APA and expression effect size relationship in total

Last updated: 2020-06-09

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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/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
    Modified:   analysis/unliftedsites.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	97539f3	brimittleman	2020-06-09	Seb rev. comments
html	24d63d8	brimittleman	2020-05-28	Build site.
Rmd	1cc5ab7	brimittleman	2020-05-28	add total diff

In this analysis I will call site level dAPA in the total fraction the compare the effect size relationships with the de data.

I ran the original total human v chimp data in https://brimittleman.github.io/Comparative_APA/Total_HvC.html

library(qvalue)
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(ggpubr)

Loading required package: magrittr


Attaching package: 'magrittr'

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

    set_names

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

    extract

library(cowplot)


Attaching package: 'cowplot'

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

    get_legend

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

    ggsave

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_Total/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", 'start','end')) %>% filter(loc %in% c("intron","utr3"))

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

DiffIsoTop %>% group_by(loc) %>% summarise(n())

# A tibble: 2 x 2
  loc    `n()`
  <chr>  <int>
1 intron   751
2 utr3    7310

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) %>% mutate(CorrectedlogFC=-1*logFC)

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)

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

nrow(DeandAPA)

[1] 7366

DeandAPA %>% group_by(loc) %>% summarise(n())

# A tibble: 2 x 2
  loc    `n()`
  <chr>  <int>
1 intron   413
2 utr3    6953

DE_all=ggplot(DeandAPA,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="Total APA v DE", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(label.x = -8,label.y = -1) +theme_classic(base_size = 12)

DE_all

Version	Author	Date
24d63d8	brimittleman	2020-05-28

DE_split=ggplot(DeandAPA,aes(y=deltaPAU, x=CorrectedlogFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="Total APA v DE", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,-1),label.y = -1) +theme_classic(base_size = 12)

DE_split

Version	Author	Date
24d63d8	brimittleman	2020-05-28

DeandAPASig= DeandAPA %>% filter(SigPAU2=="Yes", adj.P.Val<=0.05)

nrow(DeandAPASig)

[1] 352

DeandAPASig %>% group_by(loc) %>% summarise(n())

# A tibble: 2 x 2
  loc    `n()`
  <chr>  <int>
1 intron    24
2 utr3     328

DE_sig_all=ggplot(DeandAPASig,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="TotalSignificant differences\n in Total APA and expression", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(label.x = -8,label.y = -1) +theme_classic(base_size = 12)

DE_sig_all

Version	Author	Date
24d63d8	brimittleman	2020-05-28

DE_sig_split=ggplot(DeandAPASig,aes(y=deltaPAU, x=CorrectedlogFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="Significant differences in\n Total APA and expression", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,-1),label.y = -1) +theme_classic(base_size = 12)

DE_sig_split

Version	Author	Date
24d63d8	brimittleman	2020-05-28

plot_grid(DE_all, DE_split,DE_sig_all,DE_sig_split)

Version	Author	Date
24d63d8	brimittleman	2020-05-28

pdf("../output/Total_DErelationship.pdf", height=8, width=12)
plot_grid(DE_all, DE_split,DE_sig_all,DE_sig_split)
dev.off()

png 
  2

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

nrow(RiboandAPA)

[1] 6389

TE_all=ggplot(RiboandAPA,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="Total APA v TE", x="TE effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(label.x = -8,label.y = -1) +theme_classic(base_size = 12)

TE_all

Version	Author	Date
24d63d8	brimittleman	2020-05-28

TE_split=ggplot(RiboandAPA,aes(y=deltaPAU, x=HvC.beta, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="Total APA v TE", x="TE effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,1),label.y = -1) +theme_classic(base_size = 12)

TE_split

RiboandAPASig= RiboandAPA %>% filter(SigPAU2=="Yes", HvC.FDR<=0.05)

nrow(RiboandAPASig)

[1] 163

TE_sig_all=ggplot(RiboandAPASig,aes(y=deltaPAU, x=HvC.beta)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="Significant differences in Total APA and translation", x="TE effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(label.x = -8,label.y = -1) +theme_classic(base_size = 12)

TE_sig_all

Version	Author	Date
24d63d8	brimittleman	2020-05-28

TE_sig_split=ggplot(RiboandAPASig,aes(y=deltaPAU, x=HvC.beta, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="Significant differences in Total APA and translation", x="TE effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,1),label.y = -1) +theme_classic(base_size = 12)

TE_sig_split

Version	Author	Date
24d63d8	brimittleman	2020-05-28

plot_grid(TE_all, TE_split,TE_sig_all,TE_sig_split)

Make the DE plot without the top used. Count all

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

DeandAPAAll %>% group_by(loc) %>% summarise(n())

# A tibble: 2 x 2
  loc    `n()`
  <chr>  <int>
1 intron 11217
2 utr3   15661

DeandAPAAllsig= DeandAPAAll%>% filter(SigPAU2=="Yes", adj.P.Val<=0.05)

denotsubsig=ggplot(DeandAPAAllsig,aes(y=deltaPAU, x=CorrectedlogFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="Significant differences in Total APA and expression \n(not just top used)", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,-1),label.y = -1) +theme_classic(base_size = 12)


denotsub=ggplot(DeandAPAAll,aes(y=deltaPAU, x=CorrectedlogFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title=" Total APA and expression\n (not just top used)", x="DE log effect size", y="Difference in PAS Usage") + scale_color_brewer(palette = "Set1",name="", labels=c("Intronic", "3' UTR"))+ stat_cor(aes(col=loc),label.x = c(-8,-1),label.y = -1) +theme_classic(base_size = 12)

plot_grid(denotsub,denotsubsig)

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   ggpubr_0.2      magrittr_1.5    forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1    
 [9] tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1
[13] qvalue_2.14.0  

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] utf8_1.1.4         rlang_0.4.0        later_0.7.5       
[13] pillar_1.3.1       withr_2.1.2        glue_1.3.0        
[16] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[19] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[22] workflowr_1.6.0    cellranger_1.1.0   rvest_0.3.2       
[25] evaluate_0.12      labeling_0.3       knitr_1.20        
[28] httpuv_1.4.5       fansi_0.4.0        broom_0.5.1       
[31] Rcpp_1.0.4.6       promises_1.0.1     scales_1.0.0      
[34] backports_1.1.2    jsonlite_1.6       fs_1.3.1          
[37] hms_0.4.2          digest_0.6.18      stringi_1.2.4     
[40] grid_3.5.1         rprojroot_1.3-2    cli_1.1.0         
[43] tools_3.5.1        lazyeval_0.2.1     crayon_1.3.4      
[46] whisker_0.3-2      pkgconfig_2.0.2    xml2_1.2.0        
[49] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[52] httr_1.3.1         rstudioapi_0.10    R6_2.3.0          
[55] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1

APA and expression effect size relationship in total

Briana Mittleman

5/28/2020