Differential Used Intronic

Last updated: 2020-02-29

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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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	8366a88	brimittleman	2020-02-29	add cowplots
html	0a68177	brimittleman	2020-02-27	Build site.
Rmd	0ef2c6d	brimittleman	2020-02-27	add protien res
html	1d56205	brimittleman	2020-02-27	Build site.
Rmd	cc9f594	brimittleman	2020-02-27	add more plots for meeting
html	09ad482	brimittleman	2020-02-24	Build site.
Rmd	7385496	brimittleman	2020-02-24	add correlations plotted by location
html	5f821ee	brimittleman	2020-02-23	Build site.
Rmd	f4ae857	brimittleman	2020-02-23	wflow_publish(c(“analysis/index.Rmd”, “analysis/DiffUsedIntronic.Rmd”))

library(workflowr)

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

library(ggpubr)

Loading required package: ggplot2

Loading required package: magrittr

library(limma)
library(qvalue)
library(tidyverse)

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library(cowplot)


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For this analysis I will look at the differentially used PAS in introns and ask if I can used information from DE and dribosome to better understand these. I subset intornic because I believe the intronic and utr mechanisms are different.

Meta=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)  %>% select(PAS, chr, start,end, loc)
DiffIso= read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", 'start','end')) %>% filter(loc %in% c("intron","utr3"))
DiffIsoSig= DiffIso %>% filter(SigPAU2=="Yes")

742 of the 11228 intronic PAS are significant.

1659 of the 17012 3’ UTR PAS are significant.

I can compare the effect sizes with these genes in the DE.

Compare with expression

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

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

First do all of the genes:

DeandAPA= DiffIso %>% inner_join(DE, by="gene")

This pas I will include each PAS

ggplot(DeandAPA,aes(y=deltaPAU, x=logFC,col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc), method="lm") + labs(title="Intronic and 3' UTR APA v DE") + scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
09ad482	brimittleman	2020-02-24
5f821ee	brimittleman	2020-02-23

ggplot(DeandAPA,aes(y=deltaPAU, x=logFC)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="Intronic and 3' UTR APA v DE") + scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 3)

Version	Author	Date
09ad482	brimittleman	2020-02-24

Just the genes with significant differences in PAS

DeandAPA_sigAPA= DeandAPA %>% filter(SigPAU2=="Yes")

ggplot(DeandAPA_sigAPA,aes(y=deltaPAU, x=logFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant Intronic and 3' UTR APA v DE")+ scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 3)

ggplot(DeandAPA_sigAPA,aes(y=deltaPAU, x=logFC)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant Intronic and 3' UTR APA v DE")+ scale_color_brewer(palette = "Dark2")+ stat_cor(label.x = 3)

Sig both:

DeandAPA_sigAPAandE= DeandAPA %>% filter(SigPAU2=="Yes",  adj.P.Val<.05)

ggplot(DeandAPA_sigAPAandE,aes(y=deltaPAU, x=logFC,col=loc)) + geom_point() + geom_smooth(method="lm")+ labs(title="Significant Intronic and 3' UTR APA v Significant DE") + scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 3)

ggplot(DeandAPA_sigAPAandE,aes(y=deltaPAU, x=logFC)) + geom_point() + geom_smooth(method="lm")+ labs(title="Significant Intronic and 3' UTR APA v Significant DE") + scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 3)

Choose most Sig PAS

To break ties I will use the top average usage. I will not worry about location when chosing top PAS.

DeandAPA_topPAS= DeandAPA %>% mutate(AvgUsageBoth=(Human+Chimp)/2) %>% group_by(gene) %>% arrange(p.adjust,desc(AvgUsageBoth)) %>% slice(1) %>% ungroup()

#intron
nrow(DeandAPA_topPAS %>% filter(loc=="intron"))

[1] 1358

nrow(DeandAPA_topPAS %>% filter(loc=="intron", SigPAU2=="Yes"))

[1] 221

#3 utr
nrow(DeandAPA_topPAS %>% filter(loc=="utr3"))

[1] 5993

nrow(DeandAPA_topPAS %>% filter(loc=="utr3", SigPAU2=="Yes"))

[1] 1088

from 11228 intronic to 1358 PAS (12%) from 742 to 221 significant (30%)

from 17012 to 5993 3’ UTR PAS. (35%) from 1659 to 1088 significant (66%)

Plot the correlation in effect size

ggplot(DeandAPA_topPAS,aes(y=deltaPAU, x=logFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")  + labs(title="Intronic and 3' UTR APA top PAS v DE") + scale_color_brewer(palette = "Dark2") + stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
09ad482	brimittleman	2020-02-24
5f821ee	brimittleman	2020-02-23

ggplot(DeandAPA_topPAS,aes(y=deltaPAU, x=logFC)) + geom_point(alpha=.3) + geom_smooth(method="lm")  + stat_cor(label.x = 3)

DeandAPA_topPASsigAPA= DeandAPA_topPAS %>% filter(SigPAU2=="Yes")

ggplot(DeandAPA_topPASsigAPA,aes(y=deltaPAU, x=logFC,col=loc)) + geom_point(alpha=.4) + geom_smooth(method="lm") + labs(title="Significant APA, Top PAS v DE ") + scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
09ad482	brimittleman	2020-02-24
5f821ee	brimittleman	2020-02-23

ggplot(DeandAPA_topPASsigAPA,aes(y=deltaPAU, x=logFC)) + geom_point(alpha=.4) + geom_smooth(method="lm") + labs(title="Significant APA, Top PAS v DE ") + scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 3)

Sig both:

DeandAPA_topPASsigAPAandE= DeandAPA_topPASsigAPA %>% filter(SigPAU2=="Yes",  adj.P.Val<.05)

ggplot(DeandAPA_topPASsigAPAandE,aes(y=deltaPAU, x=logFC,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA, Top PAS  v Significant DE") +  scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
09ad482	brimittleman	2020-02-24
5f821ee	brimittleman	2020-02-23

ggplot(DeandAPA_topPASsigAPAandE,aes(y=deltaPAU, x=logFC)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA, Top PAS  v Significant DE") +  scale_color_brewer(palette = "Dark2")+ stat_cor(label.x = 3)

Plot together:

allboth=ggplot(DeandAPA,aes(y=deltaPAU, x=logFC)) + geom_point(alpha=.3) + geom_smooth( method="lm") + labs(title="APA v DE") + scale_color_brewer(palette = "Dark2")+ stat_cor(label.x = 1) +theme_classic(base_size = 12) 
allSep= ggplot(DeandAPA_topPAS,aes(y=deltaPAU, x=logFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")  + labs(title="APA, top PAS v DE") + scale_color_brewer(palette = "Dark2") + stat_cor(aes(color = loc), label.x = 1)+theme_classic(base_size = 12) 
sigAPAboth=ggplot(DeandAPA_sigAPA,aes(y=deltaPAU, x=logFC)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v DE")+ scale_color_brewer(palette = "Dark2")+ stat_cor(label.x = 1)+theme_classic(base_size = 12) 
sigAPSep=ggplot(DeandAPA_topPASsigAPA,aes(y=deltaPAU, x=logFC,col=loc)) + geom_point(alpha=.4) + geom_smooth(method="lm") + labs(title="Significant APA, Top PAS v DE ") + scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)+theme_classic(base_size = 12) 
SigBoth= ggplot(DeandAPA_sigAPAandE,aes(y=deltaPAU, x=logFC)) + geom_point() + geom_smooth(method="lm")+ labs(title="Significant APA v Significant DE") + scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 1)+theme_classic(base_size = 12) 
SigSep=ggplot(DeandAPA_topPASsigAPAandE,aes(y=deltaPAU, x=logFC,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA, Top PAS  v Significant DE") +  scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)+theme_classic(base_size = 12)

plot_grid(allboth,allSep,sigAPAboth,sigAPSep,SigBoth,SigSep, ncol=2)

Version	Author	Date
09ad482	brimittleman	2020-02-24

Ribosome occupancy

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") %>% select(Gene.name, HvC.beta, HvC.pvalue, HvC.FDR) %>% rename("gene"=Gene.name)

Join with APA

RiboandAPA=DiffIso %>% inner_join(Ribo, by="gene")

RiboandAPA %>% group_by(gene) %>% n_distinct()

[1] 21159

ggplot(RiboandAPA,aes(y=deltaPAU, x=HvC.beta, col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="APA v Ribosome Occupany")+ scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 3)

ggplot(RiboandAPA,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="APA v Ribosome Occupany")+ scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 3)

Just the genes with significant differences in PAS

RiboandAPA_sigAPA= RiboandAPA %>% filter(SigPAU2=="Yes")

ggplot(RiboandAPA_sigAPA,aes(y=deltaPAU, x=HvC.beta,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Ribosome Occupany")+ scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 3)

ggplot(RiboandAPA_sigAPA,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Ribosome Occupany")+ scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 3)

Sig both:

RiboandAPA_sigAPAandR= RiboandAPA_sigAPA %>% filter(SigPAU2=="Yes",  HvC.FDR<.05)

ggplot(RiboandAPA_sigAPAandR,aes(y=deltaPAU, x=HvC.beta,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Significant Ribosome Occupany") + scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 3)

ggplot(RiboandAPA_sigAPAandR,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Significant Ribosome Occupany") + scale_color_brewer(palette = "Dark2")+ stat_cor(label.x = 3)

The correlation in expression with intronic is not there in ribosome occupancy.

Choose most Sig PAS

To break ties I will use the top average usage. I will not worry about location at first.

RiboandAPA_topPAS= RiboandAPA %>% mutate(AvgUsageBoth=(Human+Chimp)/2) %>% group_by(gene) %>% arrange(p.adjust,desc(AvgUsageBoth)) %>% slice(1) %>% ungroup()

 

nrow(RiboandAPA %>% filter(loc=="intron"))

[1] 7405

nrow(RiboandAPA_topPAS %>% filter(loc=="intron"))

[1] 1180

nrow(RiboandAPA %>% filter(loc=="utr3"))

[1] 13754

nrow(RiboandAPA_topPAS %>% filter(loc=="utr3"))

[1] 5179

UTR and ribo: - All 13754 - in top used set- 5179

itron and ribo: - All- 7405 - in top used set- 1180

Plot the correlation in effect size

ggplot(RiboandAPA_topPAS,aes(y=deltaPAU, x=HvC.beta,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")  + labs(title="APA, top PAS v Ribosome Occupany")+ scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 3)

ggplot(RiboandAPA_topPAS,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm")  + labs(title="APA, top PAS v Ribosome Occupany")+ scale_color_brewer(palette = "Dark2")+ stat_cor(label.x = 3)

Sig APA

RiboandAPA_topPASsigAPA= RiboandAPA_topPAS %>% filter(SigPAU2=="Yes")

nrow(RiboandAPA_topPASsigAPA %>% filter(loc=="intron"))

[1] 192

nrow(RiboandAPA_topPASsigAPA %>% filter(loc=="utr3"))

[1] 908

192 intronic significant, 908 significant 3’ utr

ggplot(RiboandAPA_topPASsigAPA,aes(y=deltaPAU, x=HvC.beta,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant  APA, top PAS v Ribosome Occupany") +scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 3)

ggplot(RiboandAPA_topPASsigAPA,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant  APA, top PAS v Ribosome Occupany") +scale_color_brewer(palette = "Dark2")+ stat_cor(label.x = 3)

Sig both:

RiboandAPA_topPASsigAPAandR= RiboandAPA_topPASsigAPA %>% filter(SigPAU2=="Yes",  HvC.FDR<.05)

nrow(RiboandAPA_topPASsigAPAandR %>% filter(loc=="intron"))

[1] 43

nrow(RiboandAPA_topPASsigAPAandR %>% filter(loc=="utr3"))

[1] 227

43 PAS for intrnic 227 for 3’ UTR

ggplot(RiboandAPA_topPASsigAPAandR,aes(y=deltaPAU, x=HvC.beta,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant  APA, top PAS  v Significant Ribosome Occupany") + scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 3)

ggplot(RiboandAPA_topPASsigAPAandR,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant  APA, top PAS  v Significant Ribosome Occupany") + scale_color_brewer(palette = "Dark2")+ stat_cor(label.x = 3)

Correlation in UTR but not intronic. Not sure if this is due to the number of PAS.

Plot all together

riboBoth=ggplot(RiboandAPA,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="APA v Ribosome Occupany")+ scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 1)+theme_classic(base_size = 12) 
riboTop=ggplot(RiboandAPA_topPAS,aes(y=deltaPAU, x=HvC.beta,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")  + labs(title="APA, top PAS v Ribosome Occupany")+ scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)+theme_classic(base_size = 12) 
ribosigapaboth=ggplot(RiboandAPA_sigAPA,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Ribosome Occupany")+ scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 1)+theme_classic(base_size = 12) 

ribosigapasep=ggplot(RiboandAPA_topPASsigAPA,aes(y=deltaPAU, x=HvC.beta,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant  APA, top PAS v Ribosome Occupany") +scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)+theme_classic(base_size = 12) 

sigapasigriboboth=ggplot(RiboandAPA_sigAPAandR,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Significant Ribosome Occupany") + scale_color_brewer(palette = "Dark2")+ stat_cor(label.x = 1)+theme_classic(base_size = 12) 

sigapasigribosep=ggplot(RiboandAPA_topPASsigAPAandR,aes(y=deltaPAU, x=HvC.beta,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant  APA, top PAS  v Significant Ribosome Occupany") + scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)+theme_classic(base_size = 12)

plot_grid(riboBoth,riboTop,ribosigapaboth, ribosigapasep,sigapasigriboboth,sigapasigribosep, ncol=2)

Protein

I will use code from https://github.com/siddisis/project_primate_ribo to fit the linear model again and get effect sizes.

load(“../tables/fileS4.RData”)

load(“../rdas/HCR.protein.TMM.RData”)

Put both of these in ../data/Khan_prot

load("../data/Khan_prot/fileS4.RData")

load("../data/Khan_prot/HCR.protein.TMM.RData")

expressed.gene.names <- as.character(HCR.protein.TMM.norm.ESNGlabeled[rownames(HCR.protein.TMM.norm.ESNGlabeled) %in% rownames(protein.expressed.data),16])
names(expressed.gene.names) <- rownames(protein.expressed.data)

Use to make design matrix

# HvC 
RNA.expressed.data.HC<-RNA.expressed.data[,1:10]



species.label <- substring(colnames(RNA.expressed.data.HC),1,1)

design <- model.matrix(~species.label)
colnames(design)<-c("Chimp","Human")

Protien

protein.expressed.data.HC<-protein.expressed.data[,1:10]

protein.fit<-lmFit(protein.expressed.data.HC ,design = design)


HvC.prot<- eBayes(protein.fit)

top.table <- topTable(HvC.prot, n = Inf)

volcanoplot(HvC.prot,coef=2,highlight=2)

effectsizeDF= as.data.frame(cbind(Gene_stable_ID=rownames(protein.expressed.data.HC),logEf=HvC.prot$coefficients[,2], pval=top.table$adj.P.Val))  %>% inner_join(nameID,by="Gene_stable_ID") %>% rename('gene'=Gene.name) %>% select(-Gene_stable_ID)

Warning: Column `Gene_stable_ID` joining factor and character vector,
coercing into character vector

DPandAPA= DiffIso %>% inner_join(effectsizeDF, by="gene")
DPandAPA %>% group_by(gene) %>% summarise(n()) %>% nrow()

[1] 2568

DPandAPA$logEf= as.numeric(as.character(DPandAPA$logEf))

DPandAPA$pval= as.numeric(as.character(DPandAPA$pval))

Looking at 2568 common genes.

ggplot(DPandAPA,aes(y=deltaPAU, x=logEf,col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc), method="lm") + labs(title="Intronic and 3' UTR APA v DP") + scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 2)

ggplot(DPandAPA,aes(y=deltaPAU, x=logEf)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="Intronic and 3' UTR APA v DP") + scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 2)

Just the genes with significant differences in PAS

PandAPA_sigAPA= DPandAPA %>% filter(SigPAU2=="Yes")

ggplot(PandAPA_sigAPA,aes(y=deltaPAU, x=logEf,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)

ggplot(PandAPA_sigAPA,aes(y=deltaPAU, x=logEf)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 1)

Sig both:

PandAPA_sigAPAandP= PandAPA_sigAPA %>% filter(SigPAU2=="Yes", pval <.05)

ggplot(PandAPA_sigAPAandP,aes(y=deltaPAU, x=logEf,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Signficant Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)

ggplot(PandAPA_sigAPAandP,aes(y=deltaPAU, x=logEf)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Signficant Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 1)

Choose most Sig PAS

To break ties I will use the top average usage. I will not worry about location at first.

PandAPA_topPAS= DPandAPA %>% mutate(AvgUsageBoth=(Human+Chimp)/2) %>% group_by(gene) %>% arrange(p.adjust,desc(AvgUsageBoth)) %>% slice(1) %>% ungroup()

ggplot(PandAPA_topPAS,aes(y=deltaPAU, x=logEf,col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc), method="lm") + labs(title="Intronic and 3' UTR APA, top PAS v DP") + scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)

ggplot(PandAPA_topPAS,aes(y=deltaPAU, x=logEf)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="Intronic and 3' UTR APA v DP") + scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 1)

PandAPA_topPAS_sigAPA= PandAPA_topPAS %>% filter(SigPAU2=="Yes")

ggplot(PandAPA_topPAS_sigAPA,aes(y=deltaPAU, x=logEf,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA, top PAS v Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)

ggplot(PandAPA_topPAS_sigAPA,aes(y=deltaPAU, x=logEf)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA top PAS v Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 1)

Sig both:

PandAPA_topPAS_sigAPAandP= PandAPA_topPAS_sigAPA %>% filter(SigPAU2=="Yes", pval <.05)

ggplot(PandAPA_topPAS_sigAPAandP,aes(y=deltaPAU, x=logEf,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA top PAS v Signficant Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)

ggplot(PandAPA_topPAS_sigAPAandP,aes(y=deltaPAU, x=logEf)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA top PAS v Signficant Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 1)

Check pvalues:

protKhan=read.csv("../data/Khan_prot/Khan_TableS4.csv",header = T)  %>% rename("Gene_stable_ID"= ENSG) %>% inner_join(nameID,by="Gene_stable_ID")  %>% rename("gene"=Gene.name)

Warning: Column `Gene_stable_ID` joining factor and character vector,
coercing into character vector

protKhanwmine= protKhan %>% inner_join(effectsizeDF, by="gene")
protKhanwmine$logEf=as.numeric(as.character(protKhanwmine$logEf))

protKhanwmine$pval=as.numeric(as.character(protKhanwmine$pval))
cor.test(protKhanwmine$pval,protKhanwmine$HC.pvalues.protein )


    Pearson's product-moment correlation

data:  protKhanwmine$pval and protKhanwmine$HC.pvalues.protein
t = -0.71198, df = 3248, p-value = 0.4765
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.04685410  0.02189991
sample estimates:
        cor 
-0.01249186

This is not good. Try the difference in means approach:

Chimp-human

protKhanSmall= protKhan %>% select(gene,mean.H.protein,mean.C.protein, HC.qvalues.rna) %>% mutate(Effect=mean.C.protein-mean.H.protein)

deltaPandAPA= DiffIso %>% inner_join(protKhanSmall, by="gene")
deltaPandAPA %>% group_by(gene) %>% summarise(n()) %>% nrow()

[1] 2620

2620 genes

ggplot(deltaPandAPA,aes(y=deltaPAU, x=Effect,col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc), method="lm") + labs(title="Intronic and 3' UTR APA v DP") + scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 2)

ggplot(deltaPandAPA,aes(y=deltaPAU, x=Effect)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="Intronic and 3' UTR APA v DP") + scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 2)

Correlation effect sizes:

protKhanSmall_withmone= protKhanSmall %>% inner_join(effectsizeDF, by="gene")
protKhanSmall_withmone$logEf=as.numeric(as.character(protKhanSmall_withmone$logEf))


cor.test(protKhanSmall_withmone$logEf, protKhanSmall_withmone$Effect)


    Pearson's product-moment correlation

data:  protKhanSmall_withmone$logEf and protKhanSmall_withmone$Effect
t = -11459, df = 3248, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9999885 -0.9999868
sample estimates:
       cor 
-0.9999876

Ok this is equal but opposite. So this is correct.

I need to check the direction of the effects.

Plot together:

protboth=ggplot(DPandAPA,aes(y=deltaPAU, x=logEf)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="APA v Protein") + scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 2)+theme_classic(base_size = 12) 
protsep=ggplot(PandAPA_topPAS,aes(y=deltaPAU, x=logEf,col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc), method="lm") + labs(title="APA, top PAS  v Protein") + scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)+theme_classic(base_size = 12) 
protsigapa=ggplot(PandAPA_sigAPA,aes(y=deltaPAU, x=logEf)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 1)+theme_classic(base_size = 12) 
protsigapasep=ggplot(PandAPA_topPAS_sigAPA,aes(y=deltaPAU, x=logEf,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA, top PAS v Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)+theme_classic(base_size = 12) 
protsigall=ggplot(PandAPA_sigAPAandP,aes(y=deltaPAU, x=logEf)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA v Signficant Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor( label.x = 1)+theme_classic(base_size = 12) 
protsigallsep=ggplot(PandAPA_topPAS_sigAPAandP,aes(y=deltaPAU, x=logEf,col=loc)) + geom_point(alpha=.3) + geom_smooth(method="lm")+ labs(title="Significant APA top PAS v Signficant Protein")+ scale_color_brewer(palette = "Dark2")+ stat_cor(aes(color = loc), label.x = 1)+theme_classic(base_size = 12)

plot_grid(protboth,protsep,protsigapa,protsigapasep,protsigall,protsigallsep, ncol=2)

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] cowplot_0.9.4   forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] tidyverse_1.2.1 qvalue_2.14.0   limma_3.38.2    ggpubr_0.2     
[13] magrittr_1.5    ggplot2_3.1.1   workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   reshape2_1.4.3     splines_3.5.1     
 [4] haven_1.1.2        lattice_0.20-38    colorspace_1.3-2  
 [7] generics_0.0.2     htmltools_0.3.6    yaml_2.2.0        
[10] rlang_0.4.0        later_0.7.5        pillar_1.3.1      
[13] glue_1.3.0         withr_2.1.2        RColorBrewer_1.1-2
[16] modelr_0.1.2       readxl_1.1.0       plyr_1.8.4        
[19] munsell_0.5.0      gtable_0.2.0       cellranger_1.1.0  
[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_1.0.2         promises_1.0.1     scales_1.0.0      
[31] backports_1.1.2    jsonlite_1.6       fs_1.3.1          
[34] hms_0.4.2          digest_0.6.18      stringi_1.2.4     
[37] grid_3.5.1         rprojroot_1.3-2    cli_1.1.0         
[40] tools_3.5.1        lazyeval_0.2.1     crayon_1.3.4      
[43] whisker_0.3-2      pkgconfig_2.0.2    xml2_1.2.0        
[46] lubridate_1.7.4    rstudioapi_0.10    assertthat_0.2.0  
[49] rmarkdown_1.10     httr_1.3.1         R6_2.3.0          
[52] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1

Differential Used Intronic

Briana Mittleman

2/23/2020

Compare with expression

Choose most Sig PAS

Plot together:

Ribosome occupancy

Choose most Sig PAS

Plot all together

Protein

Choose most Sig PAS

Plot together: