update suppl fig documentation

0_data/0_resources/retina_samples_ID.txt

+ID	rID	population
+28383	R28383	H. unicolor
+28384	R28384	H. puella
+28385	R28385	H. nigricans
+28387	R28387	H. nigricans
+28390	R28390	H. nigricans
+28391	R28391	H. unicolor
+28394	R28394	H. nigricans
+28399	R28399	H. nigricans
+AG9RX46	RAG9RX46	H. nigricans
+AG9RX47	RAG9RX47	H. puella
+AG9RX48	RAG9RX48	H. puella
+AG9RX49	RAG9RX49	H. nigricans
+AG9RX50	RAG9RX50	H. nigricans
+AG9RX51	RAG9RX51	H. puella
+AG9RX52	RAG9RX52	H. nigricans
+AG9RX53	RAG9RX53	H. puella
+AG9RX54	RAG9RX54	H. unicolor
+AG9RX55	RAG9RX55	H. unicolor
+PL17_01	RPL17_01	H. unicolor
+PL17_02	RPL17_02	H. puella
+PL17_04	RPL17_04	H. puella
+PL17_16	RPL17_16	H. puella
+PL17_17	RPL17_17	H. puella
+PL17_18	RPL17_18	H. puella
+28386	R28386

0_data/0_resources/retina_samples_ID_altVersion.csv

+ids	population
+R28383	uni
+R28384	pue
+R28385	nig
+R28387	nig
+R28390	nig
+R28391	uni
+R28394	nig
+R28399	nig
+RAG9RX46	nig
+RAG9RX47	pue
+RAG9RX48	pue
+RAG9RX49	nig
+RAG9RX50	nig
+RAG9RX51	pue
+RAG9RX52	nig
+RAG9RX53	pue
+RAG9RX54	uni
+RAG9RX55	uni
+RPL17_01	uni
+RPL17_02	pue
+RPL17_04	pue
+RPL17_16	pue
+RPL17_17	pue
+RPL17_18	pue

3_figures/F1.Rmd

@@ -13,7 +13,7 @@ knitr::opts_knit$set(root.dir = './F_scripts')
 ## Summary
 
 This is the accessory documentation of Figure 1.
-The Figure can be recreated by runing the **R** script F1.R:
+The Figure can be recreated by running the **R** script F1.R:
 ```sh
 cd $WORK/3_figures/F_scripts
 
@@ -25,7 +25,7 @@ rm Rplots.pdf
 ## Details of F1.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 geodata packages.
-Aditionally, the supporting R script (**F1.functions.R**) in needed:
+Additionally, the supporting R script (**F1.functions.R**) in needed:
 
 
 ```{r head,results='hide',message=FALSE}
@@ -51,10 +51,10 @@ source("../../0_data/0_scripts/F1.functions.R")
 
 ### Fig. 1 a)
 
-First, we load the data sheet containing the sampling location as well as the population specifics for each sample and subsample it to include only the samples included in the resequencing study.
+First, we load the data sheet containing the sampling location as well as the population specifics for each sample and sub sample it to include only the samples included in the re-sequencing study.
 
 The Coordinates need to be transformed from (deg min sec) to decimal degrees.
-The sample inventory is summarised over populations and merged with the coordinates later used for the sample pointers.
+The sample inventory is summarized over populations and merged with the coordinates later used for the sample pointers.
 
 
 ```{r mapSampleData, results='hide', message=FALSE, warning=FALSE}
@@ -84,7 +84,7 @@ worldmap = clipPolys(worldmap, xlim=xlimW,ylim=ylimW, keepExtra=TRUE)
 ```
 
 External images are loaded for annotation.
-This includes the hamlet images, the contry flags and legend.
+This includes the hamlet images, the country flags and legend.
 
 ```{r mapAnnotationImgs, results='hide', message=FALSE, warning=FALSE}
 # compas and legend
@@ -117,7 +117,7 @@ sampSize <- data %>% group_by(spec,loc) %>% summarise(n=length(id)) %>% filter(s
             rep(-98.75,3)+xpos)) # honduras
 ```
 
-Next, we read in the shappe files containg the distribution of the halet species.
+Next, we read in the shape files containing the distribution of the hamlet species.
 One file contains the overlap of the distribution ranges of *H. nigricans*, *H. puella* and *H. unicolor*, while the other contains the distribution range of the whole genus (*Hypoplectrus*).
 These distributions rely on the data provided by the [*Smithsonian Tropical Research Institute*](http://biogeodb.stri.si.edu/caribbean/en/research/index/range) that were processed using [`gimp`](https://www.gimp.org/) and [`qgis`](https://qgis.org/en/site/) (including the [`Georeferencer Plugin`](https://docs.qgis.org/2.8/en/docs/user_manual/plugins/plugins_georeferencer.html))
 
@@ -180,7 +180,7 @@ p1
 
 ### Fig. 1 b)
 
-Next we create the subfigure containing the *F~ST~* information (Fig. 1 b).
+Next we create the sub figure containing the *F~ST~* information (Fig. 1 b).
 We first read in the data and define the color palette.
 
 ```{r fstData, results='hide', message=FALSE, warning=FALSE}
@@ -232,8 +232,8 @@ p5
 
 ### Fig. 1 c)
 
-The last subfigure contains the PCA output for each location.
-For those, we define the colorpalette for hamlet species and read in the PCA data generated by `pcadapt`.
+The last sub figure contains the PCA output for each location.
+For those, we define the color palette for hamlet species and read in the PCA data generated by `pcadapt`.
 
 ```{r pcaData, results='hide', message=FALSE, warning=FALSE}
 
@@ -376,7 +376,9 @@ F1 <- ggdraw()+
                   y = c(.995,.42,.42),
                   label =letters[1:3])
 ```
+
 It is then exported using `ggsave()`.
+
 ```{r exportF1, eval=FALSE}
 ggsave(plot = F1,filename = '../output/F1.pdf',width = 183,height = 145,units = 'mm',device = cairo_pdf)
   

3_figures/F2.Rmd

@@ -14,7 +14,7 @@ knitr::opts_knit$set(root.dir = './F_scripts')
 
 This is the accessory documentation of Figure 2.
 
-The Figure can be recreated by runing the **R** script F2.R:
+The Figure can be recreated by running the **R** script F2.R:
 
 ```sh
 cd $WORK/3_figures/F_scripts
@@ -100,8 +100,8 @@ nhuh <- read.csv('../../2_output/08_popGen/05_fst/nighon-unihon.50kb.5kb.windowe
   mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='NU',RUN='NHUH');
 ```
 
-The individual spcies comparisons are combined into a single data set.
-Then the order of the species comparisons and of the coparison types are set.
+The individual species comparisons are combined into a single data set.
+Then the order of the species comparisons and of the comparison types are set.
 
 ```{r combineData, results='hide', message=FALSE, warning=FALSE}
 data <- rbind(pn,pu,nu,ppnp,ppup,npup,pbnb,pbub,nbub,phnh,phuh,nhuh)
@@ -110,7 +110,7 @@ data$RUN <- factor(as.character(data$RUN),
 data$COL <- factor(as.character(data$COL),levels=c('PN','PU','NU'))
 ```
 
-Then, the geonme wide weighted mean of *F~ST~* is loaded.
+Then, the genome wide weighted mean of *F~ST~* is loaded.
 
 ```{r gwData, results='hide', message=FALSE, warning=FALSE}
 gwFST <- read.csv('../../2_output/08_popGen/05_fst/genome_wide_weighted_mean_fst.txt',sep='\t')
@@ -119,8 +119,8 @@ gwFST$RUN <- factor(gwFST$run,levels=levels(data$RUN))
 
 ### Computing 99.98 *F~ST~* percentiles
 
-The upper 99.98 *F~ST~* percintile is computed per run.
-Then windows above this threshold are indentified for each global run.
+The upper 99.98 *F~ST~* percentile is computed per run.
+Then windows above this threshold are identified for each global run.
 
 ```{r fstThresholds, results='hide', message=FALSE, warning=FALSE}
 threshs <- data %>% group_by(RUN) %>% summarise(thresh=quantile(WEIGHTED_FST,.9998))
@@ -166,7 +166,7 @@ yl <- expression(italic('F'[ST]))
 secScale <- 20
 ```
 
-The baseplot is done with `ggplot()`.
+The base plot is done with `ggplot()`.
 
 ```{r basePlot, results='hide', message=FALSE, warning=FALSE}
 p1 <- ggplot()+
@@ -259,7 +259,15 @@ F2 <- ggdraw(p1)+
   draw_grob(panGrob, 0.954, boxes$y[3]+.78*yd, .045, .045)
 ```
 
-It is then exported using `ggsave()`.
+The *F~ST~* outlier windows are then exported to be used as reference in other figures.
+
+```{r exportMusks, eval=FALSE}
+write.table(x = data2, file = 'fst_outlier_windows.txt',sep='\t',quote = F,row.names = F)
+write.table(x = musks, file = 'fst_outlier_ID.txt',sep='\t',quote = F,row.names = F)
+```
+
+
+The figure is then exported using `ggsave()`.
 
 ```{r, eval=FALSE}
 ggsave(plot = F2,filename = '../output/F2.png',width = 183,height = 183,dpi = 200,units = 'mm')
@@ -272,4 +280,5 @@ ggsave(plot = F2,filename = '../output/F2.png',width = 183,height = 183,dpi = 20
 F2
 ```
 </center>
+
 ---
\ No newline at end of file

3_figures/F3.Rmd

@@ -44,11 +44,11 @@ source('../../0_data/0_scripts/F3.functions.R');
 source('../../0_data/0_scripts/F3.plot_fun.R')
 ```
 
-The script `F3.plot_fun.R`contains a function (`create_K_plot()`) that plots a single genomic range (a single subfigure) as seen in Figure 3.
-The Details of this scriptare explained below.
+The script `F3.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 curent LG, the annotation file, the extand of the region in question, candiate genes, genes of secondary interest, SNP positions of interest, and the outlier region ID as defined in Figure 2. 
+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 subPlots,results='hide',message=FALSE, warning=FALSE}
 p1 <- create_K_plot(searchLG = "LG09",gfffile = '../../1-output/09_gff_from_IKMB/HP.annotation.named.gff',xr = c(17800000,18000000),
@@ -158,7 +158,7 @@ Then the different data types are imported using the previously loaded import fu
   data_dxy<-dxy_list$data_dxy
 ```
 
-Then the color themes, axsis labels further global plotting settings are set.
+Then the color themes, axis labels further global plotting settings are set.
 
 ```{r, eval=FALSE}
   clr <- c('#fb8620','#1b519c','#d93327')
@@ -230,8 +230,8 @@ The first data track contains the gene models from the annotation file as well a
           panel.border = element_blank());
 ```
 
-To achieve propper formatting, the gene labels are adjusted independently depending on the outlier region ID.
-This is done after ploting to be able to represent special formats within the plot labels (eg. greek letters *AND* italics)
+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, eval=FALSE}
  if(muskID=='A'){
@@ -257,7 +257,7 @@ This is done after ploting to be able to represent special formats within the pl
    }
 ```
 
-The second data track contans the *F~ST~* values.
+The second data track contains the *F~ST~* values.
 
 ```{r, eval=FALSE}
   p12 <-  ggplot()+
@@ -285,7 +285,7 @@ The second data track contans the *F~ST~* values.
     plotSET
 ```
 
-The third data track contains th GxP p values.
+The third data track contains the GxP p values.
 
 ```{r, eval=FALSE}
   p13 <-  ggplot()+
@@ -330,7 +330,7 @@ The fourth data track contains the d~XY~ values.
 ```
 
 Finally the individual tracks are stacked and aligned using `plot_grid()` (`cowplot` package).
-The complete subfigure is then returned by the function.
+The complete sub figure is then returned by the function.
 
 ```{r, eval=FALSE}
   p2 <- plot_grid(p11,p12,p13,p14,

3_figures/F4.Rmd

@@ -213,7 +213,7 @@ The position of some of the gene labels need shifting to avoid overlapping.
   genes$labPOS[genes$name %in% c("sws2a\u03B1","sws2a\u03B2","sws2b","lws")] <- genes$labPOS[genes$name %in% c("sws2a\u03B1","sws2a\u03B2","sws2b","lws")]+c(-16,-1,12,20)
 ```
 
-Then we define a data frame for the poligons that zoom onto the gene position within the annotation/legend of subfigure a.
+Then we define a data frame for the polygons that zoom onto the gene position within the annotation/legend of sub figure a.
 
 ```{r eval=FALSE}
   GZrS <- 40000;GZrS2 <- 20000; #gene zoom offset
@@ -249,15 +249,15 @@ Some parameters are set for plotting (base colors for the LD color gradient, gen
   GLABoffset <- 8
 ```
 
-At this point, the data set selection is checked. If the current selection is the global data set, the additional annotaion is included.
+At this point, the data set selection is checked. If the current selection is the global data set, the additional annotation is included.
 
 ```{r eval=FALSE}
   if(sel %in% c(1,8)){
 ```
 
-For the additional annotaion, several helpers are needed and defined as data frames.
-The construction of these is obscurred by the fact that in the base plot, the later horizontal bands are plottet tilted by an angle of 45 degrees.
-Therfore, a simple y offset affects both x and y axsis in the base plot.
+For the additional annotation, several helpers are needed and defined as data frames.
+The construction of these is obscured by the fact that in the base plot, the later horizontal bands are plotted tilted by an angle of 45 degrees.
+Therefore, a simple y offset affects both x and y axis in the base plot.
 
 ```{r eval=FALSE}
     rS <- 100000 # width of grey annotation band
@@ -419,7 +419,7 @@ Finally, the function returns the current base plot stored in `p1`.
 
 Within this script, the ILD data of the genome wide subsets of SNPs is loaded and and plotted.
 
-First we read in the global data set, then we loop over the individual population subsets and append the data to the gobal data set.
+First we read in the global data set, then we loop over the individual population subsets and append the data to the global data set.
 Since we're only interested in the distribution of r^2, we select only this column and create extra columns for the run ID, and the run type (global, subset by species, subset by location)
 
 ```{r eval=FALSE}
@@ -450,7 +450,7 @@ BW$run <- factor(BW$run,levels=c('Global',"Panana","Belize","Honduras",
                                  "H. nigricans","H. puella","H. unicolor")) 
 ```
 
-To be able to include the mean values to the boxplots we create a small summary table of the LD data.  
+To be able to include the mean values to the box plots we create a small summary table of the LD data.  
 
 ```{r eval=FALSE}
 dt2 <- BW %>% 
@@ -525,7 +525,7 @@ dataBoxGenes$xS <- factor(dataBoxGenes$run,
                           levels = c("global","boc","bel","hon","nig","pue","uni"))
 ```
 
-To be able to include the mean values to the boxplots we create a small summary table of the LD data.  
+To be able to include the mean values to the box plots we create a small summary table of the LD data.  
 
 ```{r eval=FALSE}
 BoxGenes_summary <- dataBoxGenes %>% 

3_figures/F5.Rmd

@@ -14,7 +14,7 @@ knitr::opts_knit$set(root.dir = './F_scripts')
 
 This is the accessory documentation of Figure 5.
 
-The Figure can be recreated by runing the **R** script F5.R:
+The Figure can be recreated by running the **R** script F5.R:
 
 ```sh
 cd $WORK/3_figures/F_scripts
@@ -30,7 +30,7 @@ Is an executable R script that depends on a variety of image manipulation and da
 
 It Furthermore depends on the R scripts `F4.functions.R` (located under `$WORK/0_data/0_scripts`).
 
-This script is a modification of sript F4.R. It uses the same funtions and just differs in the data sets and settings 
+This script is a modification of script F4.R. It uses the same functions and just differs in the data sets and settings 
 
 ```{r head,results='hide',message=FALSE}
 library(tidyverse)

3_figures/F_scripts/F2.R

@@ -130,6 +130,9 @@ p1 <- ggplot()+
         )+
   facet_grid(RUN~.)
 
+write.table(x = data2, file = 'fst_outlier_windows.txt',sep='\t',quote = F,row.names = F)
+write.table(x = musks, file = 'fst_outlier_ID.txt',sep='\t',quote = F,row.names = F)
+
 legGrob <- gTree(children=gList(pictureGrob(readPicture("../../0_data/0_img/legend-pw-cairo.svg"))))
 carGrob <- gTree(children=gList(pictureGrob(readPicture("../../0_data/0_img/caribbean-cairo.svg"))))
 panGrob <- gTree(children=gList(pictureGrob(readPicture("../../0_data/0_img/panama-cairo.svg"))))

3_figures/F_scripts/F4.R

@@ -9,8 +9,8 @@ source('../../0_data/0_scripts/F4.functions.R')
 
 plts <- list()
 
-for(k in 1:10){
-plts[[k]] <- trplot(k)
+for(k in 1:7){
+  plts[[k]] <- trplot(k)
 }
 
 tN <- theme(legend.position = 'none')
@@ -72,5 +72,4 @@ F4 <- ggdraw(pGr1)+
                   y=c(.99,.81,.81,.57,.57),
                   size = 14)
                   
-#ggsave(plot = F4,filename = '../output/F4..pdf',width = 183,height = 230,units = 'mm',device = cairo_pdf)
 ggsave(plot = F4,filename = '../output/F4.png',width = 183,height = 235,units = 'mm',dpi = 150)

3_figures/F_scripts/S02.R

+#!/usr/bin/env Rscript
+library(grid)
+library(gridSVG)
+library(grImport2)
+library(grConvert)
+library(tidyverse)
+library(cowplot)
+library(hrbrthemes)
+karyo <- read.csv('../../0_data/0_resources/F2.karyo.txt',sep='\t') %>%
+  mutate(GSTART=lag(cumsum(END),n = 1,default = 0),
+         GEND=GSTART+END,GROUP=rep(letters[1:2],12)) %>% 
+  select(CHROM,GSTART,GEND,GROUP)
+
+# global -------------
+pn <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/pue-nig-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% 
+  mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='PN',RUN='PN');
+
+pu <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/pue-uni-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% 
+  mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='PU',RUN='PU');
+
+nu <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/nig-uni-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% 
+  mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='NU',RUN='NU');
+# PAN -------------
+ppnp <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/pueboc-nigboc-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% 
+  mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='PN',RUN='PPNP');
+
+ppup <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/pueboc-uniboc-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% 
+  mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='PU',RUN='PPUP');
+
+npup <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/nigboc-uniboc-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% 
+  mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='NU',RUN='NPUP');
+# Bel -------------
+pbnb <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/puebel-nigbel-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% 
+  mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='PN',RUN='PBNB');
+
+pbub <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/puebel-unibel-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% 
+  mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='PU',RUN='PBUB');
+
+nbub <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/nigbel-unibