[,1],breaks=40,xlab="t (gene level)",main="Buccal inverse language")

hist(m_buc_mot[,1],breaks=40,xlab="t (gene level)",main="Buccal motor skills")

Scatterplots

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

df <- merge(m_bl_ados,m_gu_ados,by=0)
rownames(df) <- df[,1]
df[,1]=NULL
colnames(df) <- c("Blood at assessment","Guthrie card")
plot(df, col = rgb(red = 0.6, green = 0.6, blue = 0.6, alpha = 0.5) ,pch=19, cex=0.9, main="ADOS")
abline(v=0,h=0,lty=2)

df <- merge(m_bl_diag,m_gu_diag,by=0)
rownames(df) <- df[,1]
df[,1]=NULL
colnames(df) <- c("Blood at assessment","Guthrie card")
plot(df, col = rgb(red = 0.6, green = 0.6, blue = 0.6, alpha = 0.5) ,pch=19, cex=0.9, main="Diagnosis")
abline(v=0,h=0,lty=2)

df <- merge(m_bl_iiq,m_gu_iiq,by=0)
rownames(df) <- df[,1]
df[,1]=NULL
colnames(df) <- c("Blood at assessment","Guthrie card")
plot(df, col = rgb(red = 0.6, green = 0.6, blue = 0.6, alpha = 0.5) ,pch=19, cex=0.9, main="Inverse IQ")
abline(v=0,h=0,lty=2)

df <- merge(m_bl_ilan,m_gu_ilan,by=0)
rownames(df) <- df[,1]
df[,1]=NULL
colnames(df) <- c("Blood at assessment","Guthrie card")
plot(df, col = rgb(red = 0.6, green = 0.6, blue = 0.6, alpha = 0.5) ,pch=19, cex=0.9, main="Inverse language")
abline(v=0,h=0,lty=2)

df <- merge(m_bl_mot,m_gu_mot,by=0)
rownames(df) <- df[,1]
df[,1]=NULL
colnames(df) <- c("Blood at assessment","Guthrie card")
plot(df, col = rgb(red = 0.6, green = 0.6, blue = 0.6, alpha = 0.5) ,pch=19, cex=0.9, main="Motor skill")
abline(v=0,h=0,lty=2)

Gene ontology biological process

par(mar=c(4,25,1,1))

mgo_gu_ados <- mitch_calc(x=m_gu_ados, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_gu_ados$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_gu_ados$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Guthrie ADOS up") %>% kable_paper("hover", full_width = F)
Guthrie ADOS up
set setSize pANOVA s.dist p.adjustANOVA
5060 PROTEIN UFMYLATION 6 0.0026971 0.7072422 0.0498730
6809 ROSTROCAUDAL NEURAL TUBE PATTERNING 10 0.0002644 0.6661036 0.0080553
2272 MACROPHAGE COLONY STIMULATING FACTOR PRODUCTION 7 0.0022944 0.6654645 0.0442769
7253 TRANSLATIONAL TERMINATION 15 0.0001457 0.5663581 0.0050054
5079 PTERIDINE CONTAINING COMPOUND BIOSYNTHETIC PROCESS 12 0.0006891 0.5657546 0.0174630
6653 RESPONSE TO OXYGEN GLUCOSE DEPRIVATION 11 0.0016087 0.5492040 0.0336299
6488 RESPIRATORY CHAIN COMPLEX III ASSEMBLY 12 0.0018483 0.5190427 0.0374944
4612 POSITIVE REGULATION OF PROTEIN SUMOYLATION 12 0.0018792 0.5182265 0.0379171
4321 POSITIVE REGULATION OF INTERFERON BETA PRODUCTION 37 0.0000005 0.4789639 0.0000331
4933 PROTEIN IMPORT INTO MITOCHONDRIAL MATRIX 19 0.0004536 0.4646387 0.0125981
3942 POLYPRENOL METABOLIC PROCESS 23 0.0001143 0.4646346 0.0042386
5025 PROTEIN PEPTIDYL PROLYL ISOMERIZATION 19 0.0006362 0.4525795 0.0163971
176 ANAPHASE PROMOTING COMPLEX DEPENDENT CATABOLIC PROCESS 24 0.0001433 0.4483067 0.0049527
7468 VIRAL BUDDING 25 0.0002773 0.4200000 0.0083489
3671 OLIGOSACCHARIDE LIPID INTERMEDIATE BIOSYNTHETIC PROCESS 20 0.0018223 0.4026608 0.0370666
1961 INTERFERON BETA PRODUCTION 56 0.0000005 0.3868311 0.0000393
5067 PROTON MOTIVE FORCE DRIVEN ATP SYNTHESIS 65 0.0000002 0.3759364 0.0000134
995 CYTOCHROME COMPLEX ASSEMBLY 38 0.0000632 0.3749719 0.0026856
6772 RIBOSOMAL LARGE SUBUNIT BIOGENESIS 70 0.0000001 0.3682547 0.0000088
278 ATP SYNTHESIS COUPLED ELECTRON TRANSPORT 85 0.0000000 0.3655862 0.0000006 
dn %>% kbl(caption="Guthrie ADOS dn") %>% kable_paper("hover", full_width = F)
Guthrie ADOS dn
set setSize pANOVA s.dist p.adjustANOVA
7513 XENOBIOTIC GLUCURONIDATION 7 0.0004607 -0.7643903 0.0127010
557 CELLULAR GLUCURONIDATION 21 0.0000104 -0.5557311 0.0005904
4537 POSITIVE REGULATION OF PEPTIDYL SERINE PHOSPHORYLATION OF STAT PROTEIN 16 0.0002687 -0.5260639 0.0081554
6859 SENSORY PERCEPTION OF BITTER TASTE 44 0.0000000 -0.5056038 0.0000006
1080 DETECTION OF CHEMICAL STIMULUS INVOLVED IN SENSORY PERCEPTION OF TASTE 46 0.0000002 -0.4446470 0.0000149
2850 NEGATIVE REGULATION OF CELLULAR RESPONSE TO VASCULAR ENDOTHELIAL GROWTH FACTOR STIMULUS 18 0.0014411 -0.4337483 0.0309887
7387 URONIC ACID METABOLIC PROCESS 26 0.0003712 -0.4032660 0.0107028
6864 SENSORY PERCEPTION OF SMELL 380 0.0000000 -0.3902248 0.0000000
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 455 0.0000000 -0.3843351 0.0000000
6867 SENSORY PERCEPTION OF TASTE 66 0.0000001 -0.3751158 0.0000118
1093 DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION 469 0.0000000 -0.3721027 0.0000000
1092 DETECTION OF STIMULUS 589 0.0000000 -0.3247732 0.0000000
1442 ESTROGEN METABOLIC PROCESS 39 0.0008775 -0.3078399 0.0210998
432 CALCIUM ION IMPORT ACROSS PLASMA MEMBRANE 52 0.0002630 -0.2924874 0.0080466
6858 SENSORY PERCEPTION 881 0.0000000 -0.2425926 0.0000000
2077 KERATINIZATION 80 0.0005350 -0.2238936 0.0141283
1942 INORGANIC ION IMPORT ACROSS PLASMA MEMBRANE 132 0.0000400 -0.2069824 0.0018647
1764 G PROTEIN COUPLED RECEPTOR SIGNALING PATHWAY 1157 0.0000000 -0.1753588 0.0000000
6447 REGULATORY NCRNA MEDIATED GENE SILENCING 609 0.0000000 -0.1601689 0.0000000
7514 XENOBIOTIC METABOLIC PROCESS 123 0.0022845 -0.1592359 0.0442769 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Guthrie ADOS GOBP",xlab="S dist")

mgo_gu_diag <- mitch_calc(x=m_gu_diag, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_gu_diag$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_gu_diag$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Guthrie diagnosis up") %>% kable_paper("hover", full_width = F)
Guthrie diagnosis up
set setSize pANOVA s.dist p.adjustANOVA
2961 NEGATIVE REGULATION OF EPITHELIAL CELL PROLIFERATION INVOLVED IN PROSTATE GLAND DEVELOPMENT 6 0.0011760 0.7648265 0.0280071
5060 PROTEIN UFMYLATION 6 0.0025389 0.7115693 0.0488691
6809 ROSTROCAUDAL NEURAL TUBE PATTERNING 10 0.0005170 0.6339548 0.0147934
3765 PATTERN SPECIFICATION INVOLVED IN KIDNEY DEVELOPMENT 8 0.0026088 0.6145838 0.0499585
1368 EPITHELIAL CELL PROLIFERATION INVOLVED IN PROSTATE GLAND DEVELOPMENT 10 0.0015622 0.5775551 0.0338830
514 CARDIOBLAST PROLIFERATION 11 0.0015409 0.5513850 0.0336042
7253 TRANSLATIONAL TERMINATION 15 0.0003636 0.5315958 0.0113556
4612 POSITIVE REGULATION OF PROTEIN SUMOYLATION 12 0.0020244 0.5145497 0.0414020
4321 POSITIVE REGULATION OF INTERFERON BETA PRODUCTION 37 0.0000062 0.4293835 0.0004066
3942 POLYPRENOL METABOLIC PROCESS 23 0.0008815 0.4005644 0.0227199
6772 RIBOSOMAL LARGE SUBUNIT BIOGENESIS 70 0.0000000 0.4004453 0.0000008
1585 GABAERGIC NEURON DIFFERENTIATION 19 0.0025200 0.4002839 0.0488691
1560 FOREBRAIN REGIONALIZATION 24 0.0009401 0.3900202 0.0238220
2345 MATURATION OF LSU RRNA 27 0.0005839 0.3823097 0.0163374
5067 PROTON MOTIVE FORCE DRIVEN ATP SYNTHESIS 65 0.0000002 0.3758727 0.0000163
1961 INTERFERON BETA PRODUCTION 56 0.0000014 0.3730216 0.0001028
5073 PROXIMAL DISTAL PATTERN FORMATION 34 0.0001678 0.3728462 0.0061022
2520 MITOCHONDRIAL ELECTRON TRANSPORT NADH TO UBIQUINONE 43 0.0000333 0.3656706 0.0016810
4970 PROTEIN LOCALIZATION TO CHROMOSOME CENTROMERIC REGION 38 0.0001170 0.3610724 0.0045155
1309 ENDOPLASMIC RETICULUM TO CYTOSOL TRANSPORT 27 0.0012433 0.3589472 0.0290586 
dn %>% kbl(caption="Guthrie diagnosis dn") %>% kable_paper("hover", full_width = F)
Guthrie diagnosis dn
set setSize pANOVA s.dist p.adjustANOVA
7513 XENOBIOTIC GLUCURONIDATION 7 0.0005097 -0.7584994 0.0146410
4537 POSITIVE REGULATION OF PEPTIDYL SERINE PHOSPHORYLATION OF STAT PROTEIN 16 0.0000108 -0.6353694 0.0006604
557 CELLULAR GLUCURONIDATION 21 0.0000006 -0.6298522 0.0000479
6859 SENSORY PERCEPTION OF BITTER TASTE 44 0.0000000 -0.5217609 0.0000003
6864 SENSORY PERCEPTION OF SMELL 380 0.0000000 -0.5114419 0.0000000
6882 SERINE PHOSPHORYLATION OF STAT PROTEIN 20 0.0001122 -0.4988269 0.0043683
7387 URONIC ACID METABOLIC PROCESS 26 0.0000131 -0.4936799 0.0007678
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 455 0.0000000 -0.4835340 0.0000000
1093 DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION 469 0.0000000 -0.4704472 0.0000000
1080 DETECTION OF CHEMICAL STIMULUS INVOLVED IN SENSORY PERCEPTION OF TASTE 46 0.0000001 -0.4601793 0.0000071
1092 DETECTION OF STIMULUS 589 0.0000000 -0.3979450 0.0000000
1442 ESTROGEN METABOLIC PROCESS 39 0.0001112 -0.3575649 0.0043599
6867 SENSORY PERCEPTION OF TASTE 66 0.0000011 -0.3473213 0.0000819
4478 POSITIVE REGULATION OF NATURAL KILLER CELL MEDIATED IMMUNITY 33 0.0013187 -0.3230252 0.0299845
6858 SENSORY PERCEPTION 881 0.0000000 -0.2736140 0.0000000
5952 REGULATION OF NATURAL KILLER CELL MEDIATED IMMUNITY 52 0.0010093 -0.2635352 0.0252357
1764 G PROTEIN COUPLED RECEPTOR SIGNALING PATHWAY 1157 0.0000000 -0.2043171 0.0000000
6447 REGULATORY NCRNA MEDIATED GENE SILENCING 609 0.0000000 -0.1803389 0.0000000
3455 NERVOUS SYSTEM PROCESS 1402 0.0000000 -0.1644524 0.0000000
4834 POST TRANSCRIPTIONAL REGULATION OF GENE EXPRESSION 970 0.0024213 -0.0574879 0.0476767 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Guthrie diagnosis GOBP",xlab="S dist")

mgo_gu_iiq <- mitch_calc(x=m_gu_iiq, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_gu_iiq$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_gu_iiq$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Guthrie inverse IQ up") %>% kable_paper("hover", full_width = F)
Guthrie inverse IQ up
set setSize pANOVA s.dist p.adjustANOVA
1558 FOREBRAIN NEURON FATE COMMITMENT 7 0.0003199 0.7853357 0.0070445
5060 PROTEIN UFMYLATION 6 0.0013071 0.7576996 0.0220080
7043 STEM CELL FATE SPECIFICATION 6 0.0022601 0.7198341 0.0322764
3752 PANCREATIC A CELL DIFFERENTIATION 11 0.0000644 0.6957417 0.0019144
6818 RRNA PSEUDOURIDINE SYNTHESIS 7 0.0018319 0.6800827 0.0277968
4919 PROTEIN DNA COVALENT CROSS LINKING REPAIR 6 0.0040997 0.6766234 0.0486660
6809 ROSTROCAUDAL NEURAL TUBE PATTERNING 10 0.0002186 0.6749629 0.0051703
2499 MIDBRAIN HINDBRAIN BOUNDARY DEVELOPMENT 7 0.0022144 0.6677875 0.0317445
3765 PATTERN SPECIFICATION INVOLVED IN KIDNEY DEVELOPMENT 8 0.0023087 0.6221194 0.0327362
6776 RIBOSOMAL SMALL SUBUNIT EXPORT FROM NUCLEUS 8 0.0023573 0.6208391 0.0330995
822 CEREBRAL CORTEX GABAERGIC INTERNEURON DIFFERENTIATION 10 0.0007125 0.6180601 0.0136090
4967 PROTEIN LOCALIZATION TO CENP A CONTAINING CHROMATIN 16 0.0000272 0.6057554 0.0009012
7144 TELENCEPHALON REGIONALIZATION 13 0.0001756 0.6008820 0.0043466
7253 TRANSLATIONAL TERMINATION 15 0.0000599 0.5984063 0.0017947
514 CARDIOBLAST PROLIFERATION 11 0.0009267 0.5766262 0.0166998
1560 FOREBRAIN REGIONALIZATION 24 0.0000021 0.5590545 0.0001027
6912 SINOATRIAL NODE DEVELOPMENT 12 0.0009146 0.5527025 0.0166271
503 CARDIAC PACEMAKER CELL DEVELOPMENT 9 0.0041570 0.5516522 0.0491144
6653 RESPONSE TO OXYGEN GLUCOSE DEPRIVATION 11 0.0016027 0.5493918 0.0253404
1753 GROWTH PLATE CARTILAGE CHONDROCYTE DIFFERENTIATION 10 0.0032835 0.5368435 0.0412544 
dn %>% kbl(caption="Guthrie inverse IQ dn") %>% kable_paper("hover", full_width = F)
Guthrie inverse IQ dn
set setSize pANOVA s.dist p.adjustANOVA
7513 XENOBIOTIC GLUCURONIDATION 7 0.0001865 -0.8154446 0.0045573
1538 FLAVONOID GLUCURONIDATION 5 0.0037325 -0.7488142 0.0454538
1537 FLAVONE METABOLIC PROCESS 6 0.0025544 -0.7111351 0.0349394
4537 POSITIVE REGULATION OF PEPTIDYL SERINE PHOSPHORYLATION OF STAT PROTEIN 16 0.0000026 -0.6779929 0.0001222
557 CELLULAR GLUCURONIDATION 21 0.0000001 -0.6700741 0.0000067
6864 SENSORY PERCEPTION OF SMELL 380 0.0000000 -0.5933148 0.0000000
1447 ETHANOL OXIDATION 9 0.0029810 -0.5716083 0.0383504
6859 SENSORY PERCEPTION OF BITTER TASTE 44 0.0000000 -0.5687514 0.0000000
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 455 0.0000000 -0.5540033 0.0000000
1093 DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION 469 0.0000000 -0.5438483 0.0000000
1080 DETECTION OF CHEMICAL STIMULUS INVOLVED IN SENSORY PERCEPTION OF TASTE 46 0.0000000 -0.5210309 0.0000001
6882 SERINE PHOSPHORYLATION OF STAT PROTEIN 20 0.0001040 -0.5012135 0.0027754
7387 URONIC ACID METABOLIC PROCESS 26 0.0000155 -0.4895853 0.0005594
1092 DETECTION OF STIMULUS 589 0.0000000 -0.4615625 0.0000000
342 BILE ACID AND BILE SALT TRANSPORT 28 0.0001590 -0.4122759 0.0040153
6613 RESPONSE TO LECTIN 22 0.0012617 -0.3970872 0.0212904
3424 NEGATIVE REGULATION OF VASCULAR ENDOTHELIAL CELL PROLIFERATION 21 0.0022646 -0.3848222 0.0322793
1442 ESTROGEN METABOLIC PROCESS 39 0.0000498 -0.3753279 0.0015411
2157 LIPID DIGESTION 21 0.0030233 -0.3737607 0.0388280
183 ANDROGEN METABOLIC PROCESS 29 0.0007363 -0.3621217 0.0138884 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Guthrie inverse IQ GOBP",xlab="S dist")

mgo_gu_ilan <- mitch_calc(x=m_gu_ilan, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_gu_ilan$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_gu_ilan$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Guthrie inverse language up") %>% kable_paper("hover", full_width = F)
Guthrie inverse language up
set setSize pANOVA s.dist p.adjustANOVA
5060 PROTEIN UFMYLATION 6 0.0028276 0.7038434 0.0343234
2499 MIDBRAIN HINDBRAIN BOUNDARY DEVELOPMENT 7 0.0024353 0.6615501 0.0307515
1558 FOREBRAIN NEURON FATE COMMITMENT 7 0.0031749 0.6438775 0.0371607
6809 ROSTROCAUDAL NEURAL TUBE PATTERNING 10 0.0005141 0.6342244 0.0087541
2966 NEGATIVE REGULATION OF ERYTHROCYTE DIFFERENTIATION 8 0.0029079 0.6078231 0.0348488
7253 TRANSLATIONAL TERMINATION 15 0.0000922 0.5830203 0.0020871
514 CARDIOBLAST PROLIFERATION 11 0.0008916 0.5785050 0.0138074
6653 RESPONSE TO OXYGEN GLUCOSE DEPRIVATION 11 0.0009846 0.5736691 0.0149358
5079 PTERIDINE CONTAINING COMPOUND BIOSYNTHETIC PROCESS 12 0.0005812 0.5734825 0.0097205
3752 PANCREATIC A CELL DIFFERENTIATION 11 0.0015680 0.5505028 0.0214951
2562 MITOTIC DNA REPLICATION CHECKPOINT SIGNALING 10 0.0030833 0.5403927 0.0362831
4967 PROTEIN LOCALIZATION TO CENP A CONTAINING CHROMATIN 16 0.0003481 0.5163798 0.0062224
3732 OUTER MITOCHONDRIAL MEMBRANE ORGANIZATION 15 0.0005640 0.5141847 0.0095005
6603 RESPONSE TO INTERLEUKIN 7 12 0.0027130 0.4998652 0.0334718
4612 POSITIVE REGULATION OF PROTEIN SUMOYLATION 12 0.0027403 0.4993560 0.0336964
2522 MITOCHONDRIAL ELECTRON TRANSPORT UBIQUINOL TO CYTOCHROME C 12 0.0028601 0.4971769 0.0345506
2375 MEIOTIC SPINDLE ORGANIZATION 18 0.0002766 0.4949940 0.0051267
1181 DNA REPLICATION CHECKPOINT SIGNALING 18 0.0004403 0.4784404 0.0075827
4321 POSITIVE REGULATION OF INTERFERON BETA PRODUCTION 37 0.0000008 0.4681637 0.0000350
176 ANAPHASE PROMOTING COMPLEX DEPENDENT CATABOLIC PROCESS 24 0.0000840 0.4636510 0.0019404 
dn %>% kbl(caption="Guthrie inverse language dn") %>% kable_paper("hover", full_width = F)
Guthrie inverse language dn
set setSize pANOVA s.dist p.adjustANOVA
7513 XENOBIOTIC GLUCURONIDATION 7 0.0001107 -0.8436413 0.0024359
1538 FLAVONOID GLUCURONIDATION 5 0.0014885 -0.8203198 0.0205932
1537 FLAVONE METABOLIC PROCESS 6 0.0010570 -0.7719535 0.0158470
557 CELLULAR GLUCURONIDATION 21 0.0000001 -0.6722573 0.0000050
4537 POSITIVE REGULATION OF PEPTIDYL SERINE PHOSPHORYLATION OF STAT PROTEIN 16 0.0000068 -0.6494461 0.0002308
5528 REGULATION OF ENAMEL MINERALIZATION 7 0.0030259 -0.6471117 0.0358624
114 ALKALOID METABOLIC PROCESS 7 0.0036131 -0.6351117 0.0410138
6859 SENSORY PERCEPTION OF BITTER TASTE 44 0.0000000 -0.5374640 0.0000000
6864 SENSORY PERCEPTION OF SMELL 380 0.0000000 -0.5078732 0.0000000
1080 DETECTION OF CHEMICAL STIMULUS INVOLVED IN SENSORY PERCEPTION OF TASTE 46 0.0000000 -0.5004764 0.0000003
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 455 0.0000000 -0.4877219 0.0000000
1093 DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION 469 0.0000000 -0.4755322 0.0000000
2157 LIPID DIGESTION 21 0.0001658 -0.4746622 0.0033816
7387 URONIC ACID METABOLIC PROCESS 26 0.0000341 -0.4694822 0.0009121
6882 SERINE PHOSPHORYLATION OF STAT PROTEIN 20 0.0002891 -0.4681334 0.0052944
3681 OPSONIZATION 16 0.0015210 -0.4577810 0.0209658
438 CALCIUM ION TRANSMEMBRANE TRANSPORT VIA HIGH VOLTAGE GATED CALCIUM CHANNEL 18 0.0009132 -0.4514004 0.0139976
5480 REGULATION OF DELAYED RECTIFIER POTASSIUM CHANNEL ACTIVITY 18 0.0011376 -0.4429763 0.0165919
7512 XENOBIOTIC EXPORT FROM CELL 26 0.0001304 -0.4333913 0.0027713
2077 KERATINIZATION 80 0.0000000 -0.4333510 0.0000000 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Guthrie inverse language GOBP",xlab="S dist")

mgo_gu_mot <- mitch_calc(x=m_gu_mot, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_gu_mot$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_gu_mot$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Guthrie motor up") %>% kable_paper("hover", full_width = F)
Guthrie motor up
set setSize pANOVA s.dist p.adjustANOVA
1558 FOREBRAIN NEURON FATE COMMITMENT 7 0.0007223 0.7378492 0.0157991
1372 EPITHELIAL MESENCHYMAL CELL SIGNALING 6 0.0019751 0.7293117 0.0335546
6911 SINOATRIAL NODE CELL DIFFERENTIATION 6 0.0019900 0.7287877 0.0335976
7043 STEM CELL FATE SPECIFICATION 6 0.0024633 0.7137253 0.0393605
241 APOPTOTIC PROCESS INVOLVED IN HEART MORPHOGENESIS 7 0.0017889 0.6816099 0.0309506
636 CELLULAR RESPONSE TO IRON ION 8 0.0011795 0.6622119 0.0223046
7383 URETER MORPHOGENESIS 7 0.0029719 0.6483181 0.0447334
7144 TELENCEPHALON REGIONALIZATION 13 0.0000614 0.6417975 0.0024581
822 CEREBRAL CORTEX GABAERGIC INTERNEURON DIFFERENTIATION 10 0.0007417 0.6160385 0.0160413
7014 SPINAL CORD ASSOCIATION NEURON DIFFERENTIATION 12 0.0004681 0.5831723 0.0115886
504 CARDIAC PACEMAKER CELL DIFFERENTIATION 11 0.0013409 0.5583855 0.0249176
5571 REGULATION OF EXIT FROM MITOSIS 18 0.0000496 0.5523247 0.0020844
2104 LATERAL MESODERM DEVELOPMENT 15 0.0002258 0.5499476 0.0067888
3564 NORADRENERGIC NEURON DIFFERENTIATION 10 0.0028207 0.5453794 0.0431481
1560 FOREBRAIN REGIONALIZATION 24 0.0000128 0.5143965 0.0006520
6912 SINOATRIAL NODE DEVELOPMENT 12 0.0024991 0.5040212 0.0396089
7382 URETER DEVELOPMENT 18 0.0004091 0.4810870 0.0105450
3452 NEPHRON TUBULE FORMATION 20 0.0002821 0.4689559 0.0081340
1206 DORSAL SPINAL CORD DEVELOPMENT 20 0.0003043 0.4664210 0.0085463
218 ANTIGEN PROCESSING AND PRESENTATION OF PEPTIDE ANTIGEN VIA MHC CLASS IB 18 0.0006934 0.4617720 0.0153476 
dn %>% kbl(caption="Guthrie motor dn") %>% kable_paper("hover", full_width = F)
Guthrie motor dn
set setSize pANOVA s.dist p.adjustANOVA
4537 POSITIVE REGULATION OF PEPTIDYL SERINE PHOSPHORYLATION OF STAT PROTEIN 16 0.0000004 -0.7302779 0.0000369
6882 SERINE PHOSPHORYLATION OF STAT PROTEIN 20 0.0000103 -0.5696930 0.0005401
6864 SENSORY PERCEPTION OF SMELL 380 0.0000000 -0.5205650 0.0000000
557 CELLULAR GLUCURONIDATION 21 0.0000368 -0.5200981 0.0016481
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 455 0.0000000 -0.4758215 0.0000000
1093 DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION 469 0.0000000 -0.4617613 0.0000000
6390 REGULATION OF T CELL CHEMOTAXIS 16 0.0016220 -0.4550791 0.0289269
2890 NEGATIVE REGULATION OF CHOLESTEROL EFFLUX 22 0.0003273 -0.4424029 0.0088510
6859 SENSORY PERCEPTION OF BITTER TASTE 44 0.0000005 -0.4368320 0.0000445
3425 NEGATIVE REGULATION OF VASCULAR ENDOTHELIAL GROWTH FACTOR PRODUCTION 22 0.0005505 -0.4254465 0.0129873
3350 NEGATIVE REGULATION OF STEROL TRANSPORT 31 0.0000504 -0.4205748 0.0020974
7387 URONIC ACID METABOLIC PROCESS 26 0.0002867 -0.4108847 0.0082036
342 BILE ACID AND BILE SALT TRANSPORT 28 0.0003257 -0.3923449 0.0088510
6613 RESPONSE TO LECTIN 22 0.0017048 -0.3863524 0.0300297
2611 MONOCYTE CHEMOTACTIC PROTEIN 1 PRODUCTION 21 0.0023501 -0.3834181 0.0378741
2730 NATURAL KILLER CELL ACTIVATION INVOLVED IN IMMUNE RESPONSE 30 0.0002844 -0.3827660 0.0081690
1080 DETECTION OF CHEMICAL STIMULUS INVOLVED IN SENSORY PERCEPTION OF TASTE 46 0.0000076 -0.3813558 0.0004224
5803 REGULATION OF LYMPHOCYTE CHEMOTAXIS 26 0.0007912 -0.3801715 0.0169162
1092 DETECTION OF STIMULUS 589 0.0000000 -0.3796645 0.0000000
1442 ESTROGEN METABOLIC PROCESS 39 0.0028776 -0.2757789 0.0438395 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Guthrie motor GOBP",xlab="S dist")

mgo_bl_ados <- mitch_calc(x=m_bl_ados, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_bl_ados$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_bl_ados$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Blood at assessment ADOS up") %>% kable_paper("hover", full_width = F)
Blood at assessment ADOS up
set setSize pANOVA s.dist p.adjustANOVA
4967 PROTEIN LOCALIZATION TO CENP A CONTAINING CHROMATIN 16 0.0001573 0.5456352 0.0248248
3118 NEGATIVE REGULATION OF MEGAKARYOCYTE DIFFERENTIATION 18 0.0001638 0.5130762 0.0251549
6469 RENAL TUBULAR SECRETION 19 0.0002584 0.4841131 0.0341218
1452 EXCRETION 29 0.0000635 0.4290110 0.0116549
5067 PROTON MOTIVE FORCE DRIVEN ATP SYNTHESIS 65 0.0000010 0.3501087 0.0002914
1008 CYTOPLASMIC TRANSLATION 141 0.0000000 0.3323978 0.0000000
6775 RIBOSOMAL SMALL SUBUNIT BIOGENESIS 101 0.0000000 0.3222053 0.0000096
3629 NUCLEOSOME ORGANIZATION 119 0.0000000 0.3099292 0.0000024
2347 MATURATION OF SSU RRNA 51 0.0001878 0.3022663 0.0277095
2719 NADH DEHYDROGENASE COMPLEX ASSEMBLY 52 0.0002032 0.2977573 0.0294083
278 ATP SYNTHESIS COUPLED ELECTRON TRANSPORT 85 0.0000061 0.2837711 0.0014760
4916 PROTEIN DNA COMPLEX ASSEMBLY 203 0.0000000 0.2822657 0.0000000
2534 MITOCHONDRIAL RESPIRATORY CHAIN COMPLEX ASSEMBLY 92 0.0000086 0.2682906 0.0020318
6780 RIBOSOME BIOGENESIS 304 0.0000000 0.2673278 0.0000000
2542 MITOCHONDRIAL TRANSLATION 130 0.0000003 0.2591115 0.0001047
3744 OXIDATIVE PHOSPHORYLATION 126 0.0000005 0.2587032 0.0001583
276 ATP BIOSYNTHETIC PROCESS 89 0.0000259 0.2579086 0.0051381
2526 MITOCHONDRIAL GENE EXPRESSION 162 0.0000000 0.2513328 0.0000140
6815 RRNA METABOLIC PROCESS 254 0.0000000 0.2413736 0.0000000
99 AEROBIC RESPIRATION 170 0.0000002 0.2312740 0.0000666 
dn %>% kbl(caption="Blood at assessment ADOS dn") %>% kable_paper("hover", full_width = F)
Blood at assessment ADOS dn
set setSize pANOVA s.dist p.adjustANOVA
557 CELLULAR GLUCURONIDATION 21 0.0002416 -0.4626730 0.0324734
6859 SENSORY PERCEPTION OF BITTER TASTE 44 0.0000223 -0.3693670 0.0048035
2506 MIRNA MEDIATED GENE SILENCING BY INHIBITION OF TRANSLATION 91 0.0000245 -0.2558517 0.0050574
6864 SENSORY PERCEPTION OF SMELL 383 0.0000000 -0.2518434 0.0000000
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 458 0.0000000 -0.2441348 0.0000000
1093 DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION 472 0.0000000 -0.2414203 0.0000000
1092 DETECTION OF STIMULUS 592 0.0000000 -0.2025938 0.0000000
6447 REGULATORY NCRNA MEDIATED GENE SILENCING 610 0.0000000 -0.1466113 0.0000004
6858 SENSORY PERCEPTION 884 0.0000000 -0.1460074 0.0000000
1764 G PROTEIN COUPLED RECEPTOR SIGNALING PATHWAY 1160 0.0000000 -0.1318204 0.0000000
2910 NEGATIVE REGULATION OF CYTOKINE PRODUCTION 359 0.0002184 -0.1135510 0.0304412
3455 NERVOUS SYSTEM PROCESS 1405 0.0000000 -0.1024019 0.0000001 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Blood at assessment ADOS GOBP",xlab="S dist",cex.main=0.75)

mgo_bl_diag <- mitch_calc(x=m_bl_diag, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_bl_diag$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_bl_diag$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Blood at assessment diagnosis up") %>% kable_paper("hover", full_width = F)
Blood at assessment diagnosis up
set setSize pANOVA s.dist p.adjustANOVA 
dn %>% kbl(caption="Blood at assessment diagnosis dn") %>% kable_paper("hover", full_width = F)
Blood at assessment diagnosis dn
set setSize pANOVA s.dist p.adjustANOVA 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
if ( length(b)>1 ) {
  barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Blood at assessment diagnosis GOBP",xlab="S dist",cex.main=0.75)
}

mgo_bl_iiq <- mitch_calc(x=m_bl_iiq, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_bl_iiq$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_bl_iiq$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Blood at assessment inverse IQ up") %>% kable_paper("hover", full_width = F)
Blood at assessment inverse IQ up
set setSize pANOVA s.dist p.adjustANOVA
2535 MITOCHONDRIAL RIBOSOME ASSEMBLY 10 0.0008181 0.6110976 0.0341512
4431 POSITIVE REGULATION OF MITOCHONDRIAL TRANSLATION 16 0.0005962 0.4957003 0.0262023
6489 RESPIRATORY CHAIN COMPLEX IV ASSEMBLY 29 0.0000136 0.4665801 0.0011924
2514 MITOCHONDRIAL CYTOCHROME C OXIDASE ASSEMBLY 25 0.0000634 0.4620593 0.0045440
995 CYTOCHROME COMPLEX ASSEMBLY 38 0.0000019 0.4459714 0.0002329
1008 CYTOPLASMIC TRANSLATION 141 0.0000000 0.4441172 0.0000000
6775 RIBOSOMAL SMALL SUBUNIT BIOGENESIS 101 0.0000000 0.4148811 0.0000000
6355 REGULATION OF TRANSCRIPTION FROM RNA POLYMERASE II PROMOTER IN RESPONSE TO STRESS 32 0.0000687 0.4065210 0.0048786
7358 U2 TYPE PRESPLICEOSOME ASSEMBLY 22 0.0009829 0.4058058 0.0393481
6778 RIBOSOME ASSEMBLY 58 0.0000001 0.4002045 0.0000214
5067 PROTON MOTIVE FORCE DRIVEN ATP SYNTHESIS 65 0.0000001 0.3852551 0.0000132
6772 RIBOSOMAL LARGE SUBUNIT BIOGENESIS 70 0.0000000 0.3830616 0.0000059
2534 MITOCHONDRIAL RESPIRATORY CHAIN COMPLEX ASSEMBLY 92 0.0000000 0.3773624 0.0000001
5882 REGULATION OF MITOCHONDRIAL GENE EXPRESSION 30 0.0003537 0.3767902 0.0173962
1452 EXCRETION 29 0.0004986 0.3734723 0.0224681
5890 REGULATION OF MITOCHONDRIAL TRANSLATION 25 0.0012311 0.3733369 0.0467941
2719 NADH DEHYDROGENASE COMPLEX ASSEMBLY 52 0.0000037 0.3709489 0.0003958
3345 NEGATIVE REGULATION OF STEM CELL DIFFERENTIATION 26 0.0012063 0.3667523 0.0463263
6780 RIBOSOME BIOGENESIS 304 0.0000000 0.3660072 0.0000000
5516 REGULATION OF DNA TEMPLATED TRANSCRIPTION IN RESPONSE TO STRESS 37 0.0001384 0.3619882 0.0083324 
dn %>% kbl(caption="Blood at assessment inverse IQ dn") %>% kable_paper("hover", full_width = F)
Blood at assessment inverse IQ dn
set setSize pANOVA s.dist p.adjustANOVA
6859 SENSORY PERCEPTION OF BITTER TASTE 44 0.0000001 -0.4682343 0.0000132
4537 POSITIVE REGULATION OF PEPTIDYL SERINE PHOSPHORYLATION OF STAT PROTEIN 17 0.0009507 -0.4629011 0.0384662
3425 NEGATIVE REGULATION OF VASCULAR ENDOTHELIAL GROWTH FACTOR PRODUCTION 22 0.0003250 -0.4426300 0.0166407
1080 DETECTION OF CHEMICAL STIMULUS INVOLVED IN SENSORY PERCEPTION OF TASTE 46 0.0000003 -0.4367748 0.0000435
6867 SENSORY PERCEPTION OF TASTE 66 0.0000151 -0.3079537 0.0012896
1028 DEFENSE RESPONSE TO FUNGUS 52 0.0002657 -0.2922821 0.0144876
2507 MIRNA MEDIATED GENE SILENCING BY MRNA DESTABILIZATION 62 0.0000893 -0.2876532 0.0059452
2506 MIRNA MEDIATED GENE SILENCING BY INHIBITION OF TRANSLATION 91 0.0000139 -0.2634818 0.0012037
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 458 0.0000000 -0.2178743 0.0000000
1093 DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION 472 0.0000000 -0.2136265 0.0000000
6447 REGULATORY NCRNA MEDIATED GENE SILENCING 610 0.0000000 -0.2010204 0.0000000
6864 SENSORY PERCEPTION OF SMELL 383 0.0000000 -0.1972771 0.0000000
1092 DETECTION OF STIMULUS 592 0.0000000 -0.1924343 0.0000000
6858 SENSORY PERCEPTION 884 0.0000000 -0.1460842 0.0000000
6861 SENSORY PERCEPTION OF LIGHT STIMULUS 208 0.0003358 -0.1442153 0.0170583
1764 G PROTEIN COUPLED RECEPTOR SIGNALING PATHWAY 1160 0.0000000 -0.1384683 0.0000000
2910 NEGATIVE REGULATION OF CYTOKINE PRODUCTION 359 0.0002341 -0.1130090 0.0129548
3455 NERVOUS SYSTEM PROCESS 1405 0.0000000 -0.1022411 0.0000000
2166 LIPID LOCALIZATION 496 0.0001314 -0.1002377 0.0081075
26 ACTIN FILAMENT BASED PROCESS 774 0.0000968 -0.0823362 0.0062265 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Blood at assessment inverse IQ GOBP",xlab="S dist",cex.main=0.75)

mgo_bl_ilan <- mitch_calc(x=m_bl_ilan, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_bl_ilan$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_bl_ilan$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Blood at assessment inverse language up") %>% kable_paper("hover", full_width = F)
Blood at assessment inverse language up
set setSize pANOVA s.dist p.adjustANOVA
2535 MITOCHONDRIAL RIBOSOME ASSEMBLY 10 0.0006614 0.6217733 0.0338642
4967 PROTEIN LOCALIZATION TO CENP A CONTAINING CHROMATIN 16 0.0007751 0.4853384 0.0379255
4431 POSITIVE REGULATION OF MITOCHONDRIAL TRANSLATION 16 0.0008403 0.4821052 0.0397761
2514 MITOCHONDRIAL CYTOCHROME C OXIDASE ASSEMBLY 25 0.0000499 0.4685786 0.0042647
6489 RESPIRATORY CHAIN COMPLEX IV ASSEMBLY 29 0.0000132 0.4673548 0.0013591
1008 CYTOPLASMIC TRANSLATION 141 0.0000000 0.4536339 0.0000000
995 CYTOCHROME COMPLEX ASSEMBLY 38 0.0000040 0.4321931 0.0005015
6775 RIBOSOMAL SMALL SUBUNIT BIOGENESIS 101 0.0000000 0.4208751 0.0000000
7358 U2 TYPE PRESPLICEOSOME ASSEMBLY 22 0.0007990 0.4129191 0.0382999
6778 RIBOSOME ASSEMBLY 58 0.0000001 0.4108687 0.0000125
1452 EXCRETION 29 0.0001692 0.4034464 0.0119018
6355 REGULATION OF TRANSCRIPTION FROM RNA POLYMERASE II PROMOTER IN RESPONSE TO STRESS 32 0.0001228 0.3922055 0.0093351
5067 PROTON MOTIVE FORCE DRIVEN ATP SYNTHESIS 65 0.0000001 0.3907457 0.0000108
6771 RIBOSOMAL LARGE SUBUNIT ASSEMBLY 24 0.0009568 0.3894367 0.0438774
6772 RIBOSOMAL LARGE SUBUNIT BIOGENESIS 70 0.0000000 0.3861471 0.0000051
2719 NADH DEHYDROGENASE COMPLEX ASSEMBLY 52 0.0000020 0.3806227 0.0002898
2534 MITOCHONDRIAL RESPIRATORY CHAIN COMPLEX ASSEMBLY 92 0.0000000 0.3762124 0.0000001
6780 RIBOSOME BIOGENESIS 304 0.0000000 0.3621178 0.0000000
2542 MITOCHONDRIAL TRANSLATION 130 0.0000000 0.3584209 0.0000000
2347 MATURATION OF SSU RRNA 51 0.0000105 0.3566204 0.0011279 
dn %>% kbl(caption="Blood at assessment inverse language dn") %>% kable_paper("hover", full_width = F)
Blood at assessment inverse language dn
set setSize pANOVA s.dist p.adjustANOVA
3168 NEGATIVE REGULATION OF MYOBLAST PROLIFERATION 9 0.0008757 -0.6405000 0.0409345
6859 SENSORY PERCEPTION OF BITTER TASTE 44 0.0000006 -0.4348725 0.0000932
536 CD4 POSITIVE ALPHA BETA T CELL CYTOKINE PRODUCTION 19 0.0011314 -0.4313776 0.0486546
3425 NEGATIVE REGULATION OF VASCULAR ENDOTHELIAL GROWTH FACTOR PRODUCTION 22 0.0009043 -0.4086805 0.0417535
1080 DETECTION OF CHEMICAL STIMULUS INVOLVED IN SENSORY PERCEPTION OF TASTE 46 0.0000027 -0.4000845 0.0003503
6867 SENSORY PERCEPTION OF TASTE 66 0.0000976 -0.2772866 0.0076531
2507 MIRNA MEDIATED GENE SILENCING BY MRNA DESTABILIZATION 62 0.0002367 -0.2699035 0.0150087
1028 DEFENSE RESPONSE TO FUNGUS 52 0.0008946 -0.2662432 0.0415624
2506 MIRNA MEDIATED GENE SILENCING BY INHIBITION OF TRANSLATION 91 0.0000142 -0.2632154 0.0014437
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 458 0.0000000 -0.2220818 0.0000000
1093 DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION 472 0.0000000 -0.2138277 0.0000000
6864 SENSORY PERCEPTION OF SMELL 383 0.0000000 -0.2073461 0.0000000
1092 DETECTION OF STIMULUS 592 0.0000000 -0.1918749 0.0000000
4833 POST GOLGI VESICLE MEDIATED TRANSPORT 101 0.0009796 -0.1897862 0.0444140
6447 REGULATORY NCRNA MEDIATED GENE SILENCING 610 0.0000000 -0.1819526 0.0000000
6858 SENSORY PERCEPTION 884 0.0000000 -0.1449288 0.0000000
1764 G PROTEIN COUPLED RECEPTOR SIGNALING PATHWAY 1160 0.0000000 -0.1366128 0.0000000
6861 SENSORY PERCEPTION OF LIGHT STIMULUS 208 0.0006907 -0.1364662 0.0351253
2910 NEGATIVE REGULATION OF CYTOKINE PRODUCTION 359 0.0003895 -0.1089577 0.0227988
3455 NERVOUS SYSTEM PROCESS 1405 0.0000000 -0.1033921 0.0000000 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Blood at assessment inverse language GOBP",xlab="S dist",cex.main=0.7)

mgo_bl_mot <- mitch_calc(x=m_bl_mot, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_bl_mot$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_bl_mot$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Blood at assessment motor up") %>% kable_paper("hover", full_width = F)
Blood at assessment motor up
set setSize pANOVA s.dist p.adjustANOVA 
dn %>% kbl(caption="Blood at assessment motor dn") %>% kable_paper("hover", full_width = F)
Blood at assessment motor dn
set setSize pANOVA s.dist p.adjustANOVA
6447 REGULATORY NCRNA MEDIATED GENE SILENCING 610 0 -0.1294254 0.0003546 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
if ( length(b)>1 ) {
  barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Blood at assessment motor GOBP",xlab="S dist",cex.main=0.7)
}

mgo_buc_ados <- mitch_calc(x=m_buc_ados, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_buc_ados$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_buc_ados$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Buccal ADOS up") %>% kable_paper("hover", full_width = F)
Buccal ADOS up
set setSize pANOVA s.dist p.adjustANOVA
4458 POSITIVE REGULATION OF MUSCLE ADAPTATION 13 0.0007494 0.5398844 0.0430538
4967 PROTEIN LOCALIZATION TO CENP A CONTAINING CHROMATIN 16 0.0004605 0.5057140 0.0306727
2347 MATURATION OF SSU RRNA 51 0.0001221 0.3109133 0.0119374
6775 RIBOSOMAL SMALL SUBUNIT BIOGENESIS 101 0.0000013 0.2784706 0.0004952
1008 CYTOPLASMIC TRANSLATION 141 0.0000001 0.2621247 0.0000523
3629 NUCLEOSOME ORGANIZATION 119 0.0000092 0.2353487 0.0018674
6772 RIBOSOMAL LARGE SUBUNIT BIOGENESIS 70 0.0006648 0.2352294 0.0406781
6815 RRNA METABOLIC PROCESS 254 0.0000000 0.2070210 0.0000099
6780 RIBOSOME BIOGENESIS 304 0.0000000 0.2021478 0.0000012
1141 DIGESTIVE SYSTEM DEVELOPMENT 142 0.0000367 0.2005955 0.0053056
4916 PROTEIN DNA COMPLEX ASSEMBLY 203 0.0000011 0.1979692 0.0004952
2542 MITOCHONDRIAL TRANSLATION 130 0.0002022 0.1887122 0.0161891
5898 REGULATION OF MITOTIC NUCLEAR DIVISION 118 0.0005438 0.1842754 0.0352812
2526 MITOCHONDRIAL GENE EXPRESSION 162 0.0001548 0.1722223 0.0137038
6758 RIBONUCLEOPROTEIN COMPLEX BIOGENESIS 448 0.0000000 0.1603975 0.0000048
572 CELLULAR RESPIRATION 216 0.0000755 0.1562398 0.0086490
1239 ELECTRON TRANSPORT CHAIN 156 0.0008125 0.1553182 0.0452956
5964 REGULATION OF NEURON DIFFERENTIATION 192 0.0008707 0.1393120 0.0478317
5932 REGULATION OF MUSCLE SYSTEM PROCESS 239 0.0002097 0.1391770 0.0165370
4922 PROTEIN FOLDING 209 0.0005760 0.1381253 0.0364296 
dn %>% kbl(caption="Buccal ADOS dn") %>% kable_paper("hover", full_width = F)
Buccal ADOS dn
set setSize pANOVA s.dist p.adjustANOVA
4537 POSITIVE REGULATION OF PEPTIDYL SERINE PHOSPHORYLATION OF STAT PROTEIN 17 0.0000003 -0.7138465 0.0001996
6882 SERINE PHOSPHORYLATION OF STAT PROTEIN 21 0.0000014 -0.6078321 0.0005062
536 CD4 POSITIVE ALPHA BETA T CELL CYTOKINE PRODUCTION 19 0.0001190 -0.5098671 0.0119374
4385 POSITIVE REGULATION OF LYMPHOCYTE CHEMOTAXIS 20 0.0003203 -0.4647085 0.0241040
5803 REGULATION OF LYMPHOCYTE CHEMOTAXIS 26 0.0000794 -0.4470420 0.0087830
2730 NATURAL KILLER CELL ACTIVATION INVOLVED IN IMMUNE RESPONSE 31 0.0000304 -0.4327074 0.0048656
7330 T CELL CHEMOTAXIS 27 0.0001415 -0.4230449 0.0128320
2378 MELANOCYTE DIFFERENTIATION 24 0.0004407 -0.4143720 0.0296101
6859 SENSORY PERCEPTION OF BITTER TASTE 44 0.0000026 -0.4095691 0.0007463
4388 POSITIVE REGULATION OF LYMPHOCYTE MIGRATION 40 0.0000077 -0.4087746 0.0016943
6864 SENSORY PERCEPTION OF SMELL 382 0.0000000 -0.3951192 0.0000000
4478 POSITIVE REGULATION OF NATURAL KILLER CELL MEDIATED IMMUNITY 33 0.0000931 -0.3929943 0.0097358
4767 POSITIVE REGULATION OF T CELL MIGRATION 33 0.0001214 -0.3865151 0.0119374
1080 DETECTION OF CHEMICAL STIMULUS INVOLVED IN SENSORY PERCEPTION OF TASTE 46 0.0000116 -0.3735514 0.0021989
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 457 0.0000000 -0.3729835 0.0000000
2226 LYMPHOCYTE CHEMOTAXIS 62 0.0000007 -0.3645783 0.0003423
1093 DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION 471 0.0000000 -0.3624202 0.0000000
4390 POSITIVE REGULATION OF MACROPHAGE ACTIVATION 30 0.0007894 -0.3540169 0.0447955
5806 REGULATION OF LYMPHOCYTE MIGRATION 66 0.0000117 -0.3119235 0.0021989
1092 DETECTION OF STIMULUS 591 0.0000000 -0.3109072 0.0000000 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Buccal ADOS GOBP",xlab="S dist")

