Remove genes with unlisted sites- check robustness

Last updated: 2020-06-09

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/SetsdAPADIC.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dInforContent.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/incorporateQTLsAncestral.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/miRNAanalysis.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd
    Modified:   analysis/unliftedsites.Rmd

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	1f3d41d	brimittleman	2020-06-09	add figure out
html	bb8d220	brimittleman	2020-06-04	Build site.
Rmd	a276deb	brimittleman	2020-06-04	add robust to unlift

In a previous analysis I found 300 genes that may be affected by unlifted PAS. I will use this analysis to check if my results are consistent if we are conservative and remove these genes.

library(ggpubr)

Loading required package: ggplot2

Loading required package: magrittr

library(workflowr)

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

library(tidyverse)

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

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

── Conflicts ───────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract()   masks magrittr::extract()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::lag()       masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()

library(cowplot)


Attaching package: 'cowplot'

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

    get_legend

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

    ggsave

UnfliftGenes=read.table("../data/UnliftedSites/GeneswUnliftedandPassingPAS.txt", header = T,stringsAsFactors = F)

-Effect size relationships

-enrichment for DE/TE

-same diff dominant

p not e

Effect size relationships

Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)  %>% dplyr::select(PAS, chr, start,end, loc)
DiffIso= read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", 'start','end')) %>% filter(loc %in% c("intron","utr3"))
DiffIsoTop=DiffIso %>% mutate(AvgUsageBoth=(Human+Chimp)/2) %>% group_by(gene) %>% arrange(p.adjust,desc(AvgUsageBoth)) %>% slice(1) %>% ungroup()

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID, Gene.name)

DE=read.table("../data/DiffExpression/DEtested_allres.txt",stringsAsFactors = F,header = F, col.names = c("Gene_stable_ID" ,"logFC" ,"AveExpr" , "t" ,  "P.Value" ,  "adj.P.Val", "B"  )) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::rename('gene'=Gene.name) %>% dplyr::select(-Gene_stable_ID) %>% mutate(CorrectedlogFC=-1*logFC)

DeandAPA= DiffIsoTop %>% inner_join(DE, by="gene") %>%anti_join(UnfliftGenes,by="gene")

DE_all=ggplot(DeandAPA,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="APA v DE \n Remove 353 unlifted", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(label.x = -8,label.y = -1) +theme_classic(base_size = 12)

DE_all

Version	Author	Date
bb8d220	brimittleman	2020-06-04

DE_split=ggplot(DeandAPA,aes(y=deltaPAU, x=CorrectedlogFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="APA v DE \n Remove 353 unlifted", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,0),label.y = -1) +theme_classic(base_size = 12)

DE_split

Version	Author	Date
bb8d220	brimittleman	2020-06-04

DeandAPASig= DeandAPA %>% filter(SigPAU2=="Yes", adj.P.Val<=0.05)

nrow(DeandAPASig)

[1] 393

DE_sig_all=ggplot(DeandAPASig,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="Significant differences in APA and expression\n Remove 353 unlifted", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(label.x = -8,label.y = -1) +theme_classic(base_size = 12)

DE_sig_all

Version	Author	Date
bb8d220	brimittleman	2020-06-04

DE_sig_split=ggplot(DeandAPASig,aes(y=deltaPAU, x=CorrectedlogFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="Significant differences in APA and expression\n Remove 353 unlifted", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,0),label.y = -1) +theme_classic(base_size = 12)

DE_sig_split

Version	Author	Date
bb8d220	brimittleman	2020-06-04

DE v TE

Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) 
Meta_genes= Meta %>% select(gene) %>% unique()

Meta_PAS=Meta %>%  select(PAS,gene)

dAPAGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F)
dAPAPAS=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene")) %>% select(PAS,gene,SigPAU2 ) 

dAPAPAS_genes= dAPAPAS %>% select(gene) %>% unique()

dAPATestedGenes= dAPAPAS  %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% dAPAGenes$gene,"Yes", "No")) 

dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F)%>% select(sIC,gene)
dICdata_sig= dICdata %>% filter(sIC=="Yes")

dAPAandDic= dICdata %>% inner_join(dAPATestedGenes,by="gene") %>% mutate(Both=ifelse(sIC=="Yes" & dAPA=="Yes", "Yes","No"),OnlyIC=ifelse(sIC=="Yes" & dAPA=="No", "Yes","No"),OnlyAPA=ifelse(sIC=="No" & dAPA=="Yes", "Yes","No"))

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

Join DE:

DEandAPA=DiffExp %>% inner_join(dAPAandDic,by="gene") %>% anti_join(UnfliftGenes,by="gene")

Expression enrichments:

sets=c("OnlyAPA", "OnlyIC", "Both")
DE_pval=c()
DE_enrich=c()

x=nrow(DEandAPA %>% filter(OnlyAPA=="Yes", DE=="Yes"))
m=nrow(DEandAPA %>% filter(DE=="Yes"))
n=nrow(DEandAPA %>% filter(DE=="No"))
k=nrow(DEandAPA %>% filter(OnlyAPA=="Yes"))
N=nrow(DEandAPA)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.0006534149

DE_pval=c(DE_pval, phyper(x-1,m,n,k,lower.tail=F))
DE_enrich=c(DE_enrich, (x/k)/(m/N))

x=nrow(DEandAPA %>% filter(OnlyIC=="Yes", DE=="Yes"))
m=nrow(DEandAPA %>% filter(DE=="Yes"))
n=nrow(DEandAPA %>% filter(DE=="No"))
k=nrow(DEandAPA %>% filter(OnlyIC=="Yes"))
N=nrow(DEandAPA)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.2234779

DE_pval=c(DE_pval, phyper(x-1,m,n,k,lower.tail=F))
DE_enrich=c(DE_enrich, (x/k)/(m/N))


x=nrow(DEandAPA %>% filter(Both=="Yes", DE=="Yes"))
m=nrow(DEandAPA %>% filter(DE=="Yes"))
n=nrow(DEandAPA %>% filter(DE=="No"))
k=nrow(DEandAPA %>% filter(Both=="Yes"))
N=nrow(DEandAPA)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.0162844

DE_pval=c(DE_pval, phyper(x-1,m,n,k,lower.tail=F))
DE_enrich=c(DE_enrich, (x/k)/(m/N))


DEdf=as.data.frame(cbind(sets,DE_pval, DE_enrich)) %>% rename(Pval=DE_pval, Enrichment=DE_enrich) %>% mutate(Pheno="Expression")
DEdf

     sets                 Pval       Enrichment      Pheno
1 OnlyAPA 0.000653414923024692 1.12256754451574 Expression
2  OnlyIC    0.223477911524423 1.05270145258434 Expression
3    Both   0.0162844040325435 1.14539262950798 Expression

Ribo=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",header = T, stringsAsFactors = F) %>% rename("Gene_stable_ID"= ENSG) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name, HvC.beta, HvC.pvalue, HvC.FDR) %>% rename("gene"=Gene.name) %>% mutate(dTE=ifelse(HvC.FDR <0.05, "Yes","No"))
RiboSmall= Ribo %>% select(gene,dTE)

DTandAPA=Ribo %>% inner_join(dAPAandDic,by="gene")  %>% anti_join(UnfliftGenes,by="gene")

DT_pval=c()
DT_enrich=c()

x=nrow(DTandAPA %>% filter(OnlyAPA=="Yes", dTE=="Yes"))
m=nrow(DTandAPA %>% filter(dTE=="Yes"))
n=nrow(DTandAPA %>% filter(dTE=="No"))
k=nrow(DTandAPA %>% filter(OnlyAPA=="Yes"))
N=nrow(DTandAPA)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.04848155

DT_pval=c(DT_pval, phyper(x-1,m,n,k,lower.tail=F))
DT_enrich=c(DT_enrich, (x/k)/(m/N))


x=nrow(DTandAPA %>% filter(OnlyIC=="Yes", dTE=="Yes"))
m=nrow(DTandAPA %>% filter(dTE=="Yes"))
n=nrow(DTandAPA %>% filter(dTE=="No"))
k=nrow(DTandAPA %>% filter(OnlyIC=="Yes"))
N=nrow(DTandAPA)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.02402838

DT_pval=c(DT_pval, phyper(x-1,m,n,k,lower.tail=F))
DT_enrich=c(DT_enrich, (x/k)/(m/N))

x=nrow(DTandAPA %>% filter(Both=="Yes", dTE=="Yes"))
m=nrow(DTandAPA %>% filter(dTE=="Yes"))
n=nrow(DTandAPA %>% filter(dTE=="No"))
k=nrow(DTandAPA %>% filter(Both=="Yes"))
N=nrow(DTandAPA)
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.02586374

DT_pval=c(DT_pval, phyper(x-1,m,n,k,lower.tail=F))
DT_enrich=c(DT_enrich, (x/k)/(m/N))
DTdf=as.data.frame(cbind(sets,DT_pval, DT_enrich)) %>% rename(Pval=DT_pval, Enrichment=DT_enrich) %>% mutate(Pheno="Translation")
DTdf

     sets               Pval       Enrichment       Pheno
1 OnlyAPA 0.0484815452167291 1.10358865436524 Translation
2  OnlyIC 0.0240283752892615 1.20480789240106 Translation
3    Both 0.0258637425077943 1.21130122254139 Translation

AllDF= DEdf %>% bind_rows(DTdf)

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

AllDF$Pval=as.numeric(AllDF$Pval)
AllDF$Enrichment=as.numeric(AllDF$Enrichment)
AllDF$Pheno=factor(AllDF$Pheno, levels=c("Expression", "Translation", "Protein"))
useCOl <- c("#d73027", "#4575b4","#fee090")

enrichpoint=ggplot(AllDF,aes(x=sets,col=sets,y=Enrichment,label = round(Enrichment,3)))+ geom_bar(stat="identity",color="grey",aes(y=AllDF$Enrichment),width=.01)+geom_point(size=10) + coord_flip() + geom_hline(yintercept = 1) + facet_grid(~Pheno)+scale_color_manual(values=useCOl)+ labs( title="Enrichment for APA phenotype differences in other regulatory phenotypes \n Remove 353 unlifted",x="Set", y="Enrichment")+geom_text(color = "black", size = 3) + theme(legend.position = "none")
pvalplot=ggplot(AllDF,aes(x=Pheno, by=sets, y=-log10(Pval),fill=sets)) +geom_bar(stat = "identity",position = "dodge") +geom_hline(yintercept =1.3)+ scale_fill_manual(values=useCOl)+  theme(legend.position = "bottom")

plot_grid(enrichpoint,pvalplot, nrow=2)

Version	Author	Date
bb8d220	brimittleman	2020-06-04

dominant PAS

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

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

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

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

BothResAll=BothRes_10 %>% bind_rows(BothRes_20) %>% bind_rows(BothRes_30) %>% bind_rows(BothRes_40) %>% bind_rows(BothRes_50) %>% bind_rows(BothRes_60) %>% bind_rows(BothRes_70) %>% bind_rows(BothRes_80) %>% bind_rows(BothRes_90) 

Pval=c()
Enrich=c()
set=c(10,20,30,40,50,60,70,80,90)
expected=c()
actual=c()


nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DE= read.table("../data/DiffExpression/DEtested_allres.txt",header=F, stringsAsFactors = F,col.names = c('Gene_stable_ID', 'logFC' ,'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B')) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% rename("gene"=Gene.name) %>% mutate(DE=ifelse(adj.P.Val<=.05, "Yes","No")) %>% select(gene,DE)
DE_yes= DE %>% filter(DE=="Yes")
All4= BothResAll  %>% select(gene,cut,Set) %>% inner_join(DE, by="gene")


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

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

diffP=ggplot(ResDF,aes(x=set, y=-log10(Pval),fill=set)) + geom_bar(stat="identity") +labs(title="Enrichment pvalues for DE and different dominant \n Remove 353 unlifted",x="Dominance Cutoff")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")+ geom_hline(yintercept = 1.30103)
diffE=ggplot(ResDF,aes(x=set, y=Enrich,fill=set)) + geom_bar(stat="identity") + geom_hline(yintercept = 1)+labs(title="Enrichment for DE and different dominant \n Remove 353 unlifted",x="Dominance Cutoff")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")

PvalSame=c()
EnrichSame=c()
expectedSame=c()
actualSame=c()



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

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

Samep=ggplot(ResDFSame,aes(x=set, y=-log10(PvalSame),fill=set)) + geom_bar(stat="identity") +labs(title="Enrichment pvalues for DE and same dominant  \n Remove 353 unlifted",x="Dominance Cutoff")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")+ geom_hline(yintercept = 1.30103)
SameE=ggplot(ResDFSame,aes(x=set, y=EnrichSame,fill=set)) + geom_bar(stat="identity") + geom_hline(yintercept = 1)+labs(title="Enrichment for DE and same dominant \n Remove 353 unlifted",x="Dominance Cutoff")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")

plot_grid(diffE, SameE, diffP, Samep)

Version	Author	Date
bb8d220	brimittleman	2020-06-04

plot_grid( diffP, Samep)

Version	Author	Date
bb8d220	brimittleman	2020-06-04

p not e

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


Meta_genes= Meta %>% select(gene) %>% unique()

Meta_PAS=Meta %>%  select(PAS,gene)

dAPAGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F) %>% anti_join(UnfliftGenes,by="gene") 
dAPAPAS=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene")) %>% select(PAS,gene,SigPAU2 ) %>% anti_join(UnfliftGenes,by="gene") 

dAPAPAS_genes= dAPAPAS %>% select(gene) %>% unique()

dAPATestedGenes= dAPAPAS  %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% dAPAGenes$gene,"Yes", "No")) 

dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F)%>% select(sIC,gene)%>% anti_join(UnfliftGenes,by="gene") 

dAPAandDic= dICdata %>% inner_join(dAPATestedGenes,by="gene") %>% mutate(Both=ifelse(sIC=="Yes" & dAPA=="Yes", "Yes","No"),OnlyIC=ifelse(sIC=="Yes" & dAPA=="No", "Yes","No"),OnlyAPA=ifelse(sIC=="No" & dAPA=="Yes", "Yes","No"))

dIConly=dAPAandDic %>%filter(OnlyIC=="Yes") 

Both=dAPAandDic %>%filter(Both=="Yes")
dAPAonly=dAPAandDic %>%filter(OnlyAPA=="Yes")

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID, Gene.name)

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

Prot= read.table("../data/Khan_prot/ProtData_effectSize.txt",header = T,stringsAsFactors = F)  %>% mutate(dP=ifelse(pval<0.05, "Yes", "No")) %>% filter(dP=="Yes")

Ribo=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",header = T, stringsAsFactors = F) %>% rename("Gene_stable_ID"= ENSG) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name, HvC.beta, HvC.pvalue, HvC.FDR) %>% rename("gene"=Gene.name) %>% mutate(dTE=ifelse(HvC.FDR <0.05, "Yes","No")) %>% filter(dTE=="Yes")


RiboAll=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",header = T, stringsAsFactors = F) %>% rename("Gene_stable_ID"= ENSG) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name, HvC.beta, HvC.pvalue, HvC.FDR) %>% rename("gene"=Gene.name) %>% mutate(dTE=ifelse(HvC.FDR <0.05, "Yes","No")) 


Set=c("OnlydIC", "Both", "OnlyAPA")
Number=c(40, 33,76)
SetSize=c(nrow(dIConly),nrow(Both),nrow(dAPAonly) )

useCOl <- c("#d73027", "#4575b4","#fee090")
DFres=data.frame(cbind(Set,Number,SetSize)) 
DFres$Number=as.numeric(as.character(DFres$Number))
DFres$SetSize=as.numeric(as.character(DFres$SetSize))

DFres_prop=DFres %>% mutate(Prop=Number/SetSize)

numberPlot=ggplot(DFres_prop,aes(x=Set, fill=Set, y=Number))+ geom_bar(stat="identity")+ scale_fill_manual(values=useCOl)+geom_text(aes(label=Number), position=position_dodge(width=0.9), vjust=2)+ theme(legend.position = "none") + labs(title="Number of dP not DE genes", y="Number of Genes",x="")+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS\nLevel",OnlydIC= "Isoform\n Diversity"))

propPlot=ggplot(DFres_prop,aes(x=Set, fill=Set, y=Prop))+ geom_bar(stat="identity")+ scale_fill_manual(values=useCOl) + labs(title="Proportion of APA set that are\n dP not DE", y="Proportion of APA set",x="")+geom_text(aes(label=round(Prop,3)), position=position_dodge(width=0.9), vjust=2) + theme(legend.position = "none")+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS\nLevel",OnlydIC= "Isoform\n Diversity"))

numberprop=plot_grid(numberPlot,propPlot)
numberprop

Version	Author	Date
bb8d220	brimittleman	2020-06-04

ProtInfo=read.table("../data/PTM/ProtLength.txt", sep = "\t",stringsAsFactors = F,header = T,col.names = c("entry","organism", "nAA", "gene")) %>% select(nAA, gene)

Interactions=read.table("../data/bioGRID/GeneswInteractions.txt",stringsAsFactors = F, header = T) %>% inner_join(ProtInfo, by="gene")%>% mutate(NormInter=nInt/nAA)

#DiffExp$gene, DT=Ribo$gene, DP=Prot$gene

dAPAandDic_wP=dAPAandDic %>% mutate(dE=ifelse(gene %in%DiffExp$gene, "Yes", "No" ), dP=ifelse(gene %in%Prot$gene,"Yes","No" ), dPnotDE=ifelse(dE=="No"&dP=="Yes", "Yes","No")) %>% inner_join(Interactions, by="gene")

dAPAandDic_wP_dAPA= dAPAandDic_wP %>% filter(dAPA=="Yes")

dAPAandDic_wP_both= dAPAandDic_wP %>% filter(Both=="Yes")


dAPAandDic_wP_IC= dAPAandDic_wP %>% filter(OnlyIC=="Yes")

dapaProt=ggplot(dAPAandDic_wP_dAPA,aes(x=dPnotDE, y=log10(NormInter),fill=dPnotDE)) + geom_boxplot(notch = T) + stat_compare_means() + scale_fill_manual(values = c("grey", "#4575b4"))+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=10), legend.position = "false") + labs(x="Expression independent set", y="log10(Normalized Interaction)", title="") 


bothProt=ggplot(dAPAandDic_wP_both,aes(x=dPnotDE, y=log10(NormInter),fill=dPnotDE)) + geom_boxplot(notch = T) + stat_compare_means() + scale_fill_manual(values = c("grey", "#d73027"))+ theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=10), legend.position = "false") + labs(x="Expression independent set", y="log10(Normalized Interaction)", title="")

dicProt=ggplot(dAPAandDic_wP_IC,aes(x=dPnotDE, y=log10(NormInter),fill=dPnotDE)) + geom_boxplot(notch = T) + stat_compare_means() + scale_fill_manual(values = c("grey", "#fee090"))+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=10), legend.position = "false") + labs(x="Expression independent set", y="log10(Normalized Interaction)", title="")

interactionplots=plot_grid(bothProt,dapaProt,dicProt, nrow=1)

dAPAandDic_wP_trans= dAPAandDic_wP %>% inner_join(RiboAll,by="gene")



dAPAandDic_wP_trans_dpnote= dAPAandDic_wP_trans %>% filter(dPnotDE=="Yes") %>% select(gene,Both, OnlyIC, OnlyAPA,dTE)


dAPAandDic_wP_trans_dpnoteOnlyAPA=dAPAandDic_wP_trans_dpnote  %>% filter(OnlyAPA=="Yes") %>%  group_by(dTE) %>% summarise(ndTE=n()) %>% mutate(set="OnlyAPA")

dAPAandDic_wP_trans_dpnoteBoth=dAPAandDic_wP_trans_dpnote  %>% filter(Both=="Yes") %>%  group_by(dTE) %>% summarise(ndTE=n()) %>% mutate(set="Both")

dAPAandDic_wP_trans_dpnoteIC=dAPAandDic_wP_trans_dpnote  %>% filter(OnlyIC=="Yes") %>%  group_by(dTE) %>% summarise(ndTE=n()) %>% mutate(set="OnlyIC")


AllTenum= dAPAandDic_wP_trans_dpnoteOnlyAPA %>% bind_rows(dAPAandDic_wP_trans_dpnoteBoth) %>% bind_rows(dAPAandDic_wP_trans_dpnoteIC)

numTE=ggplot(AllTenum, aes(x=set,by=dTE, y=ndTE,fill=set, alpha=dTE)) +geom_bar(stat="identity", position = "dodge") + labs(title="Most dP not dE genes are not dTE", y="Number of Genes",x="") + scale_fill_manual(values = useCOl ) + scale_alpha_manual(values=c(.6, 1)) + theme(legend.position = "bottom") + geom_text(aes(label=ndTE), position=position_dodge(width=0.9), vjust=1)+guides(fill = FALSE)+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS\nLevel",OnlyIC= "Isoform\n Diversity"))
numTE

Version	Author	Date
bb8d220	brimittleman	2020-06-04

propTe=ggplot(AllTenum, aes(x=set,by=dTE, y=ndTE,fill=set, alpha=dTE)) +geom_bar(stat="identity", position = "fill") + labs(title="Most dP not dE genes \nare not dTE", y="Proportion",x="") + scale_fill_manual(values = useCOl ) + scale_alpha_manual(values=c(.4, 1)) + theme(legend.position = "bottom")+guides(fill = FALSE)+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS\nLevel",OnlyIC= "Isoform\n Diversity"))
propTe

Version	Author	Date
bb8d220	brimittleman	2020-06-04

teplots=plot_grid(numTE, propTe, nrow =1)
teplots

Version	Author	Date
bb8d220	brimittleman	2020-06-04

fullplot=plot_grid(numberprop, teplots,interactionplots, nrow = 3)
fullplot

Version	Author	Date
bb8d220	brimittleman	2020-06-04

Plots:

fig3:

plot_grid(DE_all,DE_split,DE_sig_all,DE_sig_split, scale = c(.9,.9,.9,.9), labels=c("a","b","c","d"),rel_widths=c(1,1.3,1,1.3))

pdf("../output/removeUnlift_fig3.pdf", height=8, width=12)
plot_grid(DE_all,DE_split,DE_sig_all,DE_sig_split, scale = c(.9,.9,.9,.9), labels=c("a","b","c","d"),rel_widths=c(1,1.3,1,1.3))
dev.off()

png 
  2

fig4

pdf("../output/removeUnlift_fig4.pdf", height=6, width=8,useKerning=F)
plot_grid(enrichpoint,pvalplot, nrow=2)
dev.off()

png 
  2

fig 5:

pdf("../output/removeUnlift_fig5.pdf", height=6, width=10,useKerning=F)
plot_grid(diffE, SameE, diffP, Samep)

dev.off()

png 
  2

fig 6:

pdf("../output/removeUnilift_fig6.pdf", height=9, width=12,useKerning=F)
fullplot
dev.off()

png 
  2

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   forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] tidyverse_1.2.1 workflowr_1.6.0 ggpubr_0.2      magrittr_1.5   
[13] ggplot2_3.1.1  

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_1.0.4.6      
[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] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.26.1       compiler_3.5.1

Remove genes with unlisted sites- check robustness

Briana Mittleman

6/4/2020

Effect size relationships

DE v TE

dominant PAS

p not e