Are Most used PAS dAPA

Last updated: 2020-03-18

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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File	Version	Author	Date	Message
Rmd	565dd6a	brimittleman	2020-03-18	show dom enrich not factor of usage
html	86238eb	brimittleman	2020-03-16	Build site.
Rmd	164c237	brimittleman	2020-03-16	ask about anno bias

I want to see if the most used PAS are also the differentially used.

library(tidyverse)

── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(workflowr)

This is workflowr version 1.6.0
Run ?workflowr for help getting started

library(cowplot)


Attaching package: 'cowplot'

The following object is masked from 'package:ggplot2':

    ggsave

Caveat- ties.

Dominant PAS

allPAS= read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T) 
ChimpPASwMean =allPAS %>% dplyr::select(-Human)
HumanPASwMean =allPAS %>% dplyr::select(-Chimp)


Chimp_Dom= ChimpPASwMean %>%
  group_by(gene) %>%
  arrange(desc(Chimp)) %>% 
  slice(1) %>% 
  group_by(gene) %>% 
  mutate(npas=n()) %>% 
  dplyr::select(gene,loc,PAS,Chimp) %>% 
  rename(ChimpLoc=loc, ChimpPAS=PAS)


Chimp_Dom2= ChimpPASwMean %>%
  group_by(gene) %>%
  top_n(1,Chimp) %>% 
  mutate(nPer=n()) 
nrow(Chimp_Dom2%>% filter(nPer>1) )

[1] 198

Human_Dom= HumanPASwMean %>%
  group_by(gene) %>%
  arrange(desc(Human)) %>% 
  slice(1) %>% 
  group_by(gene) %>% 
  mutate(npas=n()) %>% 
  dplyr::select(gene,loc,PAS,Human) %>% 
  rename(HumanLoc=loc, HumanPAS=PAS)

Human_Dom2= HumanPASwMean %>%
  group_by(gene) %>%
  top_n(1,Human) %>% 
  mutate(nPer=n()) 

nrow(Human_Dom2 %>% filter(nPer>1) )

[1] 161

198 genes with a tie in chimp and 161 with a tie in human. Picks top for both for analysis.

No PAS usage cutoff for dominant

BothDom= Chimp_Dom %>% inner_join(Human_Dom,by="gene")

SameDom= BothDom %>% filter(ChimpPAS==HumanPAS,HumanLoc!="008559")
nrow(SameDom)

[1] 7718

SameDom_g= SameDom %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)
ggplot(SameDom_g, aes(x=Location, by=Species, fill=Species))+ geom_bar(stat="count",position = "Dodge") + labs(x="Location", y="Number of Genes", title="Dominant PAS for genes with matching by species")

Version	Author	Date
86238eb	brimittleman	2020-03-16

ggplot(SameDom, aes(x=HumanLoc))+ geom_bar(stat="count") + labs(x="Location", y="Number of Genes", title="Dominant PAS for genes with matching by species")

Version	Author	Date
86238eb	brimittleman	2020-03-16

DiffDom=BothDom %>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559")
nrow(DiffDom)

[1] 2092

DiffDom_g= DiffDom %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plotnones=ggplot(DiffDom_g,aes(by=Species, x=Location, fill=Species))+ geom_histogram(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant PAS") + scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Diff dominant different locations:

DiffDomDiffLoc=BothDom %>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559", ChimpLoc!=HumanLoc)
nrow(DiffDomDiffLoc)

[1] 1095

DiffDomDiffLoc_g= DiffDomDiffLoc %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plotnone=ggplot(DiffDomDiffLoc_g,aes(by=Species, x=Location, fill=Species))+ geom_bar(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Dominant PAS in different locations") + scale_fill_brewer(palette = "Dark2")

Implement filters

I want to filter these and see if these distribution changes. First I will say the most used has to be over 50% in both species.

BothDom_50= BothDom %>% filter(Chimp>=.5, Human>.5)
nrow(BothDom_50)

[1] 3526

BothDom_40= BothDom %>% filter(Chimp>=.4, Human>.4)
nrow(BothDom_40)

[1] 4555

BothDom_30= BothDom %>% filter(Chimp>=.3, Human>.3)
nrow(BothDom_30)

[1] 5879

BothDom_20= BothDom %>% filter(Chimp>=.2, Human>.2)
nrow(BothDom_20)

[1] 7584

BothDom_10= BothDom %>% filter(Chimp>=.1, Human>.1)
nrow(BothDom_10)

[1] 9324

cutoffs=c(".5",'.4','.3','.2','.1', "0")
nPAS=c(nrow(BothDom_50),nrow(BothDom_40),nrow(BothDom_30),nrow(BothDom_20),nrow(BothDom_10), nrow(BothDom))
nPAS

[1] 3526 4555 5879 7584 9324 9810

Different Dominant, different Location

BothDom_50_diff=BothDom_50%>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559", ChimpLoc!=HumanLoc)
nrow(BothDom_50_diff)

[1] 33

BothDom_50_diff_g= BothDom_50_diff %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plot50=ggplot(BothDom_50_diff_g,aes(by=Species, x=Location, fill=Species))+ geom_bar(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Dominant PAS in different locations (50%)") + scale_fill_brewer(palette = "Dark2")

BothDom_40_diff=BothDom_40%>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559", ChimpLoc!=HumanLoc)
nrow(BothDom_40_diff)

[1] 85

BothDom_40_diff_g= BothDom_40_diff %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plot40=ggplot(BothDom_40_diff_g,aes(by=Species, x=Location, fill=Species))+ geom_bar(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Dominant PAS in different locations (40%)") + scale_fill_brewer(palette = "Dark2")

BothDom_30_diff=BothDom_30%>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559", ChimpLoc!=HumanLoc)
nrow(BothDom_30_diff)

[1] 211

BothDom_30_diff_g= BothDom_30_diff %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plot30=ggplot(BothDom_30_diff_g,aes(by=Species, x=Location, fill=Species))+ geom_bar(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Dominant PAS in different locations (30%)") + scale_fill_brewer(palette = "Dark2")

BothDom_20_diff=BothDom_20%>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559", ChimpLoc!=HumanLoc)
nrow(BothDom_20_diff)

[1] 481

BothDom_20_diff_g= BothDom_20_diff %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plot20=ggplot(BothDom_20_diff_g,aes(by=Species, x=Location, fill=Species))+ geom_bar(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Dominant PAS in different locations (20%)") + scale_fill_brewer(palette = "Dark2")

BothDom_10_diff=BothDom_10%>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559", ChimpLoc!=HumanLoc)
nrow(BothDom_10_diff)

[1] 931

BothDom_10_diff_g= BothDom_10_diff %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plot10=ggplot(BothDom_10_diff_g,aes(by=Species, x=Location, fill=Species))+ geom_bar(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Dominant PAS in different locations (10%)") + scale_fill_brewer(palette = "Dark2")

plot_grid(plot50, plot40, plot30, plot20, plot10,plotnone)

Version	Author	Date
86238eb	brimittleman	2020-03-16

Filters in same loc ok

BothDom_50_same=BothDom_50%>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559")
nrow(BothDom_50_same)

[1] 74

BothDom_50_same_g= BothDom_50_same %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plot50s=ggplot(BothDom_50_same_g,aes(by=Species, x=Location, fill=Species))+ geom_bar(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant (50%)") + scale_fill_brewer(palette = "Dark2")

BothDom_40_same=BothDom_40%>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559")
nrow(BothDom_40_same)

[1] 223

BothDom_40_same_g= BothDom_40_same %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plot40s=ggplot(BothDom_40_same_g,aes(by=Species, x=Location, fill=Species))+ geom_bar(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant (40%)") + scale_fill_brewer(palette = "Dark2")

BothDom_30_same=BothDom_30%>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559")
nrow(BothDom_30_same)

[1] 480

BothDom_30_same_g= BothDom_30_same %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plot30s=ggplot(BothDom_30_same_g,aes(by=Species, x=Location, fill=Species))+ geom_bar(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant (30%)") + scale_fill_brewer(palette = "Dark2")

BothDom_20_same=BothDom_20%>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559")
nrow(BothDom_20_same)

[1] 1011

BothDom_20_same_g= BothDom_20_same %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plot20s=ggplot(BothDom_20_same_g,aes(by=Species, x=Location, fill=Species))+ geom_bar(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant (20%)") + scale_fill_brewer(palette = "Dark2")

BothDom_10_same=BothDom_10%>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559")
nrow(BothDom_10_same)

[1] 1810

BothDom_10_same_g= BothDom_10_same %>% select(gene, ChimpLoc, HumanLoc) %>% gather("Species", "Location", -gene)


plot10s=ggplot(BothDom_10_same_g,aes(by=Species, x=Location, fill=Species))+ geom_bar(stat="count",position = "dodge") + labs(x="Location", y="Number of Genes", title="Different Dominant (10%)") + scale_fill_brewer(palette = "Dark2")

plot_grid(plot50s, plot40s, plot30s, plot20s, plot10s,plotnones)

Version	Author	Date
86238eb	brimittleman	2020-03-16

Plot numbers:

cutoffs=c("0.5",'0.4','0.3','0.2','0.1', "0")
#nPAS=c(nrow(BothDom_50),nrow(BothDom_40),nrow(BothDom_30),nrow(BothDom_20),nrow(BothDom_10), nrow(BothDom))
nPASDiff=c(nrow(BothDom_50_diff),nrow(BothDom_40_diff),nrow(BothDom_30_diff),nrow(BothDom_20_diff),nrow(BothDom_10_diff), nrow(DiffDomDiffLoc))
nPASSameLoc=c(nrow(BothDom_50_same),nrow(BothDom_40_same),nrow(BothDom_30_same),nrow(BothDom_20_same),nrow(BothDom_10_same), nrow(DiffDom))

NumberDF= as.data.frame(cbind(cutoffs, Genes=nPAS, DifferentPASandLoc=nPASDiff, DifferentPAS=nPASSameLoc)) %>% gather("Set", "count",-cutoffs )

Warning: attributes are not identical across measure variables;
they will be dropped

NumberDF$count=as.numeric(NumberDF$count)

ggplot(NumberDF,aes(x=cutoffs, y=count, by=Set, fill=Set)) + geom_bar(stat="identity",position = "dodge" ) +geom_text(stat='identity', aes(label=count), vjust=0,position = position_dodge(width = 1)) + labs(x="Cutoff for PAS to be dominant (Both Species)", y="Number of genes")+ scale_fill_brewer(palette = "Dark2", labels=c("Different PAS Same Location", "Different PAS and Location", "All Genes"),name="") + theme(legend.position = "top")

Version	Author	Date
86238eb	brimittleman	2020-03-16

Compare to differencially used

PASMeta=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% select(PAS, gene, chr, start,end,loc)

DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt",header = T, stringsAsFactors = F) %>% inner_join(PASMeta, by=c("chr",'start','end',"gene"))

DiffIso_sig= DiffIso %>% filter(SigPAU2=="Yes") 


DiffIsowDom=DiffIso %>% mutate(ChimpDom=ifelse(PAS %in% Chimp_Dom2$PAS, "Yes", "No"),HumanDom=ifelse(PAS %in% Human_Dom2$PAS, "Yes", "No") ) 


ggplot(DiffIsowDom,aes(x=loc,fill=ChimpDom)) + geom_bar(stat = "count") + facet_grid(~SigPAU2) +labs(x="", y="Number of PAS", title="Differentially used PAS colored by Dominant Status in Chimp")+ scale_fill_brewer(palette = "Dark2",name="Is PAS Dominant in Chimp") + theme(legend.position = "top")

Version	Author	Date
86238eb	brimittleman	2020-03-16

ggplot(DiffIsowDom,aes(x=loc,fill=HumanDom)) + geom_bar(stat = "count") + facet_grid(~SigPAU2) +labs(x="", y="Number of PAS", title="Differentially used PAS colored by Dominant Status in Human")+ scale_fill_brewer(palette = "Dark2",name="Is PAS Dominant in Chimp") + theme(legend.position = "top")

Version	Author	Date
86238eb	brimittleman	2020-03-16

Of the 3076 how many are in the dominant set.

I need to use the set that includes ties here:

SiginChimp_Dom2= Chimp_Dom2 %>% ungroup %>% select(PAS) %>% inner_join(DiffIso_sig, by="PAS")

Warning: Column `PAS` joining factor and character vector, coercing into
character vector

nrow(SiginChimp_Dom2)

[1] 1549

SiginHuman_Dom2= Human_Dom2 %>% ungroup %>% select(PAS) %>% inner_join(DiffIso_sig, by="PAS")

Warning: Column `PAS` joining factor and character vector, coercing into
character vector

nrow(SiginHuman_Dom2)

[1] 1514

Enrichemnt:

EnrichChimp=c()
PvalueChimp=c()

for (i in c('cds', 'end', 'intron', 'utr3', 'utr5')){
  x=nrow(DiffIsowDom %>% filter(ChimpDom=="Yes", SigPAU2=="Yes", loc==i))
  m=nrow(DiffIsowDom %>% filter(SigPAU2=="Yes", loc==i))
  n=nrow(DiffIsowDom %>% filter(SigPAU2=="No", loc==i))
  k=nrow(DiffIsowDom %>% filter(loc==i,ChimpDom=="Yes"))
  N=nrow(DiffIsowDom %>% filter(loc==i))
  PvalueChimp=c(PvalueChimp, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichChimp=c(EnrichChimp, round(enrich,2))
}


EnrichHuman=c()
PvalueHuman=c()

for (i in c('cds', 'end', 'intron', 'utr3', 'utr5')){
  x=nrow(DiffIsowDom %>% filter(HumanDom=="Yes", SigPAU2=="Yes", loc==i))
  m=nrow(DiffIsowDom %>% filter(SigPAU2=="Yes", loc==i))
  n=nrow(DiffIsowDom %>% filter(SigPAU2=="No", loc==i))
  k=nrow(DiffIsowDom %>% filter(loc==i,HumanDom=="Yes"))
  N=nrow(DiffIsowDom %>% filter(loc==i))
  PvalueHuman=c(PvalueHuman, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichHuman=c(EnrichHuman, round(enrich,2))
}

locations=c('cds', 'end', 'intron', 'utr3', 'utr5')


EnrichDF=as.data.frame(cbind(locations, EnrichChimp,EnrichHuman ))
colnames(EnrichDF)=c("GenicLoc", "Chimp", "Human")

EnrichDF

  GenicLoc Chimp Human
1      cds  2.64   2.5
2      end  2.51  2.92
3   intron  2.62  3.41
4     utr3  1.95  1.83
5     utr5  2.79   2.4

Compare this result to a set of PAS by usage:

For this I will use dominant at 30%, I will then use, used at 30%

SiginChimp_Dom2_30= Chimp_Dom2 %>% ungroup%>% filter(Chimp>=.3)  %>% select(PAS) %>% inner_join(DiffIso_sig, by="PAS")

Warning: Column `PAS` joining factor and character vector, coercing into
character vector

nrow(SiginChimp_Dom2)

[1] 1549

SiginHuman_Dom2_30= Human_Dom2 %>% ungroup %>% filter(Human>=.3) %>% select(PAS) %>% inner_join(DiffIso_sig, by="PAS")

Warning: Column `PAS` joining factor and character vector, coercing into
character vector

nrow(SiginHuman_Dom2)

[1] 1514

HumanUsed30= allPAS %>% filter(Human>=.3)
nrow(HumanUsed30)

[1] 6896

ChimpUsed30= allPAS %>% filter(Chimp>=.3)
nrow(ChimpUsed30)

[1] 7456

allPAS_anno= allPAS %>% select(PAS,loc) %>% mutate(SigDPAU=ifelse(PAS %in% DiffIso_sig$PAS, "Yes","No"), HumanUse30=ifelse(PAS %in% HumanUsed30$PAS, "Yes", "No"), ChimpUse30=ifelse(PAS %in% ChimpUsed30$PAS, "Yes","No"), DomHuman=ifelse(PAS %in% SiginHuman_Dom2_30$PAS, "Yes", "No"), DomChimp=ifelse(PAS %in%SiginChimp_Dom2_30$PAS, "Yes", "No" ))

Enrichment of dominant in diff used human:

x=nrow(allPAS_anno %>% filter(DomHuman=="Yes", SigDPAU=="Yes"))
m=nrow(allPAS_anno %>% filter(SigDPAU=="Yes"))
n=nrow(allPAS_anno %>% filter(SigDPAU=="No"))
k=nrow(allPAS_anno %>% filter(DomHuman=="Yes"))
N=nrow(allPAS_anno)
phyper(x,m,n,k,lower.tail=F)

[1] 0

enrich=(x/k)/(m/N)
enrich

[1] 13.75748

Compare to human just used at 30%

x=nrow(allPAS_anno %>% filter(HumanUse30=="Yes", SigDPAU=="Yes"))
m=nrow(allPAS_anno %>% filter(SigDPAU=="Yes"))
n=nrow(allPAS_anno %>% filter(SigDPAU=="No"))
k=nrow(allPAS_anno %>% filter(HumanUse30=="Yes"))
N=nrow(allPAS_anno)
phyper(x,m,n,k,lower.tail=F)

[1] 4.329756e-180

enrich=(x/k)/(m/N)
enrich

[1] 2.248358

Enrichment of dominant in diff used chimp:

x=nrow(allPAS_anno %>% filter(DomChimp=="Yes", SigDPAU=="Yes"))
m=nrow(allPAS_anno %>% filter(SigDPAU=="Yes"))
n=nrow(allPAS_anno %>% filter(SigDPAU=="No"))
k=nrow(allPAS_anno %>% filter(DomChimp=="Yes"))
N=nrow(allPAS_anno)
phyper(x,m,n,k,lower.tail=F)

[1] 0

enrich=(x/k)/(m/N)
enrich

[1] 13.75748

Enrichment of used at 30%

x=nrow(allPAS_anno %>% filter(ChimpUse30=="Yes", SigDPAU=="Yes"))
m=nrow(allPAS_anno %>% filter(SigDPAU=="Yes"))
n=nrow(allPAS_anno %>% filter(SigDPAU=="No"))
k=nrow(allPAS_anno %>% filter(ChimpUse30=="Yes"))
N=nrow(allPAS_anno)
phyper(x,m,n,k,lower.tail=F)

[1] 4.169482e-274

enrich=(x/k)/(m/N)
enrich

[1] 2.485424

At the 30% cutoff in the domiant PAS has 13.8X enrichement and 30% used has 2.49X enrichment. Is this based on the dominant at 30% always being in the other set?

Do by location:

EnrichChimpDom=c()
PvalueChimpDom=c()

for (i in c('cds', 'end', 'intron', 'utr3', 'utr5')){
  x=nrow(allPAS_anno %>% filter(DomChimp=="Yes", SigDPAU=="Yes", loc==i))
  m=nrow(allPAS_anno %>% filter(SigDPAU=="Yes", loc==i))
  n=nrow(allPAS_anno %>% filter(SigDPAU=="No", loc==i))
  k=nrow(allPAS_anno %>% filter(loc==i,DomChimp=="Yes"))
  N=nrow(allPAS_anno %>% filter(loc==i))
  PvalueChimpDom=c(PvalueChimpDom, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichChimpDom=c(EnrichChimpDom, round(enrich,2))
}

EnrichChimp30=c()
PvalueChimp30=c()
for (i in c('cds', 'end', 'intron', 'utr3', 'utr5')){
  x=nrow(allPAS_anno %>% filter(ChimpUse30=="Yes", SigDPAU=="Yes", loc==i))
  m=nrow(allPAS_anno %>% filter(SigDPAU=="Yes", loc==i))
  n=nrow(allPAS_anno %>% filter(SigDPAU=="No", loc==i))
  k=nrow(allPAS_anno %>% filter(loc==i,ChimpUse30=="Yes"))
  N=nrow(allPAS_anno %>% filter(loc==i))
  PvalueChimp30=c(PvalueChimp30, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichChimp30=c(EnrichChimp30, round(enrich,2))
}

EnrichHumanDom=c()
PvalueHumanDom=c()

for (i in c('cds', 'end', 'intron', 'utr3', 'utr5')){
  x=nrow(allPAS_anno %>% filter(DomHuman=="Yes", SigDPAU=="Yes", loc==i))
  m=nrow(allPAS_anno %>% filter(SigDPAU=="Yes", loc==i))
  n=nrow(allPAS_anno %>% filter(SigDPAU=="No", loc==i))
  k=nrow(allPAS_anno %>% filter(loc==i,DomHuman=="Yes"))
  N=nrow(allPAS_anno %>% filter(loc==i))
  PvalueHumanDom=c(PvalueHumanDom, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichHumanDom=c(EnrichHumanDom, round(enrich,2))
}

EnrichHuman30=c()
PvalueHuman30=c()
for (i in c('cds', 'end', 'intron', 'utr3', 'utr5')){
  x=nrow(allPAS_anno %>% filter(HumanUse30=="Yes", SigDPAU=="Yes", loc==i))
  m=nrow(allPAS_anno %>% filter(SigDPAU=="Yes", loc==i))
  n=nrow(allPAS_anno %>% filter(SigDPAU=="No", loc==i))
  k=nrow(allPAS_anno %>% filter(loc==i,HumanUse30=="Yes"))
  N=nrow(allPAS_anno %>% filter(loc==i))
  PvalueHuman30=c(PvalueHuman30, phyper(x,m,n,k,lower.tail=F))
  enrich=(x/k)/(m/N)
  EnrichHuman30=c(EnrichHuman30, round(enrich,2))
}

EnrichDataDFHuman=as.data.frame(cbind(loc=c('cds', 'end', 'intron', 'utr3', 'utr5'), Used30= EnrichHuman30,Dominant=EnrichHumanDom)) %>% gather("Set", "Enrichment", -loc)%>% mutate(species="Human")

Warning: attributes are not identical across measure variables;
they will be dropped

EnrichDataDFChimp=as.data.frame(cbind(loc=c('cds', 'end', 'intron', 'utr3', 'utr5'), Used30= EnrichChimp30,Dominant=EnrichChimpDom)) %>% gather("Set", "Enrichment", -loc) %>% mutate(species="Chimp")

Warning: attributes are not identical across measure variables;
they will be dropped

EnrichDataDFBoth= EnrichDataDFHuman %>% bind_rows(EnrichDataDFChimp)


EnrichDataDFBoth$Enrichment=as.numeric(EnrichDataDFBoth$Enrichment)
EnrichDataDFBoth$Set=as.factor(EnrichDataDFBoth$Set)




ggplot(EnrichDataDFBoth, aes(x=loc, y=Enrichment, by=Set, fill=Set)) + geom_bar(stat="identity", position="dodge") + geom_hline(yintercept = 1) + facet_grid(~species)+ scale_fill_brewer(palette = "Dark2",name="") + theme(legend.position = "bottom") + labs(x="Genic Location", title="Dominant PAS are more enriched for dPAS \nthan all PAS used at same level")

test opposite

EnrichChimpDomOpp=c()
for (i in c('cds', 'end', 'intron', 'utr3', 'utr5')){
  x=nrow(allPAS_anno %>% filter(DomChimp=="Yes", SigDPAU=="Yes", loc==i))
  m=nrow(allPAS_anno %>% filter(DomChimp=="Yes", loc==i))
  n=nrow(allPAS_anno %>% filter(DomChimp=="No", loc==i))
  k=nrow(allPAS_anno %>% filter(loc==i,SigDPAU=="Yes"))
  N=nrow(allPAS_anno %>% filter(loc==i))
  enrich=(x/k)/(m/N)
  EnrichChimpDomOpp=c(EnrichChimpDomOpp, round(enrich,2))
}

EnrichChimpDomOpp

[1] 22.54 15.80 15.40 10.98 13.32

EnrichChimpDom

[1] 22.54 15.80 15.40 10.98 13.32

ok test. its the same.
##diff dominance and discovery
DiffDom

SamDomLoc=SameDom %>% ungroup() %>% select(ChimpPAS) %>% rename("PAS"=ChimpPAS) %>% inner_join(allPAS, by="PAS")
ggplot(SamDomLoc, aes(x=disc, fill=disc)) +geom_bar(stat="count") + labs(title="Same dominant")

DiffDomChimp= DiffDom %>% ungroup() %>% select(ChimpPAS) %>% rename("PAS"=ChimpPAS) %>% inner_join(allPAS, by="PAS")

ggplot(DiffDomChimp, aes(x=disc, fill=disc)) +geom_bar(stat="count") + labs(title="Different dominant, Chimp PAS")

DiffDomHuman= DiffDom %>% ungroup() %>% select(HumanPAS) %>% rename("PAS"=HumanPAS) %>% inner_join(allPAS, by="PAS")

ggplot(DiffDomHuman, aes(x=disc, fill=disc)) +geom_bar(stat="count") +  labs(title="Different dominant, Human PAS")

#DiffDomDiffLoc

DiffDomDiffLocChimp= DiffDomDiffLoc %>% ungroup() %>% select(ChimpPAS) %>% rename("PAS"=ChimpPAS) %>% inner_join(allPAS, by="PAS")

ggplot(DiffDomDiffLocChimp, aes(x=disc, fill=disc)) +geom_bar(stat="count") +  labs(title="Different dominant, different Loc, Chimp PAS")

DiffDomDiffLocHuman= DiffDomDiffLoc %>% ungroup() %>% select(HumanPAS) %>% rename("PAS"=HumanPAS) %>% inner_join(allPAS, by="PAS")

ggplot(DiffDomDiffLocHuman, aes(x=disc, fill=disc)) +geom_bar(stat="count") +  labs(title="Different dominant, different Loc, Human PAS")

Does this go away if we say has to be 50%

DiffDomChimp50= BothDom_50_same %>% ungroup() %>% select(ChimpPAS) %>% rename("PAS"=ChimpPAS) %>% inner_join(allPAS, by="PAS")

ggplot(DiffDomChimp50, aes(x=disc, fill=disc)) +geom_bar(stat="count") + labs(title="Different dominant, Chimp PAS")

DiffDomHuman50= BothDom_50_same %>% ungroup() %>% select(HumanPAS) %>% rename("PAS"=HumanPAS) %>% inner_join(allPAS, by="PAS")

ggplot(DiffDomHuman50, aes(x=disc, fill=disc)) +geom_bar(stat="count") +  labs(title="Different dominant, Human PAS")

Problems:

snorna (need to be removed)
mispriming?
why not disovered in the right species

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] cowplot_0.9.4   workflowr_1.6.0 forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   reshape2_1.4.3     haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         rlang_0.4.0       
[10] later_0.7.5        pillar_1.3.1       glue_1.3.0        
[13] withr_2.1.2        RColorBrewer_1.1-2 modelr_0.1.2      
[16] readxl_1.1.0       plyr_1.8.4         munsell_0.5.0     
[19] gtable_0.2.0       cellranger_1.1.0   rvest_0.3.2       
[22] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] httpuv_1.4.5       broom_0.5.1        Rcpp_1.0.2        
[28] promises_1.0.1     scales_1.0.0       backports_1.1.2   
[31] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[34] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[37] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[40] magrittr_1.5       lazyeval_0.2.1     crayon_1.3.4      
[43] whisker_0.3-2      pkgconfig_2.0.2    xml2_1.2.0        
[46] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[49] httr_1.3.1         rstudioapi_0.10    R6_2.3.0          
[52] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1