dAPA dIC together and sep

Last updated: 2020-05-31

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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    Untracked:  output/TEeffectsize
    Untracked:  output/TEeffectsize.pdf
    Untracked:  output/Total_DEeffectsize
    Untracked:  output/Total_DEeffectsize.pdf
    Untracked:  output/Total_DEeffectsizeNotJusttop.pdf
    Untracked:  output/Total_TEeffectsize
    Untracked:  output/Total_TEeffectsize.pdf
    Untracked:  output/Ubiqplot
    Untracked:  output/dAPAandDomEnrich.png
    Untracked:  output/dEandDomEnrich.png
    Untracked:  output/dediffdom.pdf
    Untracked:  output/dpnotDE
    Untracked:  output/dtPlots/
    Untracked:  output/exandte
    Untracked:  output/fig1.pdf
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    Untracked:  output/simpson.pdf
    Untracked:  output/whichSiteplot.pdf
    Untracked:  projectNotes.Rmd
    Untracked:  proteinModelSet.Rmd

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/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

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	033290c	brimittleman	2020-05-31	add main figures 1-4
html	afecfdb	brimittleman	2020-05-20	Build site.
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
afecfdb	brimittleman	2020-05-20
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
afecfdb	brimittleman	2020-05-20
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
afecfdb	brimittleman	2020-05-20
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
afecfdb	brimittleman	2020-05-20
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
afecfdb	brimittleman	2020-05-20
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
afecfdb	brimittleman	2020-05-20
1c7e237	brimittleman	2020-05-09

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

Version	Author	Date
afecfdb	brimittleman	2020-05-20
1c7e237	brimittleman	2020-05-09

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")

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)

Version	Author	Date
afecfdb	brimittleman	2020-05-20
38c4a7f	brimittleman	2020-05-17

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
afecfdb	brimittleman	2020-05-20

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: