UTR and intronic switches

Last updated: 2020-05-23

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Knit directory: Comparative_APA/analysis/

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File	Version	Author	Date	Message
Rmd	34708c2	brimittleman	2020-05-23	check switch genes

I want to look at a set of genes where there is a switch between a UTR and intronic site. This means i am looking for genes with a signficant difference in an intronic PAS and a 3’ UTR.

library(workflowr)

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

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

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    extract

library(cowplot)


Attaching package: 'cowplot'

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Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% select(chr,start,end, gene, PAS, loc)

DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta,by=c("chr", 'start', 'end','gene'))


DiffIso_sig= DiffIso %>% filter(SigPAU2=="Yes") 

DiffIso_sig_geneloc= DiffIso_sig %>% group_by(gene,loc) %>% summarise(nsite=n()) %>% ungroup() %>%  group_by(gene) %>% summarise(locList=paste(loc, collapse = ",")) %>% filter(locList=="intron,utr3")

Select these genes and plot with expression:

DiffIso_utrinton=DiffIso %>% filter(gene %in% DiffIso_sig_geneloc$gene)

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)

DEandAPA=DE %>% inner_join(DiffIso_utrinton,by="gene")

DEandAPAIntron=DEandAPA %>% filter(loc=="intron")
DEandAPAUTR=DEandAPA %>% filter(loc=="utr3")

intronplot=ggplot(DEandAPAIntron,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth( method="lm") + labs(title="Significant Intronic APA v DE\n141 genes with a significant difference \nin intronic and 3' UTR PAS", x="DE log effect size", y="Differnece in PAS usage") + stat_cor(label.x = -4,col="blue")


utrplot=ggplot(DEandAPAUTR,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth( method="lm") + labs(title="Significant 3' UTR APA v DE\n 141 genes with a significant difference \nin intronic and 3' UTR PAS",x="DE log effect size", y="Differnece in PAS usage") + stat_cor(label.x = -4,col="blue")

plot_grid(intronplot,utrplot)

3’ UTR PAS are stronger

what about genes with only significant UTR pas:

DiffIsoUTRgenes=DiffIso %>% filter(SigPAU2=="Yes") %>% group_by(gene,loc) %>% summarise(nsite=n()) %>% ungroup() %>%  group_by(gene) %>% summarise(locList=paste(loc, collapse = ",")) %>% filter(locList=="utr3")

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

DiffIsoUTRDE= DiffIso_mostused %>% filter(gene %in% DiffIsoUTRgenes$gene) %>% inner_join(DE,by="gene")

allonlyutr=ggplot(DiffIsoUTRDE,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth( method="lm") + labs(title="Top used 3' UTR APA v DE\n 763 genes with only significant differnces in 3'UTR",x="DE log effect size", y="Differnece in PAS usage") + stat_cor(label.x = -4,col="blue")
allonlyutr

Filter sig:

DiffIsoUTRDE_sig=DiffIsoUTRDE %>% filter(SigPAU2=="Yes")

sigutronly=ggplot(DiffIsoUTRDE_sig,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth( method="lm") + labs(title="Significant Most used 3' UTR APA v DE\n 590 genes",x="DE log effect size", y="Differnece in PAS usage") + stat_cor(label.x = -4,col="blue")


sigutronly

plot_grid(intronplot,utrplot,allonlyutr)

plot both

plot_grid(allonlyutr,sigutronly)

This is interesting. This is showing that the relationship is likely due to difference in switching. When you look at genes only with 3’ UTR PAS you don’t see a relationship.

I wonder if this gene set has a relationship with 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)


DiffIsoUTRRibo=DiffIso_mostused %>% filter(gene %in% DiffIsoUTRgenes$gene) %>% inner_join(Ribo, by="gene")



ggplot(DiffIsoUTRRibo, aes(x=HvC.beta,y=deltaPAU)) + geom_point()+ geom_smooth(method="lm") + stat_cor()

DiffIsoUTRRiboSig=DiffIso_mostused %>% filter(gene %in% DiffIsoUTRgenes$gene) %>% inner_join(Ribo, by="gene")  %>% filter(SigPAU2=="Yes")



ggplot(DiffIsoUTRRiboSig, aes(x=HvC.beta,y=deltaPAU)) + geom_point()+ geom_smooth(method="lm") + stat_cor()

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] workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.4.0     
 [9] later_0.7.5      pillar_1.3.1     glue_1.3.0       withr_2.1.2     
[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      evaluate_0.12   
[21] labeling_0.3     knitr_1.20       httpuv_1.4.5     broom_0.5.1     
[25] Rcpp_1.0.4.6     promises_1.0.1   scales_1.0.0     backports_1.1.2 
[29] jsonlite_1.6     fs_1.3.1         hms_0.4.2        digest_0.6.18   
[33] stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2  cli_1.1.0       
[37] tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0
[45] rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10  R6_2.3.0        
[49] nlme_3.1-137     git2r_0.26.1     compiler_3.5.1

UTR and intronic switches

Briana Mittleman

5/23/2020