Last updated: 2020-05-31
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Knit directory: Comparative_APA/analysis/
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Unstaged changes:
Modified: analysis/DeandNumPAS.Rmd
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Modified: analysis/multiMap.Rmd
Modified: analysis/phastCon.Rmd
Modified: analysis/pol2.Rmd
Modified: analysis/speciesSpecific.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 | 2c88fe0 | brimittleman | 2020-05-31 | fig 5 and 6 |
html | c1c2047 | brimittleman | 2020-05-31 | Build site. |
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':
ggsave
library(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_names
The following object is masked from 'package:tidyr':
extract
Attaching package: 'ggpubr'
The following object is masked from 'package:cowplot':
get_legend
mkdir ../output/FigureDF
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
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
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
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
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
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
pdf("../output/fig1.pdf", height=6, width=8)
fig1all
dev.off()
png
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
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
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
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
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
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
pdf("../output/fig2.pdf", height=6, width=8)
fig2
dev.off()
png
2
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
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
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
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
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
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
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
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
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
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
pdf("../output/fig3.pdf", height=8, width=8)
fig3
dev.off()
png
2
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$Enrichment),width=.01)+geom_point(size=10) + coord_flip() + geom_hline(yintercept = 1) + facet_grid(~Pheno)+scale_color_manual(values=useCOl) + labs( title="Genes with differences in APA are enriched\n in differencial expression and differentailly translated genes",x="", y="Enrichment")+geom_text(color = "black", size = 3) +scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS",OnlyIC= "ID"))+ theme_classic()+theme(legend.position = "none", text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=12),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
figure4a
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
figure4b=ggplot(fig4Data,aes(x=sets, y=-log10(Pval),fill=sets)) +geom_bar(stat = "identity",position = "dodge") +geom_hline(yintercept =1.3)+ labs(x="")+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS",OnlyIC= "ID"))+ scale_fill_manual(values=useCOl,labels=c("Both", "PAS Level", "Isoform Diversity"), name="")+facet_wrap(~Pheno)+ theme_classic()+ theme(legend.position = "none",text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=12),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
figure4b
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
row2fig4=plot_grid(NULL, figure4b,NULL, rel_widths=c(0.12,1,.1), nrow = 1 )
fig4=plot_grid(figure4a,row2fig4,nrow =2, scale = c(.9,.9),labels = c("a","b"))
fig4
Version | Author | Date |
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c1c2047 | brimittleman | 2020-05-31 |
pdf("../output/fig4.pdf", height=6, width=8,useKerning=F)
fig4
dev.off()
png
2
fig5data=read.table("../output/FigureDF/SameDiffDomEnrichinDE.txt",header = T, stringsAsFactors = F)
fig5data$set=as.factor(fig5data$set)
fig5a=ggplot(fig5data,aes(x=set,group=type,col=set,y=Enrich))+ geom_bar(stat="identity",col="grey",alpha=.3,width=.01)+geom_point(size=10) + coord_flip()+ geom_hline(yintercept = 1) +scale_color_brewer(palette="RdYlBu")+geom_text(col="black",aes(label = round(Enrich,2)))+ facet_grid(~type)+labs(x="Dominance Cutoff", y="Enrichment",title="Enrichment for differentially expressed genes in genes\n with the same and different dominant PAS") + 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"))
fig5a
fig5b=ggplot(fig5data,aes(x=set, y=-log10(Pval),fill=set)) + geom_bar(stat="identity") +labs(title="",x="Dominance Cutoff")+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none")+ geom_hline(yintercept = 1.30103) + facet_grid(~type)+ theme_classic()+ theme(legend.position = "none", text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=10),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig5b
fig5=plot_grid(fig5a,fig5b,nrow = 2,scale = c(.9,.9),labels = c("a","b"), rel_heights = c(1,.8))
fig5