mgo_buc_diag <- mitch_calc(x=m_buc_diag, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_buc_diag$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_buc_diag$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Buccal diagnosis up") %>% kable_paper("hover", full_width = F)
Buccal diagnosis up
set setSize pANOVA s.dist p.adjustANOVA
6178 REGULATION OF RECEPTOR MEDIATED ENDOCYTOSIS INVOLVED IN CHOLESTEROL TRANSPORT 6 0.0008918 0.7831987 0.0469349
2347 MATURATION OF SSU RRNA 51 0.0004528 0.2838441 0.0282493
6775 RIBOSOMAL SMALL SUBUNIT BIOGENESIS 101 0.0000096 0.2548377 0.0014263
1008 CYTOPLASMIC TRANSLATION 141 0.0000005 0.2454890 0.0001806
3629 NUCLEOSOME ORGANIZATION 119 0.0002219 0.1959404 0.0159073
1141 DIGESTIVE SYSTEM DEVELOPMENT 142 0.0001469 0.1844992 0.0115156
6815 RRNA METABOLIC PROCESS 254 0.0000007 0.1811510 0.0002254
6780 RIBOSOME BIOGENESIS 304 0.0000001 0.1804281 0.0000414
4916 PROTEIN DNA COMPLEX ASSEMBLY 203 0.0001433 0.1547685 0.0113834
2526 MITOCHONDRIAL GENE EXPRESSION 162 0.0007458 0.1535124 0.0412716
6758 RIBONUCLEOPROTEIN COMPLEX BIOGENESIS 448 0.0000007 0.1366324 0.0002312
572 CELLULAR RESPIRATION 216 0.0005692 0.1360171 0.0337673
5932 REGULATION OF MUSCLE SYSTEM PROCESS 239 0.0002960 0.1358653 0.0200252
3773 PEPTIDE BIOSYNTHETIC PROCESS 806 0.0000075 0.0927485 0.0012312
1618 GENERATION OF PRECURSOR METABOLITES AND ENERGY 470 0.0008817 0.0895121 0.0467325
1274 EMBRYO DEVELOPMENT ENDING IN BIRTH OR EGG HATCHING 659 0.0000930 0.0892178 0.0082299
3776 PEPTIDE METABOLIC PROCESS 930 0.0000161 0.0833959 0.0020919
128 AMIDE BIOSYNTHETIC PROCESS 938 0.0001146 0.0742881 0.0098019
1273 EMBRYO DEVELOPMENT 1094 0.0000417 0.0733425 0.0042998
4918 PROTEIN DNA COMPLEX ORGANIZATION 796 0.0005743 0.0717535 0.0337673 
dn %>% kbl(caption="Buccal diagnosis dn") %>% kable_paper("hover", full_width = F)
Buccal diagnosis dn
set setSize pANOVA s.dist p.adjustANOVA
4537 POSITIVE REGULATION OF PEPTIDYL SERINE PHOSPHORYLATION OF STAT PROTEIN 17 0.0000021 -0.6641916 0.0004817
4556 POSITIVE REGULATION OF PLATELET ACTIVATION 9 0.0007075 -0.6518482 0.0394406
7354 T HELPER 2 CELL CYTOKINE PRODUCTION 13 0.0002354 -0.5890091 0.0167136
6882 SERINE PHOSPHORYLATION OF STAT PROTEIN 21 0.0000044 -0.5786554 0.0008276
536 CD4 POSITIVE ALPHA BETA T CELL CYTOKINE PRODUCTION 19 0.0000154 -0.5726506 0.0020393
4385 POSITIVE REGULATION OF LYMPHOCYTE CHEMOTAXIS 20 0.0004542 -0.4528429 0.0282493
5803 REGULATION OF LYMPHOCYTE CHEMOTAXIS 26 0.0001212 -0.4354136 0.0100493
2378 MELANOCYTE DIFFERENTIATION 24 0.0002615 -0.4304308 0.0182245
7330 T CELL CHEMOTAXIS 27 0.0002093 -0.4121562 0.0153265
4388 POSITIVE REGULATION OF LYMPHOCYTE MIGRATION 40 0.0000075 -0.4092489 0.0012312
4478 POSITIVE REGULATION OF NATURAL KILLER CELL MEDIATED IMMUNITY 33 0.0000486 -0.4085401 0.0048090
4390 POSITIVE REGULATION OF MACROPHAGE ACTIVATION 30 0.0001437 -0.4009645 0.0113834
2730 NATURAL KILLER CELL ACTIVATION INVOLVED IN IMMUNE RESPONSE 31 0.0001228 -0.3984621 0.0100493
4767 POSITIVE REGULATION OF T CELL MIGRATION 33 0.0001179 -0.3872369 0.0099667
6859 SENSORY PERCEPTION OF BITTER TASTE 44 0.0000323 -0.3620771 0.0035780
6864 SENSORY PERCEPTION OF SMELL 382 0.0000000 -0.3558539 0.0000000
2226 LYMPHOCYTE CHEMOTAXIS 62 0.0000024 -0.3460543 0.0005081
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 457 0.0000000 -0.3336786 0.0000000
2729 NATURAL KILLER CELL ACTIVATION 93 0.0000001 -0.3263409 0.0000399
5952 REGULATION OF NATURAL KILLER CELL MEDIATED IMMUNITY 52 0.0000485 -0.3256257 0.0048090 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Buccal diagnosis GOBP",xlab="S dist")

