Differential Used Intronic
Briana Mittleman
2/23/2020
Last updated: 2020-02-27
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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Unstaged changes:
Modified: analysis/ExploredAPA.Rmd
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 startedlibrary(ggpubr)Loading required package: ggplot2Loading required package: magrittrlibrary(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
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✖ 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)
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)
| 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)
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] 1358nrow(DeandAPA_topPAS %>% filter(loc=="intron", SigPAU2=="Yes"))[1] 221#3 utr
nrow(DeandAPA_topPAS %>% filter(loc=="utr3"))[1] 5993nrow(DeandAPA_topPAS %>% filter(loc=="utr3", SigPAU2=="Yes"))[1] 1088from 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)
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)
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)
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] 21159ggplot(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)
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)
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] 7405nrow(RiboandAPA_topPAS %>% filter(loc=="intron"))[1] 1180nrow(RiboandAPA %>% filter(loc=="utr3"))[1] 13754nrow(RiboandAPA_topPAS %>% filter(loc=="utr3"))[1] 5179UTR 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)
Sig APA
RiboandAPA_topPASsigAPA= RiboandAPA_topPAS %>% filter(SigPAU2=="Yes")
nrow(RiboandAPA_topPASsigAPA %>% filter(loc=="intron"))[1] 192nrow(RiboandAPA_topPASsigAPA %>% filter(loc=="utr3"))[1] 908192 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)
Sig both:
RiboandAPA_topPASsigAPAandR= RiboandAPA_topPASsigAPA %>% filter(SigPAU2=="Yes", HvC.FDR<.05)
nrow(RiboandAPA_topPASsigAPAandR %>% filter(loc=="intron"))[1] 43nrow(RiboandAPA_topPASsigAPAandR %>% filter(loc=="utr3"))[1] 22743 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)
Correlation in UTR bnut not intronic. Not sure if this is due to the number of PAS.
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] ggpubr_0.2 magrittr_1.5 ggplot2_3.1.1 workflowr_1.6.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38
[4] colorspace_1.3-2 generics_0.0.2 htmltools_0.3.6
[7] yaml_2.2.0 rlang_0.4.0 later_0.7.5
[10] pillar_1.3.1 glue_1.3.0 withr_2.1.2
[13] RColorBrewer_1.1-2 modelr_0.1.2 readxl_1.1.0
[16] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[19] cellranger_1.1.0 rvest_0.3.2 evaluate_0.12
[22] labeling_0.3 knitr_1.20 httpuv_1.4.5
[25] broom_0.5.1 Rcpp_1.0.2 promises_1.0.1
[28] scales_1.0.0 backports_1.1.2 jsonlite_1.6
[31] fs_1.3.1 hms_0.4.2 digest_0.6.18
[34] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[37] cli_1.1.0 tools_3.5.1 lazyeval_0.2.1
[40] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[43] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0
[46] rmarkdown_1.10 httr_1.3.1 rstudioapi_0.10
[49] R6_2.3.0 nlme_3.1-137 git2r_0.26.1
[52] compiler_3.5.1