Are Most used PAS dAPA

Last updated: 2020-03-19

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Knit directory: Comparative_APA/analysis/

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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.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	06ad3f9	brimittleman	2020-03-19	look at counts
html	d8ae388	brimittleman	2020-03-18	Build site.
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")

Version	Author	Date
d8ae388	brimittleman	2020-03-18

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.

Make sure this isnt driven by counts:

I can do this with raw counts.

#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" ))
PASMetaloc= PASMeta %>% select(PAS,loc)


HumanCounts=read.table("../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc", header = T)%>% separate(Geneid, into=c("disc", "PAS", "chr2", "start2", "end2", 'strand', 'geneid'), sep=":")  %>% select(PAS, contains("_N"))  %>% gather("ind", "count", -PAS) %>% group_by(PAS) %>% summarise(meanCount=mean(count))

HumanCounts_use30= HumanCounts %>% filter(PAS %in% HumanUsed30$PAS) %>% mutate(Set="Use30", species="Human") %>% inner_join(PASMetaloc, by="PAS")
HumanCounts_Dom= HumanCounts %>% filter(PAS %in% SiginHuman_Dom2_30$PAS)  %>% mutate(Set="Dominant", species="Human") %>% inner_join(PASMetaloc, by="PAS")

ChimpCounts=read.table("../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc", header = T)%>% separate(Geneid, into=c("disc", "PAS", "chr2", "start2", "end2", 'strand', 'geneid'), sep=":")  %>% select(PAS, contains("_N"))  %>% gather("ind", "count", -PAS) %>% group_by(PAS) %>% summarise(meanCount=mean(count))


ChimpCounts_use30= ChimpCounts %>% filter(PAS %in%ChimpUsed30$PAS) %>% mutate(Set="Use30", species="Chimp") %>% inner_join(PASMetaloc, by="PAS")
ChimpCounts_Dom= ChimpCounts %>% filter(PAS %in% SiginChimp_Dom2_30$PAS)  %>% mutate(Set="Dominant", species="Chimp") %>% inner_join(PASMetaloc, by="PAS")


AllCountsDF= HumanCounts_use30 %>% bind_rows(HumanCounts_Dom) %>% bind_rows(ChimpCounts_use30) %>% bind_rows(ChimpCounts_Dom)

ggplot(AllCountsDF, aes(y=log10(meanCount), x=Set, fill=Set)) +geom_boxplot() +facet_grid(~species) +scale_fill_brewer(palette = "Dark2",name="") + theme(legend.position = "none") + labs(title="Counts for Dominant PAS at 30% vs Usage at 30%", y="log10(Mean Counts)")

Version	Author	Date
d8ae388	brimittleman	2020-03-18

ggplot(AllCountsDF, aes(y=log10(meanCount), by=Set, fill=Set, x=loc)) +geom_boxplot() +facet_grid(~species) +scale_fill_brewer(palette = "Dark2",name="") + theme(legend.position = "none") + labs(title="Counts for Dominant PAS at 30% vs Usage at 30%", y="log10(Mean Counts)")

Version	Author	Date
d8ae388	brimittleman	2020-03-18

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")

Version	Author	Date
d8ae388	brimittleman	2020-03-18

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")

Version	Author	Date
d8ae388	brimittleman	2020-03-18

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