mgo_buc_iiq <- mitch_calc(x=m_buc_iiq, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_buc_iiq$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_buc_iiq$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Buccal inverse IQ up") %>% kable_paper("hover", full_width = F)
Buccal inverse IQ up
set setSize pANOVA s.dist p.adjustANOVA
6774 RIBOSOMAL SMALL SUBUNIT ASSEMBLY 18 0.0002879 0.4935782 0.0150411
4967 PROTEIN LOCALIZATION TO CENP A CONTAINING CHROMATIN 16 0.0009501 0.4771610 0.0386511
1582 G1 TO G0 TRANSITION 19 0.0012658 0.4271343 0.0480036
2347 MATURATION OF SSU RRNA 51 0.0000002 0.4174085 0.0000335
832 CHAPERONE COFACTOR DEPENDENT PROTEIN REFOLDING 32 0.0001314 0.3905000 0.0079130
2348 MATURATION OF SSU RRNA FROM TRICISTRONIC RRNA TRANSCRIPT SSU RRNA 5 8S RRNA LSU RRNA 34 0.0001413 0.3770818 0.0082450
2345 MATURATION OF LSU RRNA 27 0.0009767 0.3665484 0.0395107
6775 RIBOSOMAL SMALL SUBUNIT BIOGENESIS 101 0.0000000 0.3654324 0.0000001
4970 PROTEIN LOCALIZATION TO CHROMOSOME CENTROMERIC REGION 38 0.0002898 0.3397017 0.0150411
6798 RNA POLYMERASE II PREINITIATION COMPLEX ASSEMBLY 53 0.0000321 0.3301136 0.0023902
5831 REGULATION OF MEGAKARYOCYTE DIFFERENTIATION 35 0.0009433 0.3229531 0.0385842
6772 RIBOSOMAL LARGE SUBUNIT BIOGENESIS 70 0.0000033 0.3215225 0.0003242
995 CYTOCHROME COMPLEX ASSEMBLY 38 0.0006695 0.3188530 0.0303543
3629 NUCLEOSOME ORGANIZATION 119 0.0000000 0.3181025 0.0000004
1124 DE NOVO PROTEIN FOLDING 41 0.0004268 0.3179174 0.0202026
1008 CYTOPLASMIC TRANSLATION 141 0.0000000 0.3140084 0.0000000
4730 POSITIVE REGULATION OF TRANSCRIPTION ELONGATION BY RNA POLYMERASE II 48 0.0001719 0.3133919 0.0096568
6778 RIBOSOME ASSEMBLY 58 0.0000390 0.3122095 0.0028199
4179 POSITIVE REGULATION OF DNA TEMPLATED TRANSCRIPTION INITIATION 65 0.0000161 0.3092217 0.0013069
835 CHAPERONE MEDIATED PROTEIN FOLDING 74 0.0000048 0.3072907 0.0004424 
dn %>% kbl(caption="Buccal inverse IQ dn") %>% kable_paper("hover", full_width = F)
Buccal inverse IQ dn
set setSize pANOVA s.dist p.adjustANOVA
4537 POSITIVE REGULATION OF PEPTIDYL SERINE PHOSPHORYLATION OF STAT PROTEIN 17 0.0000003 -0.7164246 0.0000406
6882 SERINE PHOSPHORYLATION OF STAT PROTEIN 21 0.0000029 -0.5895116 0.0002949
6864 SENSORY PERCEPTION OF SMELL 382 0.0000000 -0.5162298 0.0000000
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 457 0.0000000 -0.4863088 0.0000000
6859 SENSORY PERCEPTION OF BITTER TASTE 44 0.0000000 -0.4855699 0.0000040
1093 DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION 471 0.0000000 -0.4726381 0.0000000
1080 DETECTION OF CHEMICAL STIMULUS INVOLVED IN SENSORY PERCEPTION OF TASTE 46 0.0000000 -0.4706580 0.0000051
4385 POSITIVE REGULATION OF LYMPHOCYTE CHEMOTAXIS 20 0.0003954 -0.4575811 0.0191999
5803 REGULATION OF LYMPHOCYTE CHEMOTAXIS 26 0.0003064 -0.4089329 0.0156876
557 CELLULAR GLUCURONIDATION 21 0.0011910 -0.4084972 0.0464442
1092 DETECTION OF STIMULUS 591 0.0000000 -0.4042355 0.0000000
4478 POSITIVE REGULATION OF NATURAL KILLER CELL MEDIATED IMMUNITY 33 0.0007879 -0.3376174 0.0336932
2226 LYMPHOCYTE CHEMOTAXIS 62 0.0000080 -0.3277770 0.0006933
6867 SENSORY PERCEPTION OF TASTE 66 0.0000060 -0.3221204 0.0005305
4388 POSITIVE REGULATION OF LYMPHOCYTE MIGRATION 40 0.0007785 -0.3070080 0.0334807
1442 ESTROGEN METABOLIC PROCESS 39 0.0012956 -0.2976500 0.0487535
6396 REGULATION OF T CELL MIGRATION 47 0.0011189 -0.2747110 0.0443217
5806 REGULATION OF LYMPHOCYTE MIGRATION 66 0.0002096 -0.2638232 0.0114295
6858 SENSORY PERCEPTION 883 0.0000000 -0.2617738 0.0000000
2077 KERATINIZATION 80 0.0003992 -0.2289359 0.0192226 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Buccal inverse IQ GOBP",xlab="S dist")

mgo_buc_ilan <- mitch_calc(x=m_buc_ilan, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_buc_ilan$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_buc_ilan$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Buccal inverse language up") %>% kable_paper("hover", full_width = F)
Buccal inverse language up
set setSize pANOVA s.dist p.adjustANOVA
1141 DIGESTIVE SYSTEM DEVELOPMENT 142 0.0006305 0.1661271 0.0473299
1906 IMPORT ACROSS PLASMA MEMBRANE 202 0.0005485 0.1410151 0.0424311
5932 REGULATION OF MUSCLE SYSTEM PROCESS 239 0.0003986 0.1329461 0.0337026
1352 EPIDERMIS DEVELOPMENT 375 0.0001189 0.1157043 0.0141985 
dn %>% kbl(caption="Buccal inverse language dn") %>% kable_paper("hover", full_width = F)
Buccal inverse language dn
set setSize pANOVA s.dist p.adjustANOVA
4556 POSITIVE REGULATION OF PLATELET ACTIVATION 9 0.0003047 -0.6950716 0.0286620
4537 POSITIVE REGULATION OF PEPTIDYL SERINE PHOSPHORYLATION OF STAT PROTEIN 17 0.0000024 -0.6608210 0.0008522
7354 T HELPER 2 CELL CYTOKINE PRODUCTION 13 0.0002242 -0.5910053 0.0234309
6882 SERINE PHOSPHORYLATION OF STAT PROTEIN 21 0.0000031 -0.5878135 0.0009711
536 CD4 POSITIVE ALPHA BETA T CELL CYTOKINE PRODUCTION 19 0.0000107 -0.5833816 0.0022292
5953 REGULATION OF NATURAL KILLER CELL PROLIFERATION 12 0.0006685 -0.5671411 0.0479158
2730 NATURAL KILLER CELL ACTIVATION INVOLVED IN IMMUNE RESPONSE 31 0.0000056 -0.4711475 0.0014653
2378 MELANOCYTE DIFFERENTIATION 24 0.0002712 -0.4293302 0.0268558
4388 POSITIVE REGULATION OF LYMPHOCYTE MIGRATION 40 0.0000253 -0.3847995 0.0040516
4478 POSITIVE REGULATION OF NATURAL KILLER CELL MEDIATED IMMUNITY 33 0.0003913 -0.3566056 0.0334686
4767 POSITIVE REGULATION OF T CELL MIGRATION 33 0.0004440 -0.3532448 0.0363238
2729 NATURAL KILLER CELL ACTIVATION 93 0.0000000 -0.3478647 0.0000071
416 B CELL PROLIFERATION 98 0.0000000 -0.3311985 0.0000136
5948 REGULATION OF NATURAL KILLER CELL ACTIVATION 42 0.0003727 -0.3173076 0.0322381
4323 POSITIVE REGULATION OF INTERLEUKIN 12 PRODUCTION 42 0.0005525 -0.3079653 0.0424311
4085 POSITIVE REGULATION OF CELL KILLING 75 0.0000136 -0.2905113 0.0025496
6864 SENSORY PERCEPTION OF SMELL 382 0.0000000 -0.2899215 0.0000000
5312 REGULATION OF B CELL PROLIFERATION 63 0.0000867 -0.2858750 0.0108774
5952 REGULATION OF NATURAL KILLER CELL MEDIATED IMMUNITY 52 0.0003652 -0.2856609 0.0319576
5806 REGULATION OF LYMPHOCYTE MIGRATION 66 0.0000915 -0.2783992 0.0112900 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Buccal inverse language GOBP",xlab="S dist")

mgo_buc_mot <- mitch_calc(x=m_buc_mot, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be
##             statistically significant.
up <- head(subset(mgo_buc_mot$enrichment_result,p.adjustANOVA<0.05 & s.dist>0),20)
dn <- head(subset(mgo_buc_mot$enrichment_result,p.adjustANOVA<0.05 & s.dist<0),20)
up %>% kbl(caption="Buccal motor up") %>% kable_paper("hover", full_width = F)
Buccal motor up
set setSize pANOVA s.dist p.adjustANOVA
3536 NEUTROPHIL ACTIVATION INVOLVED IN IMMUNE RESPONSE 20 0.0000686 0.5141067 0.0344391
7154 TELOMERE ORGANIZATION 178 0.0000964 0.1694079 0.0380139
4916 PROTEIN DNA COMPLEX ASSEMBLY 203 0.0000365 0.1680476 0.0249611
6804 RNA SPLICING VIA TRANSESTERIFICATION REACTIONS 286 0.0001456 0.1305016 0.0456682
875 CHROMOSOME ORGANIZATION 606 0.0000007 0.1179143 0.0007583
6802 RNA SPLICING 421 0.0000800 0.1120418 0.0358024
7472 VIRAL PROCESS 414 0.0001348 0.1093308 0.0456682
870 CHROMATIN REMODELING 579 0.0000122 0.1063244 0.0091782
5505 REGULATION OF DNA METABOLIC PROCESS 506 0.0001182 0.0999404 0.0423666
4918 PROTEIN DNA COMPLEX ORGANIZATION 796 0.0000046 0.0954330 0.0038823
2644 MRNA METABOLIC PROCESS 743 0.0000503 0.0873265 0.0291366
1171 DNA METABOLIC PROCESS 986 0.0000998 0.0731738 0.0380139
2030 INTRACELLULAR TRANSPORT 1513 0.0000404 0.0631064 0.0253242
4899 PROTEIN CONTAINING COMPLEX ASSEMBLY 1523 0.0001010 0.0595870 0.0380139
687 CELLULAR RESPONSE TO STRESS 1871 0.0000809 0.0549651 0.0358024
238 APOPTOTIC PROCESS 1856 0.0001407 0.0532805 0.0456682 
dn %>% kbl(caption="Buccal motor dn") %>% kable_paper("hover", full_width = F)
Buccal motor dn
set setSize pANOVA s.dist p.adjustANOVA
6864 SENSORY PERCEPTION OF SMELL 382 0.00e+00 -0.2265889 0.0000000
1093 DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION 471 0.00e+00 -0.2060857 0.0000000
6860 SENSORY PERCEPTION OF CHEMICAL STIMULUS 457 0.00e+00 -0.2034043 0.0000000
6381 REGULATION OF TUBE SIZE 142 5.84e-05 -0.1953360 0.0313750
1092 DETECTION OF STIMULUS 591 0.00e+00 -0.1683218 0.0000000
6858 SENSORY PERCEPTION 883 0.00e+00 -0.1105119 0.0000371
3455 NERVOUS SYSTEM PROCESS 1404 0.00e+00 -0.0871328 0.0000547
1764 G PROTEIN COUPLED RECEPTOR SIGNALING PATHWAY 1159 3.10e-06 -0.0811890 0.0029442 
b <- c(up$s.dist,dn$s.dist)
names(b) <- c(up$set,dn$set)
b <- b[order(b)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(b))))
names(b) <- substr(names(b), 0, 65)
barplot(abs(b),col=cols,horiz=TRUE,las=1,cex.names=0.7,main="Buccal motor GOBP",xlab="S dist")

Multimitch

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

gu_l <- list("ADOS"=gu_ados,"diagnosis"=gu_diag,"iIQ"=gu_iiq,"iLang"=gu_ilan,"motor"=gu_mot)
mm_gu <- mitch_import(x=gu_l, DEtype="limma", geneTable=gt )
## Note: Mean no. genes in input = 790658
## Note: no. genes in output = 22269
## Warning in mitch_import(x = gu_l, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
mres_gu <- mitch_calc(x=mm_gu, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
top <- head(subset(mres_gu$enrichment_result,p.adjustMANOVA<0.05),40)
top <- top[,c(1,4:8)]
rownames(top) <- top[,1]
top[,1]=NULL
cols <- colorRampPalette(c("blue", "white", "red"))(n = 25)
colnames(top) <- sub("s.","",colnames(top))
heatmap.2(as.matrix(top),margin=c(6, 24), trace="none",scale="none",col=cols,cexRow=0.7,cexCol=1)
mtext("Guthrie card multicontrast")

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

bl_l <- list("ADOS"=bl_ados,"diagnosis"=bl_diag,"iIQ"=bl_iiq,"iLang"=bl_ilan,"motor"=bl_mot)
mm_bl <- mitch_import(x=bl_l, DEtype="limma", geneTable=gt )
## Note: Mean no. genes in input = 802647
## Note: no. genes in output = 22285
## Warning in mitch_import(x = bl_l, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
mres_bl <- mitch_calc(x=mm_bl, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
top <- head(subset(mres_bl$enrichment_result,p.adjustMANOVA<0.05),40)
top <- top[,c(1,4:8)]
rownames(top) <- top[,1]
top[,1]=NULL
cols <- colorRampPalette(c("blue", "white", "red"))(n = 25)
colnames(top) <- sub("s.","",colnames(top))
heatmap.2(as.matrix(top),margin=c(6, 24), trace="none",scale="none",col=cols,cexRow=0.7,cexCol=1)
mtext("Blood at assessment multicontrast")

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

buc_l <- list("ADOS"=buc_ados,"diagnosis"=buc_diag,"iIQ"=buc_iiq,"iLang"=buc_ilan,"motor"=buc_mot)
mm_buc <- mitch_import(x=buc_l, DEtype="limma", geneTable=gt )
## Note: Mean no. genes in input = 801260
## Note: no. genes in output = 22286
## Warning in mitch_import(x = buc_l, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
##         output
mres_buc <- mitch_calc(x=mm_buc, genesets=gobp, minsetsize=5, priority="effect",cores=4)
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
top <- head(subset(mres_buc$enrichment_result,p.adjustMANOVA<0.05),40)
top <- top[,c(1,4:8)]
rownames(top) <- top[,1]
top[,1]=NULL
cols <- colorRampPalette(c("blue", "white", "red"))(n = 25)
colnames(top) <- sub("s.","",colnames(top))
heatmap.2(as.matrix(top),margin=c(6, 24), trace="none",scale="none",col=cols,cexRow=0.7,cexCol=1)
mtext("Buccal multicontrast")

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

In Guthrie card and blood at assessment I think the genome is undergoing a hypomethylation event, and some pathways are protected while others experience loss of methylation. The pathways that appear to have higher methylation might be an artifact of the competitive test. Methylation of those genes might be relatively stable, but compared to all other genes, it is relatively higher. The pathways with loss of methylation include the ones we have already seen, such as taste receptors. But there are other new ones like glucuronidation, which might be linked to ceellular detoxificaiton. There are also some inflammatory pathways appearing.

Session information

For reproducibility

sessionInfo()
## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## 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] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] gplots_3.1.3.1                                     
##  [2] HGNChelper_0.8.1                                   
##  [3] eulerr_7.0.1                                       
##  [4] kableExtra_1.4.0                                   
##  [5] data.table_1.15.2                                  
##  [6] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
##  [7] minfi_1.48.0                                       
##  [8] bumphunter_1.44.0                                  
##  [9] locfit_1.5-9.9                                     
## [10] iterators_1.0.14                                   
## [11] foreach_1.5.2                                      
## [12] Biostrings_2.70.1                                  
## [13] XVector_0.42.0                                     
## [14] SummarizedExperiment_1.32.0                        
## [15] Biobase_2.62.0                                     
## [16] MatrixGenerics_1.14.0                              
## [17] matrixStats_1.2.0                                  
## [18] GenomicRanges_1.54.1                               
## [19] GenomeInfoDb_1.38.5                                
## [20] IRanges_2.36.0                                     
## [21] S4Vectors_0.40.2                                   
## [22] BiocGenerics_0.48.1                                
## [23] mitch_1.15.2                                       
## [24] limma_3.58.1                                       
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.3.3             later_1.3.2              
##   [3] BiocIO_1.12.0             bitops_1.0-7             
##   [5] filelock_1.0.3            tibble_3.2.1             
##   [7] preprocessCore_1.64.0     XML_3.99-0.16.1          
##   [9] lifecycle_1.0.4           lattice_0.22-5           
##  [11] MASS_7.3-60.0.1           base64_2.0.1             
##  [13] scrime_1.3.5              magrittr_2.0.3           
##  [15] sass_0.4.9                rmarkdown_2.26           
##  [17] jquerylib_0.1.4           yaml_2.3.8               
##  [19] httpuv_1.6.14             askpass_1.2.0            
##  [21] doRNG_1.8.6               DBI_1.2.2                
##  [23] RColorBrewer_1.1-3        abind_1.4-5              
##  [25] zlibbioc_1.48.0           quadprog_1.5-8           
##  [27] rvest_1.0.4               purrr_1.0.2              
##  [29] RCurl_1.98-1.14           rappdirs_0.3.3           
##  [31] GenomeInfoDbData_1.2.11   genefilter_1.84.0        
##  [33] annotate_1.80.0           svglite_2.1.3            
##  [35] DelayedMatrixStats_1.24.0 codetools_0.2-19         
##  [37] DelayedArray_0.28.0       xml2_1.3.6               
##  [39] tidyselect_1.2.1          beanplot_1.3.1           
##  [41] BiocFileCache_2.10.1      illuminaio_0.44.0        
##  [43] webshot_0.5.5             GenomicAlignments_1.38.1 
##  [45] jsonlite_1.8.8            multtest_2.58.0          
##  [47] ellipsis_0.3.2            survival_3.5-8           
##  [49] systemfonts_1.0.6         tools_4.3.3              
##  [51] progress_1.2.3            Rcpp_1.0.12              
##  [53] glue_1.7.0                gridExtra_2.3            
##  [55] SparseArray_1.2.3         xfun_0.42                
##  [57] dplyr_1.1.4               HDF5Array_1.30.0         
##  [59] fastmap_1.1.1             GGally_2.2.1             
##  [61] rhdf5filters_1.14.1       fansi_1.0.6              
##  [63] openssl_2.1.1             caTools_1.18.2           
##  [65] digest_0.6.35             R6_2.5.1                 
##  [67] mime_0.12                 colorspace_2.1-0         
##  [69] gtools_3.9.5              biomaRt_2.58.0           
##  [71] RSQLite_2.3.5             utf8_1.2.4               
##  [73] tidyr_1.3.1               generics_0.1.3           
##  [75] rtracklayer_1.62.0        prettyunits_1.2.0        
##  [77] httr_1.4.7                htmlwidgets_1.6.4        
##  [79] S4Arrays_1.2.0            ggstats_0.5.1            
##  [81] pkgconfig_2.0.3           gtable_0.3.4             
##  [83] blob_1.2.4                siggenes_1.76.0          
##  [85] htmltools_0.5.7           echarts4r_0.4.5          
##  [87] scales_1.3.0              png_0.1-8                
##  [89] knitr_1.45                rstudioapi_0.15.0        
##  [91] tzdb_0.4.0                reshape2_1.4.4           
##  [93] rjson_0.2.21              nlme_3.1-164             
##  [95] curl_5.2.1                cachem_1.0.8             
##  [97] rhdf5_2.46.1              stringr_1.5.1            
##  [99] KernSmooth_2.23-22        AnnotationDbi_1.64.1     
## [101] restfulr_0.0.15           GEOquery_2.70.0          
## [103] pillar_1.9.0              grid_4.3.3               
## [105] reshape_0.8.9             vctrs_0.6.5              
## [107] promises_1.2.1            dbplyr_2.4.0             
## [109] xtable_1.8-4              beeswarm_0.4.0           
## [111] evaluate_0.23             readr_2.1.5              
## [113] GenomicFeatures_1.54.1    cli_3.6.2                
## [115] compiler_4.3.3            Rsamtools_2.18.0         
## [117] rlang_1.1.3               crayon_1.5.2             
## [119] rngtools_1.5.2            nor1mix_1.3-2            
## [121] mclust_6.1                plyr_1.8.9               
## [123] stringi_1.8.3             viridisLite_0.4.2        
## [125] BiocParallel_1.36.0       munsell_0.5.0            
## [127] Matrix_1.6-5              hms_1.1.3                
## [129] sparseMatrixStats_1.14.0  bit64_4.0.5              
## [131] ggplot2_3.5.0             Rhdf5lib_1.24.1          
## [133] KEGGREST_1.42.0           statmod_1.5.0            
## [135] shiny_1.8.0               highr_0.10               
## [137] memoise_2.0.1             bslib_0.6.1              
## [139] bit_4.0.5