Differential Used Intronic

Last updated: 2020-05-08

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Knit directory: Comparative_APA/analysis/

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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/SetsdAPADIC.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/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.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	715fb50	brimittleman	2020-05-08	fix plots
html	97e62e1	brimittleman	2020-05-04	Build site.
Rmd	a3de5b0	brimittleman	2020-05-04	change colors
html	6d8725a	brimittleman	2020-04-10	Build site.
html	c2a0778	brimittleman	2020-04-06	Build site.
Rmd	9676d72	brimittleman	2020-04-06	updated anno
html	c9c6b6a	brimittleman	2020-02-29	Build site.
Rmd	8366a88	brimittleman	2020-02-29	add cowplots
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Rmd	0ef2c6d	brimittleman	2020-02-27	add protien res
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Rmd	cc9f594	brimittleman	2020-02-27	add more plots for meeting
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Rmd	7385496	brimittleman	2020-02-24	add correlations plotted by location
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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 ──

✔ tibble  2.1.1       ✔ purrr   0.3.2  
✔ 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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library(cowplot)


Attaching package: 'cowplot'

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

    get_legend

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

    ggsave

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_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)  %>% dplyr::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")

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) %>% dplyr::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") %>% dplyr::rename('gene'=Gene.name) %>% dplyr::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 = "Set1")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29
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 = "Set1")+ stat_cor( label.x = 3)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29
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 = "Set1")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor(label.x = 3)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor( label.x = 3)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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] 1313

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

[1] 161

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

[1] 6075

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

[1] 826

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 = "Set1") + stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29
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)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29
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 = "Set1")+ stat_cor( label.x = 3)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29
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 = "Set1")+ stat_cor(label.x = 3)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

Plot together:

allboth=ggplot(DeandAPA,aes(y=deltaPAU, x=logFC)) + geom_point(alpha=.3) + geom_smooth( method="lm") + labs(title="All APA and all expression") + scale_color_brewer(palette = "Set1")+ stat_cor(label.x = c(-6), label.y=-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="All APA and all expression") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR")) + stat_cor(aes(color = loc), label.x = c(-8,1),label.y=-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 and all Expression")+ scale_color_brewer(palette = "Set1")+ stat_cor(label.x = -8,label.y=-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 and all Expressio ") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(color = loc), label.x = c(-8,1),label.y=-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 and Significant DE") + scale_color_brewer(palette = "Set1")+ stat_cor( label.x = -8,label.y=-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 and Significant DE") +  scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(color = loc), label.x = c(-8,1),label.y=-1)+theme_classic(base_size = 12)

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

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29
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") %>% dplyr::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] 23590

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 = "Set1")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor( label.x = 3)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor( label.x = 3)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor(label.x = 3)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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] 9994

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

[1] 1138

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

[1] 13596

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

[1] 5269

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 = "Set1")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor(label.x = 3)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

Sig APA

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

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

[1] 136

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

[1] 675

199 intronic significant, 910 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 = "Set1")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor(label.x = 3)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

Sig both:

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

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

[1] 37

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

[1] 177

47 PAS for intrnic 229 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 = "Set1")+ stat_cor(aes(color = loc), label.x = 3)

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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 = "Set1")+ stat_cor(label.x = 3)

Version	Author	Date
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

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="All APA and translation values")+ scale_color_brewer(palette = "Set1")+ stat_cor( label.x = c( -8, 5), label.y = -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="All APA and translation values")+ scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3'"))+ stat_cor(aes(color = loc), label.x =c( -8, 1),label.y = -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 and all Translation")+ scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor( label.x = -8,label.y = -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 and Significant Translation") +scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(color = loc), label.x =c( -8, 1),label.y = -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 and all Translation") + scale_color_brewer(palette = "Set1", name="")+ stat_cor(label.x = -8,label.y = -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 and Significant Translation") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(color = loc), label.x = c( -8, 1),label.y = -1)+theme_classic(base_size = 12)

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

Version	Author	Date
97e62e1	brimittleman	2020-05-04
6d8725a	brimittleman	2020-04-10
c2a0778	brimittleman	2020-04-06
c9c6b6a	brimittleman	2020-02-29

plot_grid(riboTop, ribosigapasep,sigapasigribosep)

Version	Author	Date
c9c6b6a	brimittleman	2020-02-29
0a68177	brimittleman	2020-02-27

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)

Version	Author	Date
c9c6b6a	brimittleman	2020-02-29
0a68177	brimittleman	2020-02-27

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") %>% dplyr::rename('gene'=Gene.name) %>% dplyr::select(-Gene_stable_ID)

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

write.table(effectsizeDF, "../data/Khan_prot/ProtData_effectSize.txt",col.names = T, row.names = F, quote = F)

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

[1] 2611

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

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

Looking at 2557 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 = "Set1")+ stat_cor(aes(color = loc), label.x = 1)

Version	Author	Date
c9c6b6a	brimittleman	2020-02-29
0a68177	brimittleman	2020-02-27

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 = "Set1")+ stat_cor( label.x = 1)

Version	Author	Date
c9c6b6a	brimittleman	2020-02-29
0a68177	brimittleman	2020-02-27

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 = "Set1")+ stat_cor(aes(color = loc), label.x = 1)

Version	Author	Date
c9c6b6a	brimittleman	2020-02-29
0a68177	brimittleman	2020-02-27

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 = "Set1")+ stat_cor( label.x = 1)

Version	Author	Date
c9c6b6a	brimittleman	2020-02-29
0a68177	brimittleman	2020-02-27

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 = "Set1")+ stat_cor(aes(color = loc), label.x = 1)

Version	Author	Date
c9c6b6a	brimittleman	2020-02-29
0a68177	brimittleman	2020-02-27

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 = "Set1")+ stat_cor( label.x = 1)

Version	Author	Date
c9c6b6a	brimittleman	2020-02-29
0a68177	brimittleman	2020-02-27

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 = "Set1")+ 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 = "Set1")+ 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 = "Set1")+ stat_cor(aes(color = loc), label.x = 1)

Version	Author	Date
c9c6b6a	brimittleman	2020-02-29
0a68177	brimittleman	2020-02-27

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 = "Set1")+ stat_cor( label.x = 1)

Version	Author	Date
c9c6b6a	brimittleman	2020-02-29
0a68177	brimittleman	2020-02-27

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 = "Set1")+ 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 = "Set1")+ 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] 2666

2607 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 = "Set1")+ 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 = "Set1")+ 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 = "Set1")+ 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 = "Set1")+ 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 = "Set1")+ 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 = "Set1")+ 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 = "Set1")+ 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 = "Set1")+ 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_1.0.4.6       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: