Last updated: 2020-04-23

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DiffTop2SecondDom.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/ExploredAPA_DF.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/signalsites_doublefilter.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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Rmd c1bc496 brimittleman 2020-04-23 add interaction density, dapa and e and order proble
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Rmd 2461fb7 brimittleman 2020-04-16 new dom integration 
library(workflowr)
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library(tidyverse)
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I will ask if there if dominance and DE are related. First I can ask if genes with dominant PAS are enriched in the DE genes.

PAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)
MetaCol=colnames(PAS)

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DE= read.table("../data/DiffExpression/DEtested_allres.txt",header=F, stringsAsFactors = F,col.names = c('Gene_stable_ID', 'logFC' ,'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B')) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% rename("gene"=Gene.name) %>% mutate(DE=ifelse(adj.P.Val<=.05, "Yes","No")) %>% select(gene,DE)
DE_yes= DE %>% filter(DE=="Yes")

Domiance

#9
HumanDom9=read.table("../data/DomDefGreaterX/Human_.9_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human9") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom9=read.table("../data/DomDefGreaterX/Chimp_.9_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp9") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))

#8 
HumanDom8=read.table("../data/DomDefGreaterX/Human_.8_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human8")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom8=read.table("../data/DomDefGreaterX/Chimp_.8_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp8") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))

#7 
HumanDom7=read.table("../data/DomDefGreaterX/Human_.7_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human7")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom7=read.table("../data/DomDefGreaterX/Chimp_.7_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp7")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))


#6 
HumanDom6=read.table("../data/DomDefGreaterX/Human_.6_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human6") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom6=read.table("../data/DomDefGreaterX/Chimp_.6_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp6") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))

#5  
HumanDom5=read.table("../data/DomDefGreaterX/Human_.5_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human5") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom5=read.table("../data/DomDefGreaterX/Chimp_.5_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp5")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))

#4 
HumanDom4=read.table("../data/DomDefGreaterX/Human_.4_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human4")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom4=read.table("../data/DomDefGreaterX/Chimp_.4_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp4")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))

#3 
HumanDom3=read.table("../data/DomDefGreaterX/Human_.3_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human3") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom3=read.table("../data/DomDefGreaterX/Chimp_.3_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp3")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))

#2
HumanDom2=read.table("../data/DomDefGreaterX/Human_.2_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human2")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom2=read.table("../data/DomDefGreaterX/Chimp_.2_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp2")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))

#1
HumanDom1=read.table("../data/DomDefGreaterX/Human_.1_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Human1")%>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))
ChimpDom1=read.table("../data/DomDefGreaterX/Chimp_.1_dominantPAS.txt", col.names = MetaCol,stringsAsFactors = F) %>% mutate(set="Chimp1") %>% mutate(DE=ifelse(gene %in% DE_yes$gene,"Yes","No"))

#all  
HumanDomAll= HumanDom1 %>% bind_rows(HumanDom2) %>% bind_rows(HumanDom3) %>% bind_rows(HumanDom4) %>% bind_rows(HumanDom5) %>% bind_rows(HumanDom6) %>% bind_rows(HumanDom7) %>% bind_rows(HumanDom8) %>% bind_rows(HumanDom9) 
ChimpDomAll= ChimpDom1 %>% bind_rows(ChimpDom2) %>% bind_rows(ChimpDom3) %>% bind_rows(ChimpDom4) %>% bind_rows(ChimpDom5) %>% bind_rows(ChimpDom6) %>% bind_rows(ChimpDom7) %>% bind_rows(ChimpDom8) %>% bind_rows(ChimpDom9) 
ChimpSet=c('Chimp1','Chimp2', 'Chimp3', 'Chimp4', 'Chimp5', 'Chimp6', 'Chimp7', 'Chimp8','Chimp9')
EnrichChimp=c()
PvalueChimp=c()
for (i in ChimpSet){
  x=nrow(ChimpDomAll %>% filter(set==i, DE=="Yes"))
  m=nrow(DE_yes)
  n=nrow(DE) - nrow(DE_yes)
  k=nrow(ChimpDomAll %>% filter(set==i))
  N=nrow(DE)
  PvalueChimp=c(PvalueChimp, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichChimp=c(EnrichChimp, enrich)
}

PvalueChimp
[1] 1.0000000 1.0000000 1.0000000 0.9999842 0.9999501 0.9971507 0.9994521
[8] 0.9839957 0.5079290
EnrichChimp
[1] 0.8849697 0.8877147 0.8880516 0.9057939 0.8959653 0.9108136 0.8719019
[8] 0.8855119 0.9896978
HumanSet=c('Human1','Human2', 'Human3', 'Human4', 'Human5', 'Human6', 'Human7', 'Human8','Human9')
EnrichHuman=c()
PvalueHuman=c()
for (i in HumanSet){
  x=nrow(HumanDomAll %>% filter(set==i, DE=="Yes"))
  m=nrow(DE_yes)
  n=nrow(DE) - nrow(DE_yes)
  k=nrow(HumanDomAll %>% filter(set==i))
  N=nrow(DE)
  PvalueHuman=c(PvalueHuman, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichHuman=c(EnrichHuman, enrich)
}

PvalueHuman
[1] 1.0000000 1.0000000 0.9999997 0.9999064 0.9992779 0.9991470 0.9853263
[8] 0.6526253 0.6747532
EnrichHuman
[1] 0.8885286 0.8749491 0.8850791 0.8975969 0.8974753 0.8785672 0.8903590
[8] 0.9674743 0.9054783

No enrichment for these. The real question is if genes with different dominant PAS are DE. This requires chosing how to call different dominant.

Same vs different dominant:

Are genes with different dominant at the cutoff .4 cutoff enriched for DE:

FourRes=read.table("../data/DomStructure_4/InclusiveDominantPASat4.txt", header = T,stringsAsFactors = F)
FourRes_diff= FourRes %>% filter(Set=="Different")
FourRes_same= FourRes %>% filter(Set=="Same")
x=length(intersect(FourRes_diff$gene,DE_yes$gene))
m=nrow(DE_yes)
n=nrow(DE) - nrow(DE_yes)
k=nrow(FourRes %>% filter(Set=="Different"))
N=nrow(DE)
phyper(x,m,n,k,lower.tail=F)
[1] 0.09585894
(x/k)/(m/N)
[1] 1.117149
x=length(intersect(FourRes_same$gene,DE_yes$gene))
m=nrow(DE_yes)
n=nrow(DE) - nrow(DE_yes)
k=nrow(FourRes %>% filter(Set=="Same"))
N=nrow(DE)
phyper(x,m,n,k,lower.tail=F)
[1] 0.9999999
(x/k)/(m/N)
[1] 0.883703

This is conditioned on the gene having a dominant PAS.

do this based on a set tested both

All4= FourRes %>% select(gene,Set) %>% inner_join(DE, by="gene")

x=nrow(All4 %>% filter(Set=="Different", DE=="Yes"))
m=nrow(All4 %>% filter( DE=="Yes"))
n=nrow(All4 %>% filter( DE=="No"))
k=nrow(All4 %>% filter(Set=="Different"))
N=nrow(All4)
phyper(x,m,n,k,lower.tail=F)
[1] 0.0009822532
(x/k)/(m/N)
[1] 1.305745

I am not sure what the set should be.

x=nrow(All4 %>% filter(Set=="Same", DE=="Yes"))
m=nrow(All4 %>% filter(DE=="Yes"))
n=nrow(All4 %>% filter(DE=="No"))
k=nrow(All4 %>% filter(Set=="Same"))
N=nrow(All4)
phyper(x,m,n,k,lower.tail=F)
[1] 0.9982958
(x/k)/(m/N)
[1] 0.9792784

Robust to different cutoffs

HumanRes=read.table("../data/DomDefGreaterX/Human_AllGenes_DiffTop.txt", col.names = c("Human_PAS", "gene","Human_DiffDom"),stringsAsFactors = F)

ChimpRes=read.table("../data/DomDefGreaterX/Chimp_AllGenes_DiffTop.txt", col.names = c("Chimp_PAS", "gene","Chimp_DiffDom"),stringsAsFactors = F)

BothRes=HumanRes %>% inner_join(ChimpRes,by="gene")

BothRes_10=BothRes %>% filter(Chimp_DiffDom >=0.1 | Human_DiffDom>=0.1) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=10) 
BothRes_20=BothRes %>% filter(Chimp_DiffDom >=0.2 | Human_DiffDom>=0.2) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=20)
BothRes_30=BothRes %>% filter(Chimp_DiffDom >=0.3 | Human_DiffDom>=0.3) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=30)
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
BothRes_50=BothRes %>% filter(Chimp_DiffDom >=0.5 | Human_DiffDom>=0.5) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=50)
BothRes_60=BothRes %>% filter(Chimp_DiffDom >=0.6 | Human_DiffDom>=0.6) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=60)
BothRes_70=BothRes %>% filter(Chimp_DiffDom >=0.7 | Human_DiffDom>=0.7) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=70)
BothRes_80=BothRes %>% filter(Chimp_DiffDom >=0.8 | Human_DiffDom>=0.8) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=80)
BothRes_90=BothRes %>% filter(Chimp_DiffDom >=0.9 | Human_DiffDom>=0.9) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=90)

BothResAll=BothRes_10 %>% bind_rows(BothRes_20) %>% bind_rows(BothRes_30) %>% bind_rows(BothRes_40) %>% bind_rows(BothRes_50) %>% bind_rows(BothRes_60) %>% bind_rows(BothRes_70) %>% bind_rows(BothRes_80) %>% bind_rows(BothRes_90)
Pval=c()
Enrich=c()
set=c(10,20,30,40,50,60,70,80,90)
expected=c()
actual=c()

All4= BothResAll  %>% select(gene,cut,Set) %>% inner_join(DE, by="gene")


for (i in set){
  x=nrow(All4 %>% filter(cut==i, Set=="Different", DE=="Yes"))
  actual=c(actual, x)
  m=nrow(All4 %>% filter(cut==i, DE=="Yes"))
  n=nrow(All4 %>% filter(cut==i, DE=="No"))
  k=nrow(All4 %>% filter(cut==i, Set=="Different"))
  N=nrow(All4 %>% filter(cut==i))
  val=phyper(x-1,m,n,k,lower.tail=F)
  Pval= c(Pval, val)
  en=(x/k)/(m/N)
  Enrich=c(Enrich, en)
  #ex=which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
  ex=k*(m/N)
  expected=c(expected,ex)
}

ResDF=as.data.frame(cbind(set,Pval,Enrich, actual, expected))
ResDF$set=as.factor(ResDF$set)
ResDF$Pval=as.numeric(as.character(ResDF$Pval))
ResDF$Enrich=as.numeric(as.character(ResDF$Enrich))

diffP=ggplot(ResDF,aes(x=set, y=-log10(Pval),fill=set)) + geom_bar(stat="identity") +labs(title="Enrichment pvalues for DE and different dominant \n condition on tested in both")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")
diffE=ggplot(ResDF,aes(x=set, y=Enrich,fill=set)) + geom_bar(stat="identity") + geom_hline(yintercept = 1)+labs(title="Enrichment for DE and different dominant \n condition on tested in both")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")
PvalSame=c()
EnrichSame=c()
expectedSame=c()
actualSame=c()



for (i in set){
  x=nrow(All4 %>% filter(cut==i, Set=="Same", DE=="Yes"))
  actualSame=c(actualSame, x)
  m=nrow(All4 %>% filter(cut==i, DE=="Yes"))
  n=nrow(All4 %>% filter(cut==i, DE=="No"))
  k=nrow(All4 %>% filter(cut==i, Set=="Same"))
  N=nrow(All4 %>% filter(cut==i))
  val=phyper(x-1,m,n,k,lower.tail=F)
  PvalSame= c(PvalSame, val)
  en=(x/k)/(m/N)
  EnrichSame=c(EnrichSame, en)
  #ex=which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
  ex=k*(m/N)
  expectedSame=c(expectedSame,ex)
}

ResDFSame=as.data.frame(cbind(set,PvalSame,EnrichSame, actualSame,expectedSame))
ResDFSame$set=as.factor(ResDFSame$set)
ResDFSame$PvalSame=as.numeric(as.character(ResDFSame$PvalSame))
ResDFSame$EnrichSame=as.numeric(as.character(ResDFSame$EnrichSame))

Samep=ggplot(ResDFSame,aes(x=set, y=-log10(PvalSame),fill=set)) + geom_bar(stat="identity") +labs(title="Enrichment pvalues for DE and same dominant \n condition on tested in both")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")
SameE=ggplot(ResDFSame,aes(x=set, y=EnrichSame,fill=set)) + geom_bar(stat="identity") + geom_hline(yintercept = 1)+labs(title="Enrichment for DE and same dominant \n condition on tested in both")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")
ResDF
  set        Pval   Enrich actual   expected
1  10 0.025369960 1.070079    430 401.839506
2  20 0.003500182 1.146763    221 192.716425
3  30 0.002404823 1.227587    116  94.494294
4  40 0.001704216 1.305745     76  58.204341
5  50 0.004173416 1.346146     51  37.885924
6  60 0.002304926 1.461459     36  24.632911
7  70 0.023483381 1.491590     18  12.067662
8  80 0.228627744 1.247994     11   8.814145
9  90 0.384878104 1.191860      6   5.034146
ResDFSame
  set  PvalSame EnrichSame actualSame expectedSame
1  10 0.9785161  0.9816444       1506   1534.16049
2  20 0.9973903  0.9779256       1253   1281.28357
3  30 0.9984228  0.9794500       1025   1046.50571
4  40 0.9990177  0.9792784        841    858.79566
5  50 0.9977739  0.9808864        673    686.11408
6  60 0.9990053  0.9786078        520    531.36709
7  70 0.9905316  0.9838326        361    366.93234
8  80 0.8790630  0.9902498        222    224.18586
9  90 0.8120858  0.9880709         80     80.96585

