dAPA dIC together and sep

Last updated: 2020-05-20

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DiffTop2SecondDom.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.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/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/signalsites_doublefilter.Rmd
    Modified:   analysis/speciesSpecific.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.

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File	Version	Author	Date	Message
Rmd	3cd8e7c	brimittleman	2020-05-20	update figures for new levels
html	38c4a7f	brimittleman	2020-05-17	Build site.
Rmd	4b2ff4a	brimittleman	2020-05-17	add loc enrich and diff dom plot
html	85826ad	brimittleman	2020-05-11	Build site.
Rmd	fc1a2bd	brimittleman	2020-05-11	add directional selection res
html	1c7e237	brimittleman	2020-05-09	Build site.
Rmd	f11ba50	brimittleman	2020-05-09	add ex
html	747e064	brimittleman	2020-05-07	Build site.
Rmd	88c1a33	brimittleman	2020-05-07	plot without protien
html	3fe9930	brimittleman	2020-05-07	Build site.
Rmd	341a5c0	brimittleman	2020-05-07	add seperation with dapa and dic

library(workflowr)

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

library(UpSetR)
library(VennDiagram)

Loading required package: grid

Loading required package: futile.logger

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(cowplot)


Attaching package: 'cowplot'

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

    ggsave

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")

Version	Author	Date
1c7e237	brimittleman	2020-05-09

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
1c7e237	brimittleman	2020-05-09

Expression

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

Translation

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

Protein

(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

Plot together:

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

Version	Author	Date
1c7e237	brimittleman	2020-05-09
747e064	brimittleman	2020-05-07

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
1c7e237	brimittleman	2020-05-09
747e064	brimittleman	2020-05-07

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
1c7e237	brimittleman	2020-05-09

plot together:

plot_grid(enrichpoint,pvalplot, nrow=2)

Version	Author	Date
1c7e237	brimittleman	2020-05-09
747e064	brimittleman	2020-05-07

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

Version	Author	Date
1c7e237	brimittleman	2020-05-09

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
1c7e237	brimittleman	2020-05-09

exandte=plot_grid(enrichpointnoP,pvalplotnoP, nrow=2)
exandte

Version	Author	Date
1c7e237	brimittleman	2020-05-09

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")

Selection sets from Khan

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

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)

location enrichment:

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")

Version	Author	Date
38c4a7f	brimittleman	2020-05-17

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")

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

dAPA dIC together and sep

Briana Mittleman

5/7/2020

Expression

Translation

Protein

Plot together:

Selection sets from Khan

location enrichment: