Last updated: 2020-05-31

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Knit directory: Comparative_APA/analysis/

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In my initial exploration of dAPA PAS I saw they are enriched for negative phylop scores. I will explore this trend further here. I will see if intron location explain the differences.

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(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
The following object is masked from 'package:tidyr':

    extract
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
DiffUsage=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F)

PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, gene, loc)

DiffUsagePAS=DiffUsage %>% inner_join(PASMeta, by=c("gene","chr", "start", "end"))
phylores=read.table("../data/PhyloP/PAS_phyloP.txt", col.names = c("chr","start","end", "phyloP"), stringsAsFactors = F) %>% drop_na()
NucReswPhy=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(phylores, by=c("chr","start","end"))
ggplot(NucReswPhy,aes(y=phyloP, x=SigPAU2,fill=SigPAU2)) + geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Dark2", name="Signficant")

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ggplot(NucReswPhy,aes(x=phyloP, by=SigPAU2, fill=SigPAU2)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2", name="Signficant PAS") + labs(title="Mean PhyloP scores for tested PAS") + annotate("text",label="Wilcoxan, p=1.4e -5",x=6,y=.75)

Version Author Date
6d8725a brimittleman 2020-04-10
c2a0778 brimittleman 2020-04-06
7b73ce7 brimittleman 2020-03-04

The significant PAS have on average lower phyloP scores.

Positive scores — Measure conservation, which is slower evolution than expected, at sites that are predicted to be conserved. Negative scores — Measure acceleration, which is faster evolution than expected, at sites that are predicted to be fast-evolving.

I can look at those with negative values:

x=nrow(NucReswPhy %>% filter(SigPAU2=="Yes", phyloP<0))
m= nrow(NucReswPhy %>% filter(phyloP<0))
n=nrow(NucReswPhy %>% filter(phyloP>=0))
k=nrow(NucReswPhy %>% filter(SigPAU2=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 559
#actual:
x
[1] 604
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.01326693
b=nrow(NucReswPhy %>% filter(SigPAU2=="Yes", phyloP<0))
n=nrow(NucReswPhy %>% filter(SigPAU2=="Yes"))
B=nrow(NucReswPhy %>% filter(phyloP<0))
N=nrow(NucReswPhy)

(b/n)/(B/N)
[1] 1.079162

This means these regions are more likely to be fast evolving.

Look at this by location: (is it driven by region)

NucReswPhy_meta= NucReswPhy %>% inner_join(PASMeta, by=c("chr", "start", "end", "gene"))

ggplot(NucReswPhy_meta,aes(x=phyloP, by=SigPAU2, fill=SigPAU2)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2") + facet_grid(~loc)

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NucReswPhy_meta_group=NucReswPhy_meta %>% group_by(loc,SigPAU2) %>% summarise(n=n(),meanPhylo=mean(phyloP))
NucReswPhy_meta_group
# A tibble: 10 x 4
# Groups:   loc [5]
   loc    SigPAU2     n meanPhylo
   <chr>  <chr>   <int>     <dbl>
 1 cds    No       7048    2.15  
 2 cds    Yes       262    2.14  
 3 end    No       3574    0.372 
 4 end    Yes       172    0.324 
 5 intron No      13484    0.0622
 6 intron Yes       544    0.0833
 7 utr3   No      15408    1.04  
 8 utr3   Yes      1280    0.904 
 9 utr5   No       1158    0.300 
10 utr5   Yes        81    0.261

Control sequence:upstream

(upstream 200)

Look at the 200 basepairs upstream of each PAS as a control.

metaStrand=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% select(chr, start,end, strandFix, PAS)

NucReswPhy_upstream=NucReswPhy %>% inner_join(metaStrand,by=c("chr", "start", "end")) %>% mutate(newStart=ifelse(strandFix=="+", start - 200, end), newEnd=ifelse(strandFix=="+", start, end +200))

NucReswPhy_upstreambed=NucReswPhy_upstream %>% select(chr, newStart, newEnd, PAS, Human, strandFix)

write.table(NucReswPhy_upstreambed,"../data/PhyloP/PAS_200upregions.bed",col.names = F,row.names = F,quote = F,sep="\t")
python extractPhylopReg200up.py

Phylo200UpContron=read.table("../data/PhyloP/PAS_phyloP_200upstream.txt",stringsAsFactors = F, col.names = c("chr", "start","end", "PAS","UpstreamControl_Phylop")) 

NucReswPhyandC=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(phylores, by=c("chr", "start","end")) %>% inner_join(metaStrand,by=c("chr", "start", "end"))%>% inner_join(Phylo200UpContron, by="PAS")  %>% drop_na()

NucReswPhyandCsmall=NucReswPhyandC %>% select(PAS,SigPAU2,phyloP ,UpstreamControl_Phylop ) %>% gather("set", "Phylop", -PAS, -SigPAU2)

wilcox.test(NucReswPhyandC$phyloP, NucReswPhyandC$UpstreamControl_Phylop, alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  NucReswPhyandC$phyloP and NucReswPhyandC$UpstreamControl_Phylop
W = 1088300000, p-value < 2.2e-16
alternative hypothesis: true location shift is greater than 0

Actual are greater than region upstream

ggplot(NucReswPhyandCsmall, aes(x=SigPAU2, by=set, fill=set, y=Phylop)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2",labels=c('PAS', 'Control') ) + stat_compare_means()

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NucReswPhyandCsmall_noc= NucReswPhyandCsmall %>% filter(set!="UpstreamControl_Phylop")

ggplot(NucReswPhyandCsmall_noc, aes(x=SigPAU2, fill=SigPAU2, y=Phylop )) + geom_boxplot() + stat_compare_means() + scale_fill_brewer(palette = "OrRd") + labs(title="Significant PAS",x="")+ scale_x_discrete(labels=c("Not Significant", "Signficant"))+ theme(legend.position = "none",text= element_text(size=16))

Version Author Date
6d8725a brimittleman 2020-04-10
c2a0778 brimittleman 2020-04-06
fa2369c brimittleman 2020-03-18
7b73ce7 brimittleman 2020-03-04

Significant are lower than not significant:

NucReswPhyandCsmall_nocYES= NucReswPhyandCsmall_noc %>% filter(SigPAU2=="Yes")
NucReswPhyandCsmall_nocNO= NucReswPhyandCsmall_noc %>% filter(SigPAU2=="No")

wilcox.test(NucReswPhyandCsmall_nocYES$Phylop, NucReswPhyandCsmall_nocNO$Phylop, alternative ="less")

    Wilcoxon rank sum test with continuity correction

data:  NucReswPhyandCsmall_nocYES$Phylop and NucReswPhyandCsmall_nocNO$Phylop
W = 46695000, p-value = 0.0733
alternative hypothesis: true location shift is less than 0

Significant have lower scores.

Negative enrichment

Number of negative in each set?

neg=NucReswPhyandCsmall %>% filter(Phylop <0) %>% group_by(set, SigPAU2) %>% summarise(nNeg=n())

pos=NucReswPhyandCsmall %>% filter(Phylop >0) %>% group_by(set, SigPAU2) %>% summarise(nPos=n())

both=neg %>% inner_join(pos,by= c('set', 'SigPAU2')) %>% mutate(PropNeg=nNeg/(nNeg+nPos))

both
# A tibble: 4 x 5
# Groups:   set [2]
  set                    SigPAU2  nNeg  nPos PropNeg
  <chr>                  <chr>   <int> <int>   <dbl>
1 phyloP                 No       9685 30967   0.238
2 phyloP                 Yes       604  1735   0.258
3 UpstreamControl_Phylop No      13229 27423   0.325
4 UpstreamControl_Phylop Yes       817  1522   0.349

More negative overall in actual. Is there an enrichment for negative in the control set?

x=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes", UpstreamControl_Phylop<0))
m= nrow(NucReswPhyandC %>% filter(UpstreamControl_Phylop<0))
n=nrow(NucReswPhyandC %>% filter(UpstreamControl_Phylop>=0))
k=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 764
#actual:
x
[1] 817
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.00806816
b=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes", UpstreamControl_Phylop<0))
n=nrow(NucReswPhyandC %>% filter(SigPAU2=="Yes"))
B=nrow(NucReswPhyandC %>% filter(UpstreamControl_Phylop<0))
N=nrow(NucReswPhyandC)

(b/n)/(B/N)
[1] 1.069096

Stronger enrichement in the for negative in the real results compared to contol. 1.07x in control 1.11x in actual.

Maybe I need to move the control further up.

Is this a better control? Dont want to go into an exon? What about downstream?

Control sequence (downstream 200):

NucReswPhy_downstream=NucReswPhy %>% inner_join(metaStrand,by=c("chr", "start", "end")) %>% mutate(newStart=ifelse(strandFix=="+",  end, start-200), newEnd=ifelse(strandFix=="+", end+200, start))

NucReswPhy_downstreambed=NucReswPhy_downstream %>% select(chr, newStart, newEnd, PAS, Human, strandFix)

write.table(NucReswPhy_downstreambed,"../data/PhyloP/PAS_200downpregions.bed",col.names = F,row.names = F,quote = F,sep="\t")
python extractPhylopReg200down.py

Phylo200downCont=read.table("../data/PhyloP/PAS_phyloP_200downstream.txt",stringsAsFactors = F, col.names = c("chr", "start","end", "PAS","DownstreamControl_Phylop")) 

NucReswPhyandbothC=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(phylores, by=c("chr", "start","end")) %>% inner_join(metaStrand,by=c("chr", "start", "end"))%>% inner_join(Phylo200UpContron, by="PAS")  %>% drop_na() %>%  inner_join(Phylo200downCont, by="PAS")  %>% drop_na()

NucReswPhyandCbothsmall=NucReswPhyandbothC %>% select(PAS,SigPAU2,phyloP ,UpstreamControl_Phylop,DownstreamControl_Phylop ) %>% gather("set", "Phylop", -PAS, -SigPAU2) %>% drop_na()


#difference in controls? 
wilcox.test(NucReswPhyandbothC$DownstreamControl_Phylop, NucReswPhyandbothC$UpstreamControl_Phylop,alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  NucReswPhyandbothC$DownstreamControl_Phylop and NucReswPhyandbothC$UpstreamControl_Phylop
W = 23487000, p-value = 1
alternative hypothesis: true location shift is greater than 0
levels=NucReswPhyandCbothsmall$set %>% unique()
NucReswPhyandCbothsmall$set= factor(NucReswPhyandCbothsmall$set, levels = c("UpstreamControl_Phylop", "phyloP", "DownstreamControl_Phylop"))

my_comparisons <- list( c("DownsreamControl_Pylop", "phylopP"), c("DownsreamControl_Pylop", "UpstreamControl_Phylop"), c("phylopP", "UpstreamControl_Phylop") )
ggplot(NucReswPhyandCbothsmall, aes(x=set, by=set, fill=set, y=Phylop)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2",labels=c("Upstream Control", "PAS", "Downstream Control") ) + stat_compare_means(ref.group = "phyloP",paired = FALSE,label = "p.signif")  + labs(x="", title="PAS conserved compared to surrounding regions" ) + scale_x_discrete( labels=c("Upstream Control", "PAS", "Downstream Control"))+ theme(legend.position = "none",text= element_text(size=16))

Version Author Date
6d8725a brimittleman 2020-04-10
c2a0778 brimittleman 2020-04-06
fa2369c brimittleman 2020-03-18
0bbaafe brimittleman 2020-03-05

Same here. The actual region looks more conserved.

ggplot(NucReswPhyandCbothsmall, aes(x=SigPAU2, by=set, fill=set, y=Phylop)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2",labels=c("Downstream Control", "PAS", "Upstream Control") )

Version Author Date
6d8725a brimittleman 2020-04-10
c2a0778 brimittleman 2020-04-06
fa2369c brimittleman 2020-03-18

Extend

2 more blocks up and downstream to add to plot.

Extend downstream:

NucReswPhy_downstream2=NucReswPhy_downstream %>% mutate(newStart2=ifelse(strandFix=="+",  newEnd, newStart-200), newEnd2=ifelse(strandFix=="+", newEnd+200, newStart))

NucReswPhy_downstream2bed=NucReswPhy_downstream2 %>% select(chr, newStart2, newEnd2, PAS, Human, strandFix)

write.table(NucReswPhy_downstream2bed,"../data/PhyloP/PAS_200downpregions2.bed",col.names = F,row.names = F,quote = F,sep="\t")

NucReswPhy_downstream3=NucReswPhy_downstream2 %>% mutate(newStart3=ifelse(strandFix=="+",  newEnd2, newStart2-200), newEnd3=ifelse(strandFix=="+", newEnd2+200, newStart2))

NucReswPhy_downstream3bed=NucReswPhy_downstream3 %>% select(chr, newStart3, newEnd3, PAS, Human, strandFix)

write.table(NucReswPhy_downstream3bed,"../data/PhyloP/PAS_200downpregions3.bed",col.names = F,row.names = F,quote = F,sep="\t")

Extend upstream:

NucReswPhy_upstream2=NucReswPhy_upstream %>%  mutate(newStart2=ifelse(strandFix=="+", newStart - 200, newEnd), newEnd2=ifelse(strandFix=="+", newStart, newEnd +200))

NucReswPhy_upstreambed2=NucReswPhy_upstream2 %>% select(chr, newStart2, newEnd2, PAS, Human, strandFix)

write.table(NucReswPhy_upstreambed2,"../data/PhyloP/PAS_200upregions2.bed",col.names = F,row.names = F,quote = F,sep="\t")

NucReswPhy_upstream3=NucReswPhy_upstream2 %>%  mutate(newStart3=ifelse(strandFix=="+", newStart2 - 200, newEnd2), newEnd3=ifelse(strandFix=="+", newStart2, newEnd2 +200))

NucReswPhy_upstreambed3=NucReswPhy_upstream3 %>% select(chr, newStart3, newEnd3, PAS, Human, strandFix)

write.table(NucReswPhy_upstreambed3,"../data/PhyloP/PAS_200upregions3.bed",col.names = F,row.names = F,quote = F,sep="\t")

Run phylop for each of these:

python extractPhylopGeneral.py ../data/PhyloP/PAS_200downpregions2.bed ../data/PhyloP/PAS_phyloP_200downstream2.txt

python extractPhylopGeneral.py ../data/PhyloP/PAS_200downpregions3.bed ../data/PhyloP/PAS_phyloP_200downstream3.txt

python extractPhylopGeneral.py ../data/PhyloP/PAS_200upregions2.bed ../data/PhyloP/PAS_phyloP_200upstream2.txt

python extractPhylopGeneral.py ../data/PhyloP/PAS_200upregions3.bed ../data/PhyloP/PAS_phyloP_200upstream3.txt

ResUpdown=NucReswPhyandbothC %>% select(PAS,SigPAU2,phyloP ,UpstreamControl_Phylop,DownstreamControl_Phylop ) 


Down2=read.table("../data/PhyloP/PAS_phyloP_200downstream2.txt",col.names = c("chr", "start", "end", "PAS", "Down2"),stringsAsFactors = F) %>% select(PAS, Down2)%>% drop_na()
Down3=read.table("../data/PhyloP/PAS_phyloP_200downstream3.txt",col.names = c("chr", "start", "end", "PAS", "Down3"),stringsAsFactors = F) %>% select(PAS, Down3)%>% drop_na()

Up2=read.table("../data/PhyloP/PAS_phyloP_200upstream2.txt",col.names = c("chr", "start", "end", "PAS", "Up2"),stringsAsFactors = F) %>% select(PAS, Up2)%>% drop_na()
Up3=read.table("../data/PhyloP/PAS_phyloP_200upstream3.txt",col.names = c("chr", "start", "end", "PAS", "Up3"),stringsAsFactors = F) %>% select(PAS, Up3)%>% drop_na()

ResUpdownAll= ResUpdown %>% inner_join(Down2, by="PAS")%>% inner_join(Down3, by="PAS") %>% inner_join(Up2, by="PAS") %>% inner_join(Up3, by="PAS")

ResUpdownAll_gather= ResUpdownAll %>% gather("Set", "PhyloP", -PAS, -SigPAU2)

ResUpdownAll_gather$Set=factor(ResUpdownAll_gather$Set, levels=c("Up3", "Up2","UpstreamControl_Phylop", "phyloP","DownstreamControl_Phylop", "Down2", "Down3" ))
ggplot(ResUpdownAll_gather, aes(x=Set, by=Set, fill=Set, y=PhyloP)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + theme(legend.position = "none")

Version Author Date
6d8725a brimittleman 2020-04-10
c2a0778 brimittleman 2020-04-06
fa2369c brimittleman 2020-03-18 
ggplot(ResUpdownAll_gather, aes(x=Set, by=Set, fill=Set, y=PhyloP)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + facet_grid(~SigPAU2)+ scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + theme(legend.position = "none")

Version Author Date
6d8725a brimittleman 2020-04-10
c2a0778 brimittleman 2020-04-06
fa2369c brimittleman 2020-03-18

Change colors:

ggplot(ResUpdownAll_gather, aes(x=Set, by=Set, fill=Set, y=PhyloP)) + geom_boxplot() + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + theme(legend.position = "none")+ theme_classic()+ guides(fill = FALSE)

Version Author Date
214c39d brimittleman 2020-05-04
5d82297 brimittleman 2020-04-22

Color just PAS and surrounding:

ResUpdownAll_gather2= ResUpdownAll_gather %>% mutate(region=ifelse(Set=="phyloP", "Yes", "No"))

ggplot(ResUpdownAll_gather2, aes(x=Set, by=Set, fill=region, y=PhyloP)) + geom_boxplot(notch = T) + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + guides(fill = FALSE) + theme_classic()

Version Author Date
214c39d brimittleman 2020-05-04
3790efa brimittleman 2020-04-23
5d82297 brimittleman 2020-04-22

write out:

write.table(ResUpdownAll_gather2, "../output/FigureDF/SeqConservation.txt", col.names=T, row.names=T, quote=F)

try this plot seperated by location.

PASMetaSm=PASMeta %>% select(PAS,loc)
ResUpdownAll_gather2_loc=ResUpdownAll_gather2 %>% inner_join(PASMetaSm,by="PAS")

ggplot(ResUpdownAll_gather2_loc, aes(x=Set, by=Set, fill=region, y=PhyloP)) + geom_boxplot(notch = T) + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions") + guides(fill = FALSE) + theme_classic() + facet_grid(~loc)

Version Author Date
5a9d387 brimittleman 2020-05-05 
ResUpdownAll_gather2_loc_noCoding=ResUpdownAll_gather2_loc %>% filter(loc != "cds")

ggplot(ResUpdownAll_gather2_loc_noCoding, aes(x=Set, by=Set, fill=region, y=PhyloP)) + geom_boxplot(notch = T) + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved than surrounding regions after removing coding sites") + guides(fill = FALSE) + theme_classic()

Version Author Date
5a9d387 brimittleman 2020-05-05

Only 3’ UTR:

ResUpdownAll_gather2_loc_utr=ResUpdownAll_gather2_loc %>% filter(loc == "utr3")

ggplot(ResUpdownAll_gather2_loc_utr, aes(x=Set, by=Set, fill=region, y=PhyloP)) + geom_boxplot(notch = T) + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="3' UTR PAS are more conserved than surrounding regions") + guides(fill = FALSE) + theme_classic()

kruskal wallis is like a anova, diff in groups,

wilcox test the true to sourounding regions:

#ResUpdownAll_gather2

kruskal.test(PhyloP ~ Set, data = ResUpdownAll_gather2)

    Kruskal-Wallis rank sum test

data:  PhyloP by Set
Kruskal-Wallis chi-squared = 1609.6, df = 6, p-value < 2.2e-16
wilcox.test(PhyloP ~ region, data = ResUpdownAll_gather2,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  PhyloP by region
W = 111170000, p-value < 2.2e-16
alternative hypothesis: true location shift is less than 0
#ResUpdownAll_gather2_loc_noCoding

kruskal.test(PhyloP ~ Set, data = ResUpdownAll_gather2_loc_noCoding)

    Kruskal-Wallis rank sum test

data:  PhyloP by Set
Kruskal-Wallis chi-squared = 621.46, df = 6, p-value < 2.2e-16
wilcox.test(PhyloP ~ region, data = ResUpdownAll_gather2_loc_noCoding,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  PhyloP by region
W = 83420000, p-value < 2.2e-16
alternative hypothesis: true location shift is less than 0
kruskal.test(PhyloP ~ Set, data = ResUpdownAll_gather2_loc_utr)

    Kruskal-Wallis rank sum test

data:  PhyloP by Set
Kruskal-Wallis chi-squared = 1423, df = 6, p-value < 2.2e-16
wilcox.test(PhyloP ~ region, data = ResUpdownAll_gather2_loc_utr,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  PhyloP by region
W = 13907000, p-value < 2.2e-16
alternative hypothesis: true location shift is less than 0
ResUpdownAll_gather2_loc_int=ResUpdownAll_gather2_loc %>% filter(loc == "intron")

wilcox.test(PhyloP ~ region, data = ResUpdownAll_gather2_loc_int,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  PhyloP by region
W = 15030000, p-value = 0.9989
alternative hypothesis: true location shift is less than 0
ResUpdownAll_gather2_loc_utr5=ResUpdownAll_gather2_loc %>% filter(loc == "utr5")

wilcox.test(PhyloP ~ region, data = ResUpdownAll_gather2_loc_utr5,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  PhyloP by region
W = 163620, p-value = 0.01092
alternative hypothesis: true location shift is less than 0
ResUpdownAll_gather2_loc_cds=ResUpdownAll_gather2_loc %>% filter(loc == "cds")

wilcox.test(PhyloP ~ region, data = ResUpdownAll_gather2_loc_cds, alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  PhyloP by region
W = 1517100, p-value < 2.2e-16
alternative hypothesis: true location shift is less than 0
ResUpdownAll_gather2_loc_end=ResUpdownAll_gather2_loc %>% filter(loc == "end")

wilcox.test(PhyloP ~ region, data = ResUpdownAll_gather2_loc_end,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  PhyloP by region
W = 1235600, p-value = 7.024e-06
alternative hypothesis: true location shift is less than 0

dAPA vs not by loc:

ResUpdownAll_gather2_loc_sig= ResUpdownAll_gather2_loc %>% filter(Set=="phyloP") 


ggplot(ResUpdownAll_gather2_loc_sig, aes(x=loc, by=SigPAU2, y=PhyloP, fill=SigPAU2)) +geom_boxplot()+ scale_fill_brewer(palette = "Dark2")  + stat_compare_means()


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] reshape2_1.4.3  ggpubr_0.2      magrittr_1.5    forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1    
 [9] tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         utf8_1.1.4         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       workflowr_1.6.0    cellranger_1.1.0  
[22] rvest_0.3.2        evaluate_0.12      labeling_0.3      
[25] knitr_1.20         httpuv_1.4.5       fansi_0.4.0       
[28] broom_0.5.1        Rcpp_1.0.4.6       promises_1.0.1    
[31] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[34] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[37] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[40] cli_1.1.0          tools_3.5.1        lazyeval_0.2.1    
[43] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[46] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[49] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[52] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[55] compiler_3.5.1