Look at propotion of DE genes

PropDE=as.data.frame(cbind(set, OverlapDE=actual, DE=rep(nrow(DE_yes),9))) %>% mutate(Prop=OverlapDE/DE)
PropDE$set=as.factor(PropDE$set)
ggplot(PropDE, aes(x=set,y=Prop,fill=set)) + geom_bar(stat="identity")+ labs(title="Proportion of DE genes with different dominant PAS", y="Proportion of DE genes",x="Dominance Cuttoff")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")+geom_text(aes(label=OverlapDE), position=position_dodge(width=0.9), vjust=1.5)

Version Author Date
3790efa brimittleman 2020-04-23
53a7570 brimittleman 2020-04-22
fbc6599 brimittleman 2020-04-21

plot grid:

plot_grid(diffE, SameE, diffP, Samep)

Version Author Date
add6b2a brimittleman 2020-04-23
53a7570 brimittleman 2020-04-22 
ggdotchart(ResDF,y="Enrich",x= "set", color="set",add = "segments", rotate = TRUE, dot.size = 10,  label = round(ResDF$Enrich,2), font.label = list(color = "black", size = 10, vjust = 0.5),ggtheme = theme_pubr(),sort="d", legend="none", palette="RdYlBu", title="Enrichment for DE and dAPA Different Dominant") + geom_hline(yintercept = 1)

ggdotchart(ResDFSame,y="EnrichSame",x= "set", color="set",add = "segments", rotate = TRUE, dot.size = 10,  label = round(ResDFSame$EnrichSame,2), font.label = list(color = "black", size = 10, vjust = 0.5),ggtheme = theme_pubr(),sort="d", legend="none", palette="RdYlBu", title="Enrichment for DE and dAPA Same Dominant") + geom_hline(yintercept = 1)

Plot together:

ResDFfix=ResDF %>% mutate(type="Different")
ResDFSamefix=ResDFSame %>% mutate(type="Same") 

colnames(ResDFSamefix)=colnames(ResDFfix)
ResDFfix_both=ResDFfix %>% bind_rows(ResDFSamefix)
dotchart=ggdotchart(ResDFfix_both,y="Enrich",x= "set", color="set",add = "segments", rotate = TRUE, dot.size = 10,  label = round(ResDFfix_both$Enrich,2), font.label = list(color = "black", size = 10, vjust = 0.5),ggtheme = theme_pubr(), legend="none", palette="RdYlBu", title="Enrichment for DE by Domianance") + geom_hline(yintercept = 1) + facet_grid(~type) + labs(x="Dominance Cutoff", y="Enrichement")

dotchart

Sort if off!

ggsave("../output/dEandDomEnrich.png",dotchart, width=12)
Saving 12 x 5 in image

Split into with dAPA and without

Add info about DE to All4.

diffIsoGenes= read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",stringsAsFactors = F, header = T)

All4DiffIso= All4 %>% mutate(dAPA=ifelse(gene %in% diffIsoGenes$gene, "Yes", "No"))

PvalSameapa=c()
EnrichSameAPA=c()
ExpectedSameAPA=c()
actualSameapa=c()
#same dapa
for (i in set){
  x=nrow(All4DiffIso %>% filter(cut==i, Set=="Same", DE=="Yes", dAPA=="Yes"))
  actualSameapa=c(actualSameapa, x)
  m=nrow(All4DiffIso %>% filter(cut==i, DE=="Yes", dAPA=="Yes"))
  n=nrow(All4DiffIso %>% filter(cut==i, DE=="No", dAPA=="Yes"))
  k=nrow(All4DiffIso %>% filter(cut==i, Set=="Same", dAPA=="Yes"))
  N=nrow(All4DiffIso %>% filter(cut==i,dAPA=="Yes"))
  val=phyper(x-1,m,n,k,lower.tail=F)
  PvalSameapa= c(PvalSameapa, val)
  en=(x/k)/(m/N)
  EnrichSameAPA=c(EnrichSameAPA, en)
  #ex=which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
  ex=k*(m/N)
  ExpectedSameAPA=c(ExpectedSameAPA,ex)
}
actualSameapa
[1] 231 207 172 136 103  80  46  26   7
ExpectedSameAPA
[1] 234.67700 217.20264 187.27941 146.76411 110.98324  84.50000  48.48485
[8]  25.65333   6.50000
EnrichSameAPA
[1] 0.9843316 0.9530271 0.9184138 0.9266571 0.9280681 0.9467456 0.9487500
[8] 1.0135135 1.0769231
PvalSameapa
[1] 0.6908226 0.9271034 0.9951886 0.9867290 0.9779845 0.9302170 0.8874905
[8] 0.5307203 0.5000000
set
[1] 10 20 30 40 50 60 70 80 90
PvalSameNo=c()
EnrichSameNo=c()
ExpectedSameNo=c()
actualSameNo=c()
#no dapa
for (i in set){
  x=nrow(All4DiffIso %>% filter(cut==i, Set=="Same", DE=="Yes", dAPA=="No"))
  actualSameNo=c(actualSameNo, x)
  m=nrow(All4DiffIso %>% filter(cut==i, DE=="Yes", dAPA=="No"))
  n=nrow(All4DiffIso %>% filter(cut==i, DE=="No", dAPA=="No"))
  k=nrow(All4DiffIso %>% filter(cut==i, Set=="Same", dAPA=="No"))
  N=nrow(All4DiffIso %>% filter(cut==i,dAPA=="Yes"))
  val=phyper(x-1,m,n,k,lower.tail=F)
  PvalSameNo= c(PvalSameNo, val)
  en=(x/k)/(m/N)
  EnrichSameNo=c(EnrichSameNo, en)
  #ex=which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
  ex=k*(m/N)
  ExpectedSameNo=c(ExpectedSameNo,ex)
}
actualSameNo
[1] 1275 1046  853  705  570  440  315  196   73
ExpectedSameNo
[1] 4443.0698 3500.0771 2981.5500 2764.2177 2472.6536 2256.8966 2083.2955
[8] 1392.9067  550.5417
EnrichSameNo
[1] 0.2869638 0.2988506 0.2860928 0.2550450 0.2305216 0.1949580 0.1512027
[8] 0.1407129 0.1325967
PvalSameNo
[1] 0.78224457 0.71829130 0.08718982 0.54027531 1.00000000 1.00000000
[7] 1.00000000 1.00000000 1.00000000

Ploth this:

ResDFSameAPA=as.data.frame(cbind(set,pval=PvalSameapa,enrich=EnrichSameAPA, actual=actualSameapa,expected=ExpectedSameAPA)) %>% mutate(dAPA="Yes")
ResDFSameNo=as.data.frame(cbind(set,pval=PvalSameNo,enrich=EnrichSameNo, actual=actualSameNo,expected=ExpectedSameNo))%>% mutate(dAPA="No")


ResDFSameApaBoth= ResDFSameAPA %>% bind_rows(ResDFSameNo)

ResDFSameApaBoth$set=factor(ResDFSameApaBoth$set, levels=c(10,20,30,40,50,60,70,80,90))
ResDFSameApaBoth$dAPA=as.factor(ResDFSameApaBoth$dAPA)

ggdotchart(ResDFSameApaBoth,y="enrich",x= "set",  color="set",group="dAPA",shape="dAPA", add = "segments", rotate = TRUE, dot.size = 10, label = round(ResDFSameApaBoth$enrich,2), font.label = list(color = "black", size = 10, vjust = 0.5),ggtheme = theme_pubr(), palette="RdYlBu",legend="bottom", title="Enrichment for DE by in genes with Same Dominant") + geom_hline(yintercept = 1) + labs(x="Dominance Cutoff", y="Enrichement")

#+ facet_grid(~dAPA)
#rotate = TRUE

the order is wrong because the ggdotchart code only allows descending or ascending. not none. submitted a comment on github.


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

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       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] RColorBrewer_1.1-2 ggpubr_0.2         magrittr_1.5      
 [4] cowplot_0.9.4      forcats_0.3.0      stringr_1.3.1     
 [7] dplyr_0.8.0.1      purrr_0.3.2        readr_1.3.1       
[10] tidyr_0.8.3        tibble_2.1.1       ggplot2_3.1.1     
[13] tidyverse_1.2.1    workflowr_1.6.0   

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 reshape2_1.4.3   haven_1.1.2      lattice_0.20-38 
 [5] colorspace_1.3-2 generics_0.0.2   htmltools_0.3.6  yaml_2.2.0      
 [9] rlang_0.4.0      later_0.7.5      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] broom_0.5.1      Rcpp_1.0.2       promises_1.0.1   scales_1.0.0    
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4    
[41] whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4 
[45] assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10 
[49] R6_2.3.0         nlme_3.1-137     git2r_0.26.1     compiler_3.5.1