pdf("../output/fig5.pdf", height=7, width=7,useKerning=F)
fig5
dev.off()
png
2
fig6topdata=read.table("../output/FigureDF/DPnotDE.txt",header = T, stringsAsFactors = F)
fig6middata=read.table("../output/FigureDF/DPnotDE_trans.txt",header = T, stringsAsFactors = F)
fig6dapa=read.table("../output/FigureDF/PinteractiondAPA.txt",header = T, stringsAsFactors = F)
fig6both=read.table("../output/FigureDF/Pinteractionboth.txt",header = T, stringsAsFactors = F)
fib6ic=read.table("../output/FigureDF/PinteractiondIC.txt",header = T, stringsAsFactors = F)
fig6a=ggplot(fig6topdata,aes(x=Set, fill=Set, y=Number))+ geom_bar(stat="identity")+ scale_fill_manual(values=useCOl)+geom_text(aes(label=Number), position=position_dodge(width=0.9), vjust=2)+ labs(title="Number of genes differentially expressed\n in protein but not mRNA", y="Number of Genes",x="")+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS",OnlydIC= "ID"))+ theme_classic()+theme(legend.position = "none", text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=10),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig6a
fig6b=ggplot(fig6topdata,aes(x=Set, fill=Set, y=Prop))+ geom_bar(stat="identity")+ scale_fill_manual(values=useCOl) + labs(title="Proportions of genes differentially expressed\n in protein but not mRNA", y="Proportion of Genes",x="")+geom_text(aes(label=round(Prop,3)), position=position_dodge(width=0.9), vjust=2) +scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS",OnlydIC= "ID"))+ theme_classic()+theme(legend.position = "none", text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=10),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig6b
fig6c= ggplot(fig6middata, aes(x=set,by=dTE, y=ndTE,fill=set, alpha=dTE)) +geom_bar(stat="identity", position = "dodge") + labs(title="", y="Number of Genes",x="") + scale_fill_manual(values = useCOl ) + scale_alpha_manual(values=c(.6, 1),name="Differentially translated") + geom_text(aes(label=ndTE), position=position_dodge(width=0.9), vjust=1)+guides(fill = FALSE)+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS",OnlyIC= "ID"))+theme_classic()+ theme(legend.position = "top", legend.justification = "center", text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=10),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig6c
fig6d=ggplot(fig6middata, aes(x=set,by=dTE, y=ndTE,fill=set, alpha=dTE)) +geom_bar(stat="identity", position = "fill") + labs(title="", y="Proportion of Genes",x="") + scale_fill_manual(values = useCOl ) + scale_alpha_manual(values=c(.6, 1),name="Differentially translated") + guides(fill = FALSE)+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS",OnlyIC= "ID"))+ theme(legend.position = "top",legend.justification = "center", text=element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold",size=10),axis.text.y = element_text(size=10),axis.text.x = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig6d
fig6e=ggplot(fig6both,aes(x=dPnotDE, y=log10(NormInter),fill=dPnotDE)) + geom_boxplot(notch = T) + stat_compare_means( label.x = 1.25, label.y = 1.5) + scale_fill_manual(values = c("grey", "#d73027"))+ labs(x="Differentally expressed in protein \n not in mRNA", y="log10(Interaction)", title="") + theme_classic() + theme(axis.text.x=element_text(angle=90, hjust=0, size = 10), text= element_text(size=10), legend.position = "none",plot.title = element_text(hjust = 0.5, face="bold",size=10),axis.text.y = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig6e
fig6f=ggplot(fig6dapa,aes(x=dPnotDE, y=log10(NormInter),fill=dPnotDE)) + geom_boxplot(notch = T) + stat_compare_means( label.x = 1.25, label.y = 1.5) + scale_fill_manual(values = c("grey", "#4575b4")) + labs(x="Differentally expressed in protein \n not in mRNA", y="log10(Interaction)", title="") + theme_classic() + theme(axis.text.x=element_text(angle=90, hjust=0, size = 10), text= element_text(size=10), legend.position = "none",plot.title = element_text(hjust = 0.5, face="bold",size=10),axis.text.y = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig6f
fig6g=ggplot(fib6ic,aes(x=dPnotDE, y=log10(NormInter),fill=dPnotDE)) + geom_boxplot(notch = T) + stat_compare_means( label.x = 1.25, label.y = 1.5) + scale_fill_manual(values = c("grey", "#fee090")) + labs(x="Differentally expressed in protein \n not in mRNA", y="log10(Interaction)", title="") + theme_classic() + theme(axis.text.x=element_text(angle=90, hjust=0, size = 10), text= element_text(size=10), legend.position = "none",plot.title = element_text(hjust = 0.5, face="bold",size=10),axis.text.y = element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))
fig6g
fig6top=plot_grid(fig6a,fig6b, labels = c("a","b"), scale = c(.9,.9),nrow = 1)
fig6mid=plot_grid(fig6c,fig6d, labels = c("c","d"), scale = c(.9,.9),nrow = 1)
fig6botom=plot_grid(fig6e,fig6f, fig6g, labels = c("e","f","g"), scale = c(.9,.9,.9),nrow = 1)
fig6=plot_grid(fig6top,fig6mid,fig6botom,nrow = 3, rel_heights = c(1,1,.8))
fig6
pdf("../output/fig6.pdf", height=9, width=8,useKerning=F)
fig6
dev.off()
png
2
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