adjust figures for reformating (f9726e5c) · Commits · open-source-code / hamlets_ILD_vision_pigmentation

3_figures/F4.Rmd

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		}
		```

		An individual result will look like this (plts[[1]] & plts[[2]]):
		An individual result will look like this (`plts[[1]]` & `plts[[2]]`):

		<center>
		```{r basePlotSHOW, echo=FALSE}

3_figures/S02.Rmd

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		@@ -123,7 +123,7 @@ cp1 <- plot_grid(pbel+theme(legend.position = 'none'),p1,
		phon+theme(legend.position = 'none'),p2,
		pboc+theme(legend.position = 'none'),p3,
		ncol = 2,rel_heights = c(1,1,1),
		rel_widths = c(0.65,0.35)),
		rel_widths = c(0.65,0.35),
		labels=c('A','B',rep('',4)),
		label_size = 10);
		```

3_figures/_bookdown.yml

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		@@ -2,7 +2,7 @@ book_filename: "Script repository Hench et al. 2018"
		chapter_name: ""
		repo: https://github.com/k-hench/bookdown
		output_dir: ../docs
		rmd_files: ["index.Rmd","Workflow.Rmd","F1.Rmd","F2.Rmd","F3.Rmd","E1.Rmd","S01.Rmd","S02.Rmd","S03.Rmd","S05.Rmd","S06.Rmd","S07.Rmd","S08.Rmd","S09.Rmd","S10.Rmd","S11.Rmd","S12.Rmd","S13.Rmd","S14.Rmd","S15.Rmd"]
		rmd_files: ["index.Rmd","Workflow.Rmd","F1.Rmd","F2.Rmd","F3.Rmd","F4.Rmd","S01.Rmd","S02.Rmd","S03.Rmd","S05.Rmd","S06.Rmd","S07.Rmd","S08.Rmd","S09.Rmd","S10.Rmd","S11.Rmd","S12.Rmd","S13.Rmd","S14.Rmd","S15.Rmd"]
		clean: [packages.bib, bookdown.bbl]
		new_session: yes
		delete_merged_file: True

docs/E1.md

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		---
		output: html_document
		editor_options:
		chunk_output_type: console
		---
		# Extendend Data Figure 1



		## Summary

		This is the accessory documentation of Extendend Data Figure 1.

		The Figure can be recreated by running the R script `E1.R`:

		```sh
		cd $WORK/3_figures/F_scripts

		Rscript --vanilla E1.R
		rm Rplots.pdf

		```
		## Details of `E1.R`

		In the following, the individual steps of the R script are documented.
		Is an executable R script that depends on a variety of image manipulation and data managing and genomic data packages.

		It Furthermore depends on the R scripts `E1.functions.R`,`E1.plot_fun.R` which themselves depend on `E1.getDXY.R`, `E1.getFSTs.R` and `E1.getGxP.R` (all located under `$WORK/0_data/0_scripts`).


		```r
		library(grid)
		library(gridSVG)
		library(grImport2)
		library(grConvert)
		library(tidyverse)
		library(rtracklayer)
		library(hrbrthemes)
		library(cowplot)
		require(rtracklayer)

		source('../../0_data/0_scripts/E1.functions.R');
		source('../../0_data/0_scripts/E1.plot_fun.R')
		```

		The script `E1.plot_fun.R`contains a function (`create_K_plot()`) that plots a single genomic range (a single sub figure) as seen in Figure 3.
		The Details of this script are explained below.

		Here we apply the `create_K_plot()` function one for every subplot (a,b,c & d).
		Within the function we specify the current LG, the annotation file, the extend of the region in question, candidate genes, genes of secondary interest, SNP positions of interest, and the outlier region ID as defined in Figure 2.


		```r
		p1 <- create_K_plot(searchLG = "LG09",gfffile = '../../1-output/09_gff_from_IKMB/HP.annotation.named.gff',xr = c(17800000,18000000),
		searchgene = c("SOX10_1"),secondary_genes = c("RNASEH2A"),searchsnp = c(17871737,17872597,17873443),
		muskID = 'A')
		p2 <- create_K_plot(searchLG = "LG12",gfffile = '../../1-output/09_gff_from_IKMB/HP.annotation.named.gff',xr = c(20080000,20500000),
		searchgene = c("Casz1_2"),secondary_genes = c(),searchsnp = c(20316944,20317120,20323661,20323670,20333895,20347263),
		muskID = 'B')
		p3 <- create_K_plot(searchLG = "LG12",gfffile = '../../1-output/09_gff_from_IKMB/HP.annotation.named.gff',xr = c(22100000,22350000),
		searchgene = c("hoxc13a"),secondary_genes = c('hoxc10a',"hoxc11a","hoxc12a","calcoco1_1"),
		searchsnp = c(),
		muskID = 'C')
		p4 <- create_K_plot(searchLG = "LG17",gfffile = '../../1-output/09_gff_from_IKMB/HP.annotation.named.gff',xr = c(22500000,22670000),
		searchgene = c('LWS',"SWS2abeta","SWS2aalpha","SWS2b"),searchsnp = c(22553970,22561903,22566254),
		secondary_genes = c("Hcfc1","HCFC1_2","HCFC1_1","GNL3L","TFE3_0","MDFIC2_1","CXXC1_3","CXXC1_1",'Mbd1','CCDC120'),
		muskID = 'D')
		```

		An individual result will look like this (p1):

		<center>
		<img src="E1_files/figure-html/basePlotSHOW-1.png" width="672" />
		</center>

		We read in the external legend:


		```r
		legGrob <- gTree(children=gList(pictureGrob(readPicture("../../0_data/0_img/legend-pw-cairo.svg"))))
		```

		And combine the individual subplots with the legend:


		```r
		E1 <- plot_grid(NULL,NULL,NULL,NULL,
		p1,NULL,p2,NULL,
		NULL,NULL,NULL,NULL,
		p3,NULL,p4,NULL,
		NULL,NULL,NULL,NULL,
		ncol=4,rel_heights = c(.03,1,.03,1,.1),rel_widths = c(1,.025,1,.02),
		labels = c('a','','b','',
		'','','','',
		'c','','d','',
		'','','','',
		'','','',''),label_size = 10)+
		draw_grob(legGrob, 0.1, 0, .8, 0.04)
		```

		The final figure is then exported using `ggsave()`.


		```r
		ggsave(plot = E1, filename = '../output/E1.pdf',width = 183,height = 155,units = 'mm',device = cairo_pdf)
		```
		<center>
		<img src="E1_files/figure-html/f3SHOW-1.png" width="691.65356544" />
		</center>
		---

		## Details of `E1.plot_fun.R`

		The actual work for figure 3 is done within the `create_K_plot()` function.

		This function loads the data and does the plotting - the `E1.R` script is basically just a wrapper that selects the setting for each subplot and combines the results.

		The function starts by importing the functions need to import the different data types (gene models, F~ST~ values, GxP p values, d~XY~)


		```r
		create_K_plot <- function(searchLG,gfffile,xr,searchgene,secondary_genes,searchsnp,muskID){
		source('../../0_data/0_scripts/E1.functions.R');
		source('../../0_data/0_scripts/E1.getFSTs.R')
		source('../../0_data/0_scripts/E1.getGxP.R')
		source('../../0_data/0_scripts/E1.getDXY.R')
		```

		It then sets some global options for the plotting and throws an error if the genomic range is negative.


		```r
		highclr <- '#3bb33b'
		theme_set(theme_minimal(base_size = 6))
		if(xr[1]>xr[2]){print('error: ill conditioned x-range')}
		wdth <- .3
		```

		Then the different data types are imported using the previously loaded import functions.


		```r
		# get gene models
		df_list <- get_anno_df_single_line(searchLG=searchLG,gfffile=gfffile,xrange=xr,
		genes_of_interest = searchgene,genes_of_sec_interest=secondary_genes,anno_rown=3)

		# get fst values
		fst_list <- getFSTS(searchLG,xr,searchsnp,highclr)
		global_fst<-fst_list$global_fst;
		data_fst<-fst_list$data_fst

		# get GxP p values
		pfst_list <- getGxP(searchLG,xr)
		data_pfst<-pfst_list$data_pfst

		# get dxy values
		dxy_list <- getDXY(searchLG,xr)
		data_dxy<-dxy_list$data_dxy
		```

		Then the color themes, axis labels further global plotting settings are set.


		```r
		clr <- c('#fb8620','#1b519c','#d93327')
		annoclr <- c('lightgray',highclr,rgb(.3,.3,.3))[1:3]
		df_list[[1]] <- df_list[[1]] %>% mutate(label=paste("italic(",tolower(Parentgenename),")",sep='') )
		LW <- .3;lS <- 9;tS <- 6
		plotSET <- theme(rect = element_blank(),
		text=element_text(size=tS,color='black'),
		panel.grid.major = element_line(colour = rgb(.92,.92,.92)),
		panel.grid.minor = element_line(colour = rgb(.95,.95,.95)),
		plot.background = element_blank(),
		plot.margin = unit(c(3,7,3,3),'pt'),
		panel.background = element_blank(),#element_rect(colour = 'gray',fill=NULL),
		legend.position = 'none',#c(.8,.3),
		legend.direction = 'horizontal',
		axis.title.y = element_blank(),
		axis.title.x = element_blank(),#element_text(size=13,face='bold',color='black'),
		strip.text.y = element_text(size=lS,color='black'),
		axis.text.x = element_blank(),#element_text(size=11,color='black'),
		strip.placement = "outside",
		panel.border = element_blank()
		)
		```

		The individual data tracks are then plotted individually. Later the single tracks are aligned.

		The first data track contains the gene models from the annotation file as well as the x axis labels (the position on the current LG).


		```r
		p11 <- ggplot()+
		# setting the genomic range
		coord_cartesian(xlim=xr/1000)+
		# including the exons as blocks
		geom_rect(data=df_list[[2]],aes(xmin=ps/1000,xmax=pe/1000,ymin=yl-(wdth/2),ymax=yl+(wdth/2),
		fill=as.factor(clr),col=as.factor(clr),group=Parent),lwd=.1)+
		# including the extend and direction of each mRNA
		geom_segment(data=(df_list[[1]]%>%filter(strand%in%c('+','-'))),
		aes(x=ps/1000,xend=pe/1000,y=yl,yend=yl,group=Parent),lwd=.1,
		arrow=arrow(length=unit(2,"pt"),type='closed'),color='black')+
		# including the extend of mRNA (if direction is unknown)
		geom_segment(data=(df_list[[1]]%>%filter(!strand%in%c('+','-'))),
		aes(x=ps/1000,xend=pe/1000,y=yl,yend=yl,group=Parent),lwd=.2,color='black')+
		# include gene name as label
		geom_text(data=(df_list[[1]]%>%filter(Parentgenename%in%c(searchgene,secondary_genes))),
		aes(x=labelx/1000,label=label,y=yl-.5),size=1.8,parse=T)+
		# this inhereted from ealier versions but kept to allow propper alignment with the other data tracks
		facet_grid(window~.,scales='free_y',
		switch = 'y',labeller = label_parsed,as.table = T)+
		# color settings
		scale_color_manual(values=annoclr,breaks=c("x","y","z"),guide=F)+
		scale_fill_manual(values=annoclr,guide=F)+
		# axes labels and settings
		scale_x_continuous(name=paste0(searchLG,' (',muskID,', kb)'),expand=c(0,0),position = 'top')+
		scale_y_continuous(breaks = seq(0,.75,length.out = 4))+
		# plot format adjustments
		theme(rect = element_blank(),
		text=element_text(size=tS,color='black'),
		panel.grid.major = element_blank(),
		panel.grid.minor = element_blank(),
		plot.background = element_blank(),
		panel.background = element_blank(),
		legend.position = c(.8,.3),
		legend.direction = 'horizontal',
		axis.title.y = element_blank(),
		axis.title.x = element_text(size=lS,face='bold',color='black'),
		strip.text.y = element_text(size=lS,color='black'),
		axis.text.x = element_text(size=tS,color='black'),
		strip.placement = "outside",
		panel.border = element_blank());
		```

		To achieve proper formatting, the gene labels are adjusted independently depending on the outlier region ID.
		This is done after plotting to be able to represent special formats within the plot labels (eg. Greek letters AND italics)


		```r
		if(muskID=='A'){
		p11$layers[[4]]$data$label <- c("italic(sox10)","italic(rnaseh2a)")
		} else if(muskID=='B'){
		p11$layers[[4]]$data$label <- c("italic(casz1^3)","italic(casz1^2)","italic(casz1^1)")
		} else if(muskID=='C'){
		p11$layers[[4]]$data$label <- c('italic(hoxc10a)',"italic(hoxc11a)","italic(hoxc12a)","italic(hoxc13a)","italic(calcoco1)")
		}else if(muskID=='E'){
		p11$layers[[4]]$data$label <- c("italic(polr1d)","italic(ednrb)")
		} else if(muskID=='F'){
		p11$layers[[4]]$data$label <- c("italic(foxd3)","italic(alg6)","italic(efcab7)")
		} else if(muskID=='D'){
		p11$layers[[4]]$data$label <- c("italic(hcfc1)","italic(hcfc1[2])","italic(hcfc1[1])",'italic(paste(sws2a,"\u03B2"))',
		'italic(paste(sws2a,"\u03B1"))',"italic(sw2b)","italic(lws)",
		"italic(gnl3l)","italic(tfe3)","italic(mdfic2)","italic(cxxc1[3])",
		"italic(cxxc1[1])",'italic(mbd1)','italic(ccdc120)')
		}else if(muskID=='G'){
		p11$layers[[4]]$data$label <- c("italic(lgals3bpb)","italic(mpnd)","italic(sh3gl1)","italic(rorb)")
		}else if(muskID=='H'){
		p11$layers[[4]]$data$label <- c("italic(alg2)","italic(sec61b)","italic(nr4a3)",
		"italic(stx17)","italic(erp44)","italic(invs)")
		}
		```

		The second data track contains the F~ST~ values.


		```r
		p12 <- ggplot()+
		# setting the genomic range
		coord_cartesian(xlim=xr)+
		# including the global Fst values (SNP) basis
		geom_point(data=global_fst,aes(x=POS,y=WEIR_AND_COCKERHAM_FST),col=global_fst$clr,size=.2)+
		# highlight the SNPs of interest
		geom_point(data=global_fst,aes(x=POS,y=WEIR_AND_COCKERHAM_FST),col=global_fst$clr2,size=.3)+
		# add pairwise fsts as lines (10 kb / 1kb)
		geom_line(data=(data_fst %>% filter(POS > xr[1],POS<xr[2]))
		,aes(x=POS,y=WEIGHTED_FST,col=run),lwd=LW)+
		# set color theme
		scale_color_manual(values=c(clr,annoclr),breaks=c("nig-pue","nig-uni","pue-uni"),guide=F)+
		scale_fill_manual(values=annoclr,guide=F)+
		# this inhereted from ealier versions but kept to allow propper alignment with the other data tracks
		facet_grid(window~.,scales='free_y',
		switch = 'y',labeller = label_parsed,as.table = T)+
		# axes settings
		scale_x_continuous(name=searchLG,expand=c(0,0),position = 'top')+
		scale_y_continuous(breaks = seq(0,.75,length.out = 4))+
		scale_linetype(name='location',label=c('Belize','Honduras','Panama'))+
		guides(linetype= guide_legend(override.aes = list(color = 'black')))+
		# include pre-set plot theme
		plotSET
		```

		The third data track contains the GxP p values.


		```r
		p13 <- ggplot()+
		# setting the genomic range
		coord_cartesian(xlim=xr)+
		geom_line(data=(data_pfst %>% filter(POS > xr[1],POS<xr[2]))
		,aes(x=POS,y=avgp_wald,col=run),lwd=LW)+
		scale_color_manual(values=c(clr,annoclr),breaks=c("nig-pue","nig-uni","pue-uni","x","y","z"),guide=F)+
		# this inhereted from ealier versions but kept to allow propper alignment with the other data tracks
		facet_grid(window~.,scales='free_y',
		switch = 'y',labeller = label_parsed,as.table = T)+
		# axes settings
		scale_x_continuous(name=searchLG,expand=c(0,0),position = 'top')+
		scale_y_continuous(breaks = seq(0,9,length.out = 4))+
		scale_linetype(name='location',label=c('Belize','Honduras','Panama'))+
		guides(linetype= guide_legend(override.aes = list(color = 'black')))+
		# include pre-set plot theme
		plotSET
		```

		The fourth data track contains the d~XY~ values.


		```r
		p14 <- ggplot()+
		# setting the genomic range
		coord_cartesian(xlim=xr)+
		geom_line(data=(data_dxy %>% filter(POS > xr[1],POS<xr[2]))
		,aes(x=POS,y=dxy,col=run),lwd=LW)+
		# geom_line(data=(data_dxy_pw %>% filter(POS > xr[1],POS<xr[2]))
		# ,aes(x=POS,y=dxy,col=run,linetype=group),lwd=1)+
		scale_color_manual(values=c(clr,annoclr),breaks=c("nig-pue","nig-uni","pue-uni","x","y","z"),guide=F)+
		# this inhereted from ealier versions but kept to allow propper alignment with the other data tracks
		facet_grid(window~.,scales='free_y',
		switch = 'y',labeller = label_parsed,as.table = T)+
		# axes settings
		scale_x_continuous(name=searchLG,expand=c(0,0),position = 'top')+
		scale_y_continuous(breaks = seq(0,.01,length.out = 5))+
		scale_linetype(name='location',label=c('Belize','Honduras','Panama'))+
		guides(linetype= guide_legend(override.aes = list(color = 'black')))+
		# include pre-set plot theme
		plotSET
		```

		Finally the individual tracks are stacked and aligned using `plot_grid()` (`cowplot` package).
		The complete sub figure is then returned by the function.


		```r
		p2 <- plot_grid(p11,p12,p13,p14,
		ncol = 1,align = 'v',axis = 'r',rel_heights = c(1.3,rep(1,3)))
		return(p2)}
		```

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