Main figures
Briana Mittleman
5/30/2020
Last updated: 2020-05-31
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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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 | 033290c | brimittleman | 2020-05-31 | add main figures 1-4 |
I would like to plot all of the main figures in one R markdown with cowplot. I will save the data frames in the other files as rds objects then load them here.
library(cowplot)Loading required package: ggplot2
Attaching package: 'cowplot'The following object is masked from 'package:ggplot2':
ggsavelibrary(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() ──
✖ dplyr::filter() masks stats::filter()
✖ cowplot::ggsave() masks ggplot2::ggsave()
✖ dplyr::lag() masks stats::lag()library(ggpubr)Loading required package: magrittr
Attaching package: 'magrittr'The following object is masked from 'package:purrr':
set_namesThe following object is masked from 'package:tidyr':
extract
Attaching package: 'ggpubr'The following object is masked from 'package:cowplot':
get_legendmkdir ../output/FigureDFFigure1
1A- location pic charts
fig1A_data=read.table("../output/FigureDF/nPAS_figure1.txt", header = T, stringsAsFactors = F)
fig1A_data$species <- factor(fig1A_data$species, labels = c("Chimpanzee~PAS", "Human~PAS"))
fig1A=ggplot(fig1A_data,aes(x="",y=prop, fill=loc)) + geom_bar(stat="identity",width=1, color="white")+ coord_polar("y", start=0) +theme_void() + facet_wrap(~species,labeller = label_parsed,strip.position = "left") + scale_fill_brewer(palette = "RdYlBu", name="Genic Location", labels=c("Coding", "5KB downstream", "Intronic","3' UTR", "5' UTR")) + theme(legend.position = "bottom",strip.text = element_text(size = 10, face = "italic",angle=270), text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold"),plot.margin = unit(c(0,0,0,0), "cm")) +labs(title="PAS genic locations are conserved between species", x="", y="")
fig1A
fig1B_data=read.table("../output/FigureDF/SeqConservation.txt",header = T,stringsAsFactors = F)
fig1B=ggplot(fig1B_data, aes(x=Set, by=Set, fill=region, y=PhyloP)) + geom_boxplot(notch = T) + scale_fill_brewer(palette = "RdYlBu") + scale_x_discrete(labels=c("-600", "-400", "-200", '0','200','400','600')) + labs(x="Basepairs", title="PAS are more conserved \nthan surrounding regions") + guides(fill = FALSE) + theme_classic()+ theme(plot.title = element_text(hjust = 0.5, face="bold"), axis.text.x = element_text(size=10),axis.text.y = element_text(size=10),text=element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig1B
Figure 1C:
fig1c_data=read.table("../output/FigureDF/SignalSizeFigure.txt", header = T, stringsAsFactors = F)
fic1c=ggplot(fig1c_data, aes(y=propSS,by=Species,fill=Species,x=reorder(SS, -propSS))) + geom_bar(stat="identity", position = "dodge") + theme_classic() +theme(axis.text.x = element_text(size=10,angle = 90),plot.title = element_text(hjust = 0.5, face="bold"),axis.text.y = element_text(size=10),text=element_text(size=10), legend.position = "top",plot.margin = unit(c(0,0,0,0), "cm")) + scale_fill_brewer(palette = "Dark2", label=c("Chimp", "Human")) + labs(y="Proportion of PAS",title="Proportion of PAS with a signal site", x="")figure 1 :
row2=plot_grid(fig1B,fic1c, labels=c("b","c"), scale = c(.9,.9), nrow = 1)
fig1all=plot_grid(fig1A,row2, labels = c("a", ""),nrow = 2,rel_heights=c(.75,1 ),hjust=-12)
fig1all
pdf("../output/fig1.pdf", height=6, width=8)
fig1all
dev.off()png
2 Figure 2:
Pie charts for differences
PASlevel= read.table("../output/FigureDF/PASlevelDiff.txt", stringsAsFactors = F, header=T,col.names =c("sig", "n","gene","prop")) %>% mutate(set="PAS", test=c("Conserved", "Not Tested", "Divergent"))
genelevel= read.table("../output/FigureDF/GenelevelDiff.txt", stringsAsFactors = F, header = T, col.names =c("sig", "n","gene","prop")) %>% mutate(set="gene", test=c("Conserved", "Not Tested", "Divergent"))
isoformlevel= read.table("../output/FigureDF/IsoformlevelDiff.txt", stringsAsFactors = F, header = T, col.names =c("sig", "n","gene","prop")) %>% mutate(set="isoform",test=c("Conserved", "Not Tested", "Divergent"))
fig2a=PASlevel %>% bind_rows(genelevel) %>% bind_rows(isoformlevel)
fig2a$set=factor(fig2a$set, levels=c("PAS", "gene", "isoform"),labels = c("PAS~Differences", "Genes~with~PAS~Differneces", "Differences~isoform~diversity"))
fig2a$test=factor(fig2a$test,levels= c("Conserved", "Divergent","Not Tested"))
useCOl <- c("#d73027", "#4575b4","#fee090")
fig2aplot=ggplot(fig2a,aes(by=test, y=prop, x="", fill=test)) + geom_bar(stat="identity",width=1, color="white")+ coord_polar("y", start=0) +theme_void() + scale_fill_manual(values=useCOl,name="") + facet_wrap(~set,labeller = label_parsed,strip.position = "left") + theme(legend.position = "bottom",strip.text = element_text(size = 10, angle=270), text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=12)) + labs(title="APA is functionally conserved at PAS and isoform diversity levels")Dominance plots
fig2cd_data=read.table("../output/FigureDF/DominantPAS.txt", header = T, stringsAsFactors = F)
fig2cd_data$cut=as.factor(fig2cd_data$cut)
fig2d=ggplot(fig2cd_data,aes(x=cut,y=PropSame,fill=cut)) + geom_bar(stat="identity") +geom_text(aes(label=nDom), position=position_dodge(width=0.9), vjust=1,size=3)+ scale_fill_brewer(palette = "RdYlBu")+theme_classic()+theme(legend.position = "none",text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=12),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm")) + labs(title="Most genes with a domiant PAS\n share the same dominant PAS", y="Proportion of Genes in Set", x="Domianance Cutoff")
fig2d
fig2c=ggplot(fig2cd_data,aes(x=cut,y=PropDom,fill=cut)) + geom_bar(stat="identity") +geom_text(aes(label=nDom), position=position_dodge(width=0.9), vjust=1, size=3) + scale_fill_brewer(palette = "RdYlBu")+theme_classic()+theme(legend.position = "none",text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=12),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm")) + labs(title="Proportion of Tested Genes \nwith a Dominant PAS", y="Proportion of Tested Genes", x="Domianance Cutoff")
fig2c
fig2row2=plot_grid(fig2c,fig2d, labels=c("b","c"), scale = c(.9,.9), nrow = 1)
fig2=plot_grid(fig2aplot,fig2row2, nrow = 2,labels = c("a",""),rel_heights=c(.8,1 ),hjust=-5)
fig2
pdf("../output/fig2.pdf", height=6, width=8)
fig2
dev.off()png
2 figure 3
fig3_data=read.table("../output/DEandAPA.txt", header = T, stringsAsFactors = F)
fig3_data2=read.table("../output/DEandAPA_sig.txt", header = T, stringsAsFactors = F)fig3a=ggplot(fig3_data,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="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() + theme(text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=12),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig3a
fig3b=ggplot(fig3_data,aes(y=deltaPAU, x=CorrectedlogFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="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 = -8,label.y = c(-1,1)) +theme_classic() + theme(legend.position = "right",text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=12),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig3b
fig3c=ggplot(fig3_data2,aes(y=deltaPAU, x=CorrectedlogFC)) + geom_point(alpha=.3) + geom_smooth(method="lm") + labs(title="Significant differences in \nAPA and 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(legend.position = "top",text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=12),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig3c
fig3d=ggplot(fig3_data2,aes(y=deltaPAU, x=CorrectedlogFC, col=loc)) + geom_point(alpha=.3) + geom_smooth(aes(col=loc),method="lm") + labs(title="Significant differences in\n APA and 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 = -8,label.y = c(-1,1))+ theme(legend.position = "right",text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=12),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm") )
fig3d
fig3=plot_grid(fig3a, fig3b,fig3c,fig3d, scale = c(.9,.9,.9,.9), labels=c("a","b","c","d"),rel_widths=c(1,1.3,1,1.3))
fig3
pdf("../output/fig3.pdf", height=8, width=8)
fig3
dev.off()png
2 figure 4
fig4Data=read.table("../output/FigureDF/DEandTEenrich.txt", header = T, stringsAsFactors = F)
fig4Data$Pval=as.numeric(fig4Data$Pval)
fig4Data$Enrichment=as.numeric(fig4Data$Enrichment)
fig4Data$Pheno=factor(fig4Data$Pheno, levels=c("Expression", "Translation", "Protein"))figure4a=ggplot(fig4Data,aes(x=sets,col=sets,y=Enrichment,label = round(Enrichment,3)))+ geom_bar(stat="identity",color="grey",aes(y=fig4Data$Enrichment),width=.01)+geom_point(size=10) + coord_flip() + geom_hline(yintercept = 1) + facet_grid(~Pheno)+scale_color_manual(values=useCOl) + labs( title="Genes with differences in APA are enriched\n in differencial expression and differentailly translated genes",x="", y="Enrichment")+geom_text(color = "black", size = 3) +scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS",OnlyIC= "ID"))+ theme_classic()+theme(legend.position = "none", text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=12),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
figure4a
figure4b=ggplot(fig4Data,aes(x=Pheno, by=sets, y=-log10(Pval),fill=sets)) +geom_bar(stat = "identity",position = "dodge") +geom_hline(yintercept =1.3)+ labs(x="")+ scale_fill_manual(values=useCOl,labels=c("Both", "PAS Level", "Isoform Diversity"), name="")+ theme_classic()+ theme(legend.position = "bottom",text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=12),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
figure4b
row2fig4=plot_grid(NULL, figure4b,NULL, rel_widths=c(0.15,1), nrow = 1 )
fig4=plot_grid(figure4a,row2fig4,nrow =2, scale = c(.9,.9),labels = c("a","b"))
fig4
pdf("../output/fig4.pdf", height=6, width=8,useKerning=F)
fig4
dev.off()png
2 figure 5
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] ggpubr_0.2 magrittr_1.5 forcats_0.3.0 stringr_1.3.1
[5] dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1 tidyr_0.8.3
[9] tibble_2.1.1 tidyverse_1.2.1 cowplot_0.9.4 ggplot2_3.1.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 reshape2_1.4.3 haven_1.1.2
[4] lattice_0.20-38 colorspace_1.3-2 generics_0.0.2
[7] htmltools_0.3.6 yaml_2.2.0 rlang_0.4.0
[10] later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 RColorBrewer_1.1-2 modelr_0.1.2
[16] readxl_1.1.0 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.2.0 workflowr_1.6.0 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 assertthat_0.2.0 rmarkdown_1.10
[49] httr_1.3.1 rstudioapi_0.10 R6_2.3.0
[52] nlme_3.1-137 git2r_0.26.1 compiler_3.5.1