Last updated: 2020-05-27
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Knit directory: Comparative_APA/analysis/
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File | Version | Author | Date | Message |
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Rmd | d0097b3 | brimittleman | 2020-05-27 | add intron only |
html | 957e640 | brimittleman | 2020-05-23 | Build site. |
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
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── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(ggpubr)
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Attaching package: 'magrittr'
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library(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$gene)
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID, Gene.name)
DE=read.table("../data/DiffExpression/DEtested_allres.txt",stringsAsFactors = F,header = F, col.names = c("Gene_stable_ID" ,"logFC" ,"AveExpr" , "t" , "P.Value" , "adj.P.Val", "B" )) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::rename('gene'=Gene.name) %>% dplyr::select(-Gene_stable_ID)%>% mutate(CorrectedlogFC=-1*logFC)
DEandAPA=DE %>% inner_join(DiffIso_utrinton,by="gene")
DEandAPAIntron=DEandAPA %>% filter(loc=="intron")
DEandAPAUTR=DEandAPA %>% filter(loc=="utr3")
intronplot=ggplot(DEandAPAIntron,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth( method="lm") + labs(title="Significant Intronic APA v DE\n141 genes with a significant difference \nin intronic and 3' UTR PAS", x="DE log effect size", y="Differnece in PAS usage") + stat_cor(label.x = -4,col="blue")
utrplot=ggplot(DEandAPAUTR,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth( method="lm") + labs(title="Significant 3' UTR APA v DE\n 141 genes with a significant difference \nin intronic and 3' UTR PAS",x="DE log effect size", y="Differnece in PAS usage") + stat_cor(label.x = -4,col="blue")
plot_grid(intronplot,utrplot)
Version | Author | Date |
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957e640 | brimittleman | 2020-05-23 |
3’ UTR PAS are stronger
what about genes with only significant UTR pas:
DiffIsoUTRgenes=DiffIso %>% filter(SigPAU2=="Yes") %>% group_by(gene,loc) %>% summarise(nsite=n()) %>% ungroup() %>% group_by(gene) %>% summarise(locList=paste(loc, collapse = ",")) %>% filter(locList=="utr3")
DiffIso_mostused=DiffIso %>% mutate(AvgUsageBoth=(Human+Chimp)/2) %>% group_by(gene) %>% arrange(p.adjust,desc(AvgUsageBoth)) %>% slice(1) %>% ungroup()
DiffIsoUTRDE= DiffIso_mostused %>% filter(gene %in% DiffIsoUTRgenes$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
Version | Author | Date |
---|---|---|
957e640 | brimittleman | 2020-05-23 |
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
Version | Author | Date |
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957e640 | brimittleman | 2020-05-23 |
plot_grid(intronplot,utrplot,allonlyutr)
Version | Author | Date |
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957e640 | brimittleman | 2020-05-23 |
plot both
plot_grid(allonlyutr,sigutronly)
Version | Author | Date |
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957e640 | brimittleman | 2020-05-23 |
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.
What about sites with only significance PAS in introns:
DiffIso_sig_genelocItron= DiffIso_sig %>% group_by(gene,loc) %>% summarise(nsite=n()) %>% ungroup() %>% group_by(gene) %>% summarise(locList=paste(loc, collapse = ",")) %>% filter(locList=="intron")
nrow(DiffIso_sig_genelocItron)
[1] 299
DiffIso_intonDE=DiffIso_mostused %>% filter(gene %in% DiffIso_sig_genelocItron$gene) %>% inner_join(DE,by="gene")
DiffIso_intonDEsigpas=DiffIso_sig %>% filter(gene %in% DiffIso_sig_genelocItron$gene) %>% inner_join(DE,by="gene")
Intrononly=ggplot(DiffIso_intonDE,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth( method="lm") + stat_cor(label.x = -4,col="blue")+ labs(title="Top used APA v DE\n 222 genes with only significant differences in introns",x="DE log effect size", y="Differnece in PAS usage")
Intrononly
Version | Author | Date |
---|---|---|
957e640 | brimittleman | 2020-05-23 |
ggplot(DiffIso_intonDEsigpas,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth( method="lm") + stat_cor(label.x = -4,col="blue")
Version | Author | Date |
---|---|---|
957e640 | brimittleman | 2020-05-23 |
plot 4 together:
plot_grid(intronplot,utrplot,allonlyutr,Intrononly)
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