Last updated: 2020-03-18
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Knit directory: Comparative_APA/analysis/
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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.
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")
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
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 |
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 |
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 |
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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 |
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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:
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