Differential Used Intronic

Last updated: 2020-02-27

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dAPAandapaQTL_DF.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd
    Modified:   analysis/speciesSpecific_DF.Rmd
    Modified:   analysis/upsetter_DF.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	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)

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

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

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

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)

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

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)

Version	Author	Date
09ad482	brimittleman	2020-02-24

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)

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

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)

Version	Author	Date
09ad482	brimittleman	2020-02-24

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

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

Version	Author	Date
1d56205	brimittleman	2020-02-27

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

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)

Version	Author	Date
1d56205	brimittleman	2020-02-27

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

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)

Version	Author	Date
1d56205	brimittleman	2020-02-27

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)

Version	Author	Date
09ad482	brimittleman	2020-02-24

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)

Version	Author	Date
09ad482	brimittleman	2020-02-24

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)

Version	Author	Date
09ad482	brimittleman	2020-02-24

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)

Version	Author	Date
1d56205	brimittleman	2020-02-27

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)

Version	Author	Date
09ad482	brimittleman	2020-02-24

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)

Version	Author	Date
1d56205	brimittleman	2020-02-27

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)

Version	Author	Date
09ad482	brimittleman	2020-02-24

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)

Version	Author	Date
1d56205	brimittleman	2020-02-27

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)

Version	Author	Date
09ad482	brimittleman	2020-02-24

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)

Version	Author	Date
1d56205	brimittleman	2020-02-27

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)

Version	Author	Date
09ad482	brimittleman	2020-02-24

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)

Version	Author	Date
1d56205	brimittleman	2020-02-27

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

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.

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] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    tidyverse_1.2.1
 [9] qvalue_2.14.0   limma_3.38.2    ggpubr_0.2      magrittr_1.5   
[13] 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

Ribosome occupancy

Choose most Sig PAS

Protein

Choose most Sig PAS