Last updated: 2020-07-03
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Knit directory: Comparative_APA/analysis/
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library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(UpSetR)
library(VennDiagram)
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I want to look at regulatory phenotype regulation based on dAPA, both, or dIC.
For this analysis I will use dIC at 5% FDR. Numbers are smaller but overlaps suggest it is more biological.
I will look at genes tested in all analysis then proportion results to only dAPA, dIC and dAPA, or dIC only. I will test for enrichement in each of these sets with expression, translation, and protein.
Load APA data:
For apa I reduce to gene level and count it as sig if at least one PAS is different.
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$gene,"Yes", "No"))
dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F)%>% select(sIC,gene)
dICdata_sig= dICdata %>% filter(sIC=="Yes")
dAPAandDic= dICdata %>% inner_join(dAPATestedGenes,by="gene") %>% mutate(Both=ifelse(sIC=="Yes" & dAPA=="Yes", "Yes","No"),OnlyIC=ifelse(sIC=="Yes" & dAPA=="No", "Yes","No"),OnlyAPA=ifelse(sIC=="No" & dAPA=="Yes", "Yes","No"))
nrow(dAPAandDic)
[1] 8422
Make an upsetter plot first:
#useCOl <- c("#d73027", "#4575b4","#fee090")
listInput <- list(SiteLevel=dAPAGenes$gene, IsoformDiversity=dICdata_sig$gene)
upset(fromList(listInput), order.by = "freq", empty.intersections = "on")
Ven diagram:
overlap=intersect(dAPAGenes$gene,dICdata_sig$gene)
grid.newpage()
venn.plot <- draw.pairwise.venn(area1 = length(dAPAGenes$gene),
area2 = length(dICdata_sig$gene),
cross.area = length(overlap),
c("Site Level", "Isoform Diversity"), scaled = TRUE,
fill = c("#d73027", "#fee090"),
cex = 1.5,
cat.cex = 1.5,
cat.pos = c(320, 25),
cat.dist = .05)
Version | Author | Date |
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9937c4b | brimittleman | 2020-07-02 |
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)
DEandAPA=DiffExp %>% inner_join(dAPAandDic,by="gene")
nrow(DEandAPA)
[1] 7465
Erichment for only APA:
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] 0.0005193482
DE_pval=c(DE_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 447
DE_enrich=c(DE_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.122516
Only dIC
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] 0.2216107
DE_pval=c(DE_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 154
DE_enrich=c(DE_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.052215
Both:
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] 0.02681958
DE_pval=c(DE_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 163
DE_enrich=c(DE_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.128023
All de res:
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 0.000519348163532497 1.12251636247181 Expression
2 OnlyIC 0.221610701204403 1.05221488574561 Expression
3 Both 0.0268195798877835 1.12802297586811 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")
nrow(DTandAPA)
[1] 6477
#sets=c("OnlyAPA", "OnlyIC", "Both")
DT_pval=c()
DT_enrich=c()
only APA
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] 0.06327494
DT_pval=c(DT_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 210
DT_enrich=c(DT_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.093646
Only dIC
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] 0.02205198
DT_pval=c(DT_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 94
DT_enrich=c(DT_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.205226
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] 0.01134051
DT_pval=c(DT_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 91
DT_enrich=c(DT_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.240121
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 0.0632749366195107 1.09364622715088 Translation
2 OnlyIC 0.0220519791477263 1.20522601526234 Translation
3 Both 0.0113405052499701 1.24012060208466 Translation
(pval is adjusted already)
Prot= read.table("../data/Khan_prot/ProtData_effectSize.txt",header = T,stringsAsFactors = F) %>% mutate(dP=ifelse(pval<0.05, "Yes", "No"))
ProtSmall=Prot %>% select(gene, dP)
DPandAPA=Prot %>% inner_join(dAPAandDic,by="gene")
nrow(DPandAPA)
[1] 2641
#sets=c("OnlyAPA", "OnlyIC", "Both")
DP_pval=c()
DP_enrich=c()
only APA
x=nrow(DPandAPA %>% filter(OnlyAPA=="Yes", dP=="Yes"))
m=nrow(DPandAPA %>% filter(dP=="Yes"))
n=nrow(DPandAPA %>% filter(dP=="No"))
k=nrow(DPandAPA %>% filter(OnlyAPA=="Yes"))
N=nrow(DPandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.2293006
DP_pval=c(DP_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 130
DP_enrich=c(DP_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 1.052798
Only dIC
x=nrow(DPandAPA %>% filter(OnlyIC=="Yes", dP=="Yes"))
m=nrow(DPandAPA %>% filter(dP=="Yes"))
n=nrow(DPandAPA %>% filter(dP=="No"))
k=nrow(DPandAPA %>% filter(OnlyIC=="Yes"))
N=nrow(DPandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.8222889
DP_pval=c(DP_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 68
DP_enrich=c(DP_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 0.925635
x=nrow(DPandAPA %>% filter(Both=="Yes", dP=="Yes"))
m=nrow(DPandAPA %>% filter(dP=="Yes"))
n=nrow(DPandAPA %>% filter(dP=="No"))
k=nrow(DPandAPA %>% filter(Both=="Yes"))
N=nrow(DPandAPA)
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.8802321
DP_pval=c(DP_pval, phyper(x-1,m,n,k,lower.tail=F))
x
[1] 56
DP_enrich=c(DP_enrich, (x/k)/(m/N))
(x/k)/(m/N)
[1] 0.895688
DPdf=as.data.frame(cbind(sets,DP_pval, DP_enrich)) %>% rename(Pval=DP_pval, Enrichment=DP_enrich) %>% mutate(Pheno="Protein")
DPdf
sets Pval Enrichment Pheno
1 OnlyAPA 0.229300585846805 1.05279781179472 Protein
2 OnlyIC 0.822288948709401 0.925634999175326 Protein
3 Both 0.880232087900127 0.895687984496124 Protein
AllDF= DEdf %>% bind_rows(DTdf) %>% bind_rows(DPdf)
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
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$Pheno, levels=c("Expression", "Translation", "Protein"))
useCOl <- c("#d73027", "#4575b4","#fee090")
enrichplot=ggplot(AllDF,aes(x=Pheno, by=sets, y=Enrichment,fill=sets)) +geom_bar(stat = "identity",position = "dodge") +geom_hline(yintercept =1) + scale_fill_manual(values=useCOl)
enrichplot
enrichpoint=ggplot(AllDF,aes(x=sets,col=sets,y=Enrichment,label = round(Enrichment,3)))+ geom_bar(stat="identity",color="grey",aes(y=AllDF$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",x="Set", y="Enrichment")+geom_text(color = "black", size = 3) + theme(legend.position = "none")
enrichpoint
Version | Author | Date |
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9937c4b | brimittleman | 2020-07-02 |
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")
pvalplot
Version | Author | Date |
---|---|---|
9937c4b | brimittleman | 2020-07-02 |
plot together:
plot_grid(enrichpoint,pvalplot, nrow=2)
Plot without protien:
DETEDF= 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
DETEDF$Pval=as.numeric(DETEDF$Pval)
DETEDF$Enrichment=as.numeric(DETEDF$Enrichment)
DETEDF$Pheno=factor(DETEDF$Pheno, levels=c("Expression", "Translation", "Protein"))
enrichpointnoP=ggplot(DETEDF,aes(x=sets,col=sets,y=Enrichment,label = round(Enrichment,3)))+ geom_bar(stat="identity",color="grey",aes(y=DETEDF$Enrichment),width=.01)+geom_point(size=10) + coord_flip() + geom_hline(yintercept = 1) + facet_grid(~Pheno)+scale_color_manual(values=useCOl) + labs( title="Enrichment for APA phenotype \nin other regulatory phenotypes",x="", y="Enrichment")+geom_text(color = "black", size = 3) + theme(legend.position = "none")+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS Level",OnlyIC= "Isoform Diversity"))
enrichpointnoP
pvalplotnoP=ggplot(DETEDF,aes(x=Pheno, by=sets, y=-log10(Pval),fill=sets)) +geom_bar(stat = "identity",position = "dodge") +geom_hline(yintercept =1.3)+ scale_fill_manual(values=useCOl,labels=c("Both", "PAS Level", "Isoform Diversity"), name="")+ theme(legend.position = "bottom")
pvalplotnoP
Version | Author | Date |
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9937c4b | brimittleman | 2020-07-02 |
exandte=plot_grid(enrichpointnoP,pvalplotnoP, nrow=2)
exandte
Version | Author | Date |
---|---|---|
9937c4b | brimittleman | 2020-07-02 |
Write out data for figures
write.table(DETEDF, "../output/FigureDF/DEandTEenrich.txt",col.names = T,row.names = F, quote = F)
Examples:
Only dIC
dIConly=dAPAandDic %>% filter(OnlyIC=="Yes")
dIConly_translation=dIConly %>% inner_join(Ribo, by="gene") %>% filter(dTE =="Yes")
CLECL1 chimp uses 2 more often human uses 1 most often
GRHPR- human intronic just enough to change the utr ratio
hadha- human proximal, chimp 2 UTR
IVNS1ABP- chimp 1, human more
OGFOD3 - chimp more PAS used (good igv example)
ZNF512B human more spread
dIC_both= dAPAandDic %>% filter(Both=="Yes")
KhanData=read.csv("../data/Khan_prot/Khan_TableS4.csv",stringsAsFactors = F) %>% select(gene.symbol,contains("model") ) %>% rename("gene"=gene.symbol, "Protein"=model.num.protein, "RNA"=model.num.rna)
KhanData_g=KhanData %>% gather("Set", "Model", -gene)
KhanData_g$Model= as.factor(KhanData_g$Model)
KhanData_g_RNA= KhanData_g %>% filter(Set=="RNA")
KhanData_g_Prot= KhanData_g %>% filter(Set=="Protein")
Join with all of the tested gene.
KhanWithapa=dAPAandDic %>% inner_join(KhanData_g_RNA, by="gene")
Test only APA:
Model=seq(1,6)
EnrichmentRNA_apaOnly=c()
PvalueRNA_apaOnly=c()
for (i in seq(1:6)){
x=nrow(KhanWithapa %>% filter(OnlyAPA=="Yes", Model==i))
m=nrow(KhanWithapa %>% filter(Model==i))
n=nrow(KhanWithapa %>% filter(Model!=i))
k=nrow(KhanWithapa %>% filter(OnlyAPA=="Yes"))
N=nrow(KhanWithapa)
PvalueRNA_apaOnly=c(PvalueRNA_apaOnly, phyper(x-1,m,n,k,lower.tail=F))
enrich=(x/k)/(m/N)
EnrichmentRNA_apaOnly=c(EnrichmentRNA_apaOnly, enrich)
}
EnrichmentRNA_apaOnly
[1] 0.9684754 1.0722756 1.0057708 0.9978981 0.9306543 0.5480519
PvalueRNA_apaOnly
[1] 0.6235229 0.3663835 0.4837124 0.5336991 0.6393254 0.8579106
EnrichmentRNA_ICOnly=c()
PvalueRNA_ICOnly=c()
for (i in seq(1:6)){
x=nrow(KhanWithapa %>% filter(OnlyIC=="Yes", Model==i))
m=nrow(KhanWithapa %>% filter(Model==i))
n=nrow(KhanWithapa %>% filter(Model!=i))
k=nrow(KhanWithapa %>% filter(OnlyIC=="Yes"))
N=nrow(KhanWithapa)
PvalueRNA_ICOnly=c(PvalueRNA_ICOnly, phyper(x-1,m,n,k,lower.tail=F))
enrich=(x/k)/(m/N)
EnrichmentRNA_ICOnly=c(EnrichmentRNA_ICOnly, enrich)
}
EnrichmentRNA_ICOnly
[1] 1.2218555 1.1258893 0.9778327 0.7900026 1.6286449 2.8772727
PvalueRNA_ICOnly
[1] 0.12806690 0.33083203 0.64093810 0.97070244 0.15863924 0.08092906
Both
EnrichmentRNA_both=c()
PvalueRNA_both=c()
for (i in seq(1:6)){
x=nrow(KhanWithapa %>% filter(Both=="Yes", Model==i))
m=nrow(KhanWithapa %>% filter(Model==i))
n=nrow(KhanWithapa %>% filter(Model!=i))
k=nrow(KhanWithapa %>% filter(Both=="Yes"))
N=nrow(KhanWithapa)
PvalueRNA_both=c(PvalueRNA_both, phyper(x-1,m,n,k,lower.tail=F))
enrich=(x/k)/(m/N)
EnrichmentRNA_both=c(EnrichmentRNA_both, enrich)
}
EnrichmentRNA_both
[1] 0.8269255 1.6039159 0.9402736 0.9207978 1.5819068 2.2357616
PvalueRNA_both
[1] 0.84650091 0.01547134 0.78485772 0.75903782 0.20603443 0.22400482
nrow(KhanWithapa %>% filter(Both=="Yes", Model==1))
[1] 18
nrow(KhanWithapa %>% filter(Both=="Yes", Model==2))
[1] 22
Prot:
KhanPWithapa=dAPAandDic %>% inner_join(KhanData_g_Prot, by="gene")
Only APA
EnrichmentProt_apaOnly=c()
PvalueProp_apaOnly=c()
for (i in seq(1:6)){
x=nrow(KhanPWithapa %>% filter(OnlyAPA=="Yes", Model==i))
m=nrow(KhanPWithapa %>% filter(Model==i))
n=nrow(KhanPWithapa %>% filter(Model!=i))
k=nrow(KhanPWithapa %>% filter(OnlyAPA=="Yes"))
N=nrow(KhanPWithapa)
PvalueProp_apaOnly=c(PvalueProp_apaOnly, phyper(x-1,m,n,k,lower.tail=F))
enrich=(x/k)/(m/N)
EnrichmentProt_apaOnly=c(EnrichmentProt_apaOnly, enrich)
}
EnrichmentProt_apaOnly
[1] 0.9071205 1.3016234 0.9862340 0.9931703 0.0000000 1.2647353
PvalueProp_apaOnly
[1] 0.7049786 0.1332363 0.6250351 0.5639394 1.0000000 0.4816888
EnrichmentProt_ICOnly=c()
PvalueProt_ICOnly=c()
for (i in seq(1:6)){
x=nrow(KhanPWithapa %>% filter(OnlyIC=="Yes", Model==i))
m=nrow(KhanPWithapa %>% filter(Model==i))
n=nrow(KhanPWithapa %>% filter(Model!=i))
k=nrow(KhanPWithapa %>% filter(OnlyIC=="Yes"))
N=nrow(KhanPWithapa)
PvalueProt_ICOnly=c(PvalueProt_ICOnly, phyper(x-1,m,n,k,lower.tail=F))
enrich=(x/k)/(m/N)
EnrichmentProt_ICOnly=c(EnrichmentProt_ICOnly, enrich)
}
EnrichmentProt_ICOnly
[1] 1.3890282 0.7193182 0.9083734 1.0954084 7.1931818 0.0000000
PvalueProt_ICOnly
[1] 0.1268672 0.8539473 0.9098297 0.1667131 0.1342144 1.0000000
Both:
EnrichmentProt_both=c()
PvalueProt_both=c()
for (i in seq(1:6)){
x=nrow(KhanPWithapa %>% filter(Both=="Yes", Model==i))
m=nrow(KhanPWithapa %>% filter(Model==i))
n=nrow(KhanPWithapa %>% filter(Model!=i))
k=nrow(KhanPWithapa %>% filter(Both=="Yes"))
N=nrow(KhanPWithapa)
PvalueProt_both=c(PvalueProt_both, phyper(x-1,m,n,k,lower.tail=F))
enrich=(x/k)/(m/N)
EnrichmentProt_both=c(EnrichmentProt_both, enrich)
}
EnrichmentProt_both
[1] 0.9251427 0.6986755 0.9793589 1.0384375 0.0000000 3.8695874
PvalueProt_both
[1] 0.64571093 0.85534760 0.63516140 0.37920820 1.00000000 0.03822681
Significant:
Both RNA: 2 (1.6X, 0.01547134)
mRNA expression level pattern consistent with directional selection along chimpanzee lineage
nrow(KhanWithapa %>% filter(Both=="Yes", Model==2))
[1] 22
6 = evidence of relaxation of constraint along chimpanzee lineage
nrow(KhanPWithapa %>% filter(Both=="Yes", Model==6))
[1] 3
Both Protien: 6 (3.87X, 0.038)
For the dAPA only and both. where are the PAS contibuting to the relationship. are they enriched among dAPA PAS
want those that are DE genes
dAPAPAS_wLoc=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene")) %>% select(PAS, loc, SigPAU2, gene)
DiffExpSig= DiffExp %>% filter(DE=='Yes')
BothAPAgenes= dAPAandDic %>% filter(Both=="Yes") %>% inner_join(DiffExpSig, by="gene")
OnlyAPAgenes=dAPAandDic %>% filter(OnlyAPA=="Yes")%>% inner_join(DiffExpSig, by="gene")
dAPAPAS_wLocAndBoth= dAPAPAS_wLoc %>% filter(SigPAU2=="Yes") %>% mutate(dAPAonly=ifelse(gene %in%OnlyAPAgenes$gene,"Yes","No"), Both=ifelse(gene %in% BothAPAgenes$gene, "Yes", "No"))
enrichdAPAloc=c()
pvaldAPAloc=c()
expectddAPA=c()
pvaldAPAlocDep=c()
actualdAPA=c()
for (i in c("cds", "end", "intron", "utr3", "utr5")){
x=nrow(dAPAPAS_wLocAndBoth %>% filter(dAPAonly=="Yes", loc==i))
m=nrow(dAPAPAS_wLocAndBoth %>% filter(loc==i))
n=nrow(dAPAPAS_wLocAndBoth %>% filter(loc!=i))
k=nrow(dAPAPAS_wLocAndBoth %>% filter(dAPAonly=="Yes"))
N=nrow(dAPAPAS_wLocAndBoth)
actualdAPA=c(actualdAPA, x)
exp=k*(m/N)
expectddAPA=c(expectddAPA,exp)
pvaldAPAloc=c(pvaldAPAloc, phyper(x-1,m,n,k,lower.tail=F))
pvaldAPAlocDep=c(pvaldAPAlocDep, phyper(x,m,n,k,lower.tail=T))
enrichval=(x/k)/(m/N)
enrichdAPAloc=c(enrichdAPAloc, enrichval)
}
loc=c("cds", "end", "intron", "utr3", "utr5")
enrichdAPAloc
[1] 1.1101576 0.5710053 0.9253642 1.0860081 0.6995221
pvaldAPAloc
[1] 0.144559704 0.999924051 0.895180830 0.003461421 0.966377338
enrichdapalocdf=as.data.frame(cbind(loc, Actual=actualdAPA,Expected=expectddAPA, Enrichment=enrichdAPAloc,PvalEn=pvaldAPAloc, PvalDep=pvaldAPAlocDep)) %>% mutate(set="dAPAOnly")
enrichdapalocdf
loc Actual Expected Enrichment PvalEn
1 cds 77 69.3595217762596 1.11015759665107 0.144559704469824
2 end 26 45.533731853117 0.571005251312828 0.999924050908696
3 intron 134 144.807856532878 0.925364156395589 0.895180830348247
4 utr3 368 338.855678906917 1.08600806451613 0.00346142063512895
5 utr5 15 21.4432109308284 0.699522102747909 0.966377337513707
PvalDep set
1 0.886053815761103 dAPAOnly
2 0.000168192607453154 dAPAOnly
3 0.126610345939535 dAPAOnly
4 0.997406075194543 dAPAOnly
5 0.0601740116394651 dAPAOnly
enrichBothloc=c()
pvalBothloc=c()
pvalBothDepletloc=c()
expectBoth=c()
actualBoth=c()
for (i in c("cds", "end", "intron", "utr3", "utr5")){
x=nrow(dAPAPAS_wLocAndBoth %>% filter(Both=="Yes", loc==i))
m=nrow(dAPAPAS_wLocAndBoth %>% filter(loc==i))
n=nrow(dAPAPAS_wLocAndBoth %>% filter(loc!=i))
k=nrow(dAPAPAS_wLocAndBoth %>% filter(Both=="Yes"))
N=nrow(dAPAPAS_wLocAndBoth)
actualBoth=c(actualBoth, x)
exp=k*(m/N)
expectBoth=c(expectBoth,exp)
pvalBothDepletloc=c(pvalBothDepletloc, phyper(x,m,n,k,lower.tail=T))
pvalBothloc=c(pvalBothloc, phyper(x-1,m,n,k,lower.tail=F))
enrichval=(x/k)/(m/N)
enrichBothloc=c(enrichBothloc, enrichval)
}
loc=c("cds", "end", "intron", "utr3", "utr5")
enrichBothloc
[1] 1.2875004 0.3362044 0.6519211 1.1444959 1.5468171
pvalBothloc
[1] 0.061213041 0.999858359 0.999633975 0.005274121 0.070347462
enrichbothlocdf=as.data.frame(cbind(loc,Actual=actualBoth,Expected=expectBoth, Enrichment=enrichBothloc,PvalEn=pvalBothloc,PvalDep=pvalBothDepletloc))%>% mutate(set="Both")
enrichbothlocdf
loc Actual Expected Enrichment PvalEn
1 cds 35 27.1844577284372 1.28750039267427 0.0612130405890235
2 end 6 17.8462852263023 0.336204421475739 0.999858358871073
3 intron 37 56.7553373185312 0.651921065896284 0.999633975420797
4 utr3 152 132.809564474808 1.14449588477366 0.00527412051670923
5 utr5 13 8.40435525192144 1.54681705024641 0.0703474621902344
PvalDep set
1 0.959605041842023 Both
2 0.000516899738502834 Both
3 0.000683333042635172 Both
4 0.996485012447578 Both
5 0.96410046531306 Both
Join both and plot:
deLocALl=enrichdapalocdf %>% bind_rows(enrichbothlocdf)
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
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
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
deLocALl
loc Actual Expected Enrichment PvalEn
1 cds 77 69.3595217762596 1.11015759665107 0.144559704469824
2 end 26 45.533731853117 0.571005251312828 0.999924050908696
3 intron 134 144.807856532878 0.925364156395589 0.895180830348247
4 utr3 368 338.855678906917 1.08600806451613 0.00346142063512895
5 utr5 15 21.4432109308284 0.699522102747909 0.966377337513707
6 cds 35 27.1844577284372 1.28750039267427 0.0612130405890235
7 end 6 17.8462852263023 0.336204421475739 0.999858358871073
8 intron 37 56.7553373185312 0.651921065896284 0.999633975420797
9 utr3 152 132.809564474808 1.14449588477366 0.00527412051670923
10 utr5 13 8.40435525192144 1.54681705024641 0.0703474621902344
PvalDep set
1 0.886053815761103 dAPAOnly
2 0.000168192607453154 dAPAOnly
3 0.126610345939535 dAPAOnly
4 0.997406075194543 dAPAOnly
5 0.0601740116394651 dAPAOnly
6 0.959605041842023 Both
7 0.000516899738502834 Both
8 0.000683333042635172 Both
9 0.996485012447578 Both
10 0.96410046531306 Both
#ggplot(ResDFfix_both,aes(x=set,group=type,col=set,y=Enrich))+ geom_bar(stat="identity",col="grey",alpha=.3,width=.01)+geom_point(size=10) + coord_flip()+ geom_hline(yintercept = 1) +scale_color_brewer(palette="RdYlBu")+geom_text(col="black",aes(label = round(Enrich,2)))+ facet_grid(~type)+labs(x="Dominance Cutoff", y="Enrichment",title="Enrichment for DE by Domianance") + theme(legend.position = "none")
deLocALl$Enrichment=as.numeric(deLocALl$Enrichment)
ggplot(deLocALl, aes(x=loc, col=loc, y=Enrichment)) + geom_bar(stat="identity",col="grey",alpha=.3,width=.01)+geom_point(size=10) + coord_flip()+ geom_hline(yintercept = 1) +scale_color_brewer(palette="RdYlBu")+geom_text(col="black",aes(label = round(Enrichment,2)))+ facet_grid(~set)
Check any dAPA:
dAPAPAS_wLocAndBoth_either =dAPAPAS_wLocAndBoth %>% mutate(anydAPA=ifelse(Both=="Yes" | dAPAonly=="Yes", "Yes","No"))
enrichEither=c()
pvalEither=c()
pvalEitherDep=c()
expectEither=c()
actualEither=c()
for (i in c("cds", "end", "intron", "utr3", "utr5")){
x=nrow(dAPAPAS_wLocAndBoth_either %>% filter(anydAPA=="Yes", loc==i))
m=nrow(dAPAPAS_wLocAndBoth_either %>% filter(loc==i))
n=nrow(dAPAPAS_wLocAndBoth_either %>% filter(loc!=i))
k=nrow(dAPAPAS_wLocAndBoth_either %>% filter(anydAPA=="Yes"))
N=nrow(dAPAPAS_wLocAndBoth_either)
actualEither=c(actualEither, x)
exp=k*(m/N)
expectEither=c(expectEither,exp)
pvalEitherDep=c(pvalEitherDep, phyper(x,m,n,k,lower.tail=T))
pvalEither=c(pvalEither, phyper(x-1,m,n,k,lower.tail=F))
enrichval=(x/k)/(m/N)
enrichEither=c(enrichEither, enrichval)
}
loc=c("cds", "end", "intron", "utr3", "utr5")
enrichEitherDf=as.data.frame(cbind(loc,Actual=actualEither,Expected=expectEither, Enrichment=enrichEither,PvalEn=pvalEither,PvalDep=pvalEitherDep))%>% mutate(set="Both", Location=c("Coding", "End", "intronic","3'UTR", "5'UTR"))
enrichEitherDf
loc Actual Expected Enrichment PvalEn
1 cds 112 96.5439795046968 1.16009305370048 0.0216944727729284
2 end 32 63.3800170794193 0.504890996793231 0.999999980216832
3 intron 171 201.563193851409 0.848369172628114 0.999236414270241
4 utr3 520 471.665243381725 1.10247682502897 1.85595444296267e-05
5 utr5 28 29.8475661827498 0.938099938486188 0.70650097845013
PvalDep set Location
1 0.984367879949983 Both Coding
2 5.53449882399961e-08 Both End
3 0.00108331305417675 Both intronic
4 0.999987300694221 Both 3'UTR
5 0.379451460165918 Both 5'UTR
Plot:
enrichEitherDf$Enrichment=as.numeric(as.character(enrichEitherDf$Enrichment))
ggplot(enrichEitherDf, aes(x=Location, col=Location, y=Enrichment)) + geom_bar(stat="identity",col="grey",alpha=.3,width=.01)+geom_point(size=10) + coord_flip()+ geom_hline(yintercept = 1) +scale_color_brewer(palette="RdYlBu")+geom_text(col="black",aes(label = round(Enrichment,2))) + theme(legend.position = "none") +labs(title="Genic location enrichment for dAPA PAS in DE genes")
Barplot:
ggplot(enrichEitherDf, aes(x=Location, fill=Location, y=Enrichment)) + geom_bar(stat="identity") +scale_fill_brewer(palette="RdYlBu") + geom_hline(yintercept = 1)+geom_text(col="black",aes(label = round(Enrichment,2), vjust=2)) + theme(legend.position = "none") +labs(title="Genic location enrichment for dAPA PAS in DE genes")
Either dIC and TE:
RiboSmallSig= RiboSmall %>% filter(dTE=='Yes')
BothgeneTE= dAPAandDic %>% filter(Both=="Yes") %>% inner_join(RiboSmallSig, by="gene")
OnlyICgenes=dAPAandDic %>% filter(OnlyIC=="Yes")%>% inner_join(RiboSmallSig, by="gene")
dAPAPAS_wLocAndBothTE= dAPAPAS_wLoc %>% filter(SigPAU2=="Yes") %>% mutate(dIConly=ifelse(gene %in% OnlyICgenes$gene,"Yes","No"), Both=ifelse(gene %in% BothgeneTE$gene, "Yes", "No"))%>% mutate(anydIC=ifelse(Both=="Yes" | dIConly=="Yes", "Yes","No"))
enrichEitherTE=c()
pvalEitherTE=c()
pvalEitherDepTE=c()
expectEitherTE=c()
actualEitherTE=c()
for (i in c("cds", "end", "intron", "utr3", "utr5")){
x=nrow(dAPAPAS_wLocAndBothTE %>% filter(anydIC=="Yes", loc==i))
m=nrow(dAPAPAS_wLocAndBothTE %>% filter(loc==i))
n=nrow(dAPAPAS_wLocAndBothTE %>% filter(loc!=i))
k=nrow(dAPAPAS_wLocAndBothTE %>% filter(anydIC=="Yes"))
N=nrow(dAPAPAS_wLocAndBothTE)
actualEitherTE=c(actualEitherTE, x)
exp=k*(m/N)
expectEitherTE=c(expectEitherTE,exp)
pvalEitherDepTE=c(pvalEitherDepTE, phyper(x,m,n,k,lower.tail=T))
pvalEitherTE=c(pvalEitherTE, phyper(x-1,m,n,k,lower.tail=F))
enrichval=(x/k)/(m/N)
enrichEitherTE=c(enrichEitherTE, enrichval)
}
loc=c("cds", "end", "intron", "utr3", "utr5")
enrichEitherDfTE=as.data.frame(cbind(loc,Actual=actualEitherTE,Expected=expectEitherTE, Enrichment=enrichEitherTE,PvalEn=pvalEitherTE,PvalDep=pvalEitherDepTE))%>% mutate(set="Both", Location=c("Coding", "End", "intronic","3'UTR", "5'UTR"))
enrichEitherDfTE
loc Actual Expected Enrichment PvalEn
1 cds 18 14.8787361229718 1.20978017562991 0.224435998901782
2 end 4 9.76771989752348 0.409512152474209 0.991339820772879
3 intron 21 31.0636208368915 0.676031944578081 0.989569116664339
4 utr3 87 72.6900085397097 1.1968632518797 0.00625939079480355
5 utr5 3 4.5999146029035 0.652186020607073 0.85008031316646
PvalDep set Location
1 0.847506438642276 Both Coding
2 0.0261976848806088 Both End
3 0.0187659660554926 Both intronic
4 0.996328710378025 Both 3'UTR
5 0.313553277553297 Both 5'UTR
enrichEitherDfTE$Enrichment=as.numeric(as.character(enrichEitherDfTE$Enrichment))
ggplot(enrichEitherDfTE, aes(x=Location, fill=Location, y=Enrichment)) + geom_bar(stat="identity") +scale_fill_brewer(palette="RdYlBu") + geom_hline(yintercept = 1)+geom_text(col="black",aes(label = round(Enrichment,2), vjust=2)) + theme(legend.position = "none") +labs(title="Genic location enrichment for dAPA PAS in dT genes")
Version | Author | Date |
---|---|---|
9937c4b | brimittleman | 2020-07-02 |
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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] cowplot_0.9.4 forcats_0.3.0 stringr_1.3.1
[4] dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1
[7] tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[10] tidyverse_1.2.1 VennDiagram_1.6.20 futile.logger_1.4.3
[13] UpSetR_1.3.3 workflowr_1.6.0
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 withr_2.1.2
[13] glue_1.3.0 RColorBrewer_1.1-2 lambda.r_1.2.3
[16] modelr_0.1.2 readxl_1.1.0 plyr_1.8.4
[19] cellranger_1.1.0 munsell_0.5.0 gtable_0.2.0
[22] rvest_0.3.2 evaluate_0.12 labeling_0.3
[25] knitr_1.20 httpuv_1.4.5 broom_0.5.1
[28] Rcpp_1.0.4.6 promises_1.0.1 scales_1.0.0
[31] backports_1.1.2 formatR_1.5 jsonlite_1.6
[34] fs_1.3.1 gridExtra_2.3 hms_0.4.2
[37] digest_0.6.18 stringi_1.2.4 rprojroot_1.3-2
[40] cli_1.1.0 tools_3.5.1 magrittr_1.5
[43] lazyeval_0.2.1 futile.options_1.0.1 crayon_1.3.4
[46] whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[49] lubridate_1.7.4 rstudioapi_0.10 assertthat_0.2.0
[52] rmarkdown_1.10 httr_1.3.1 R6_2.3.0
[55] nlme_3.1-137 git2r_0.26.1 compiler_3.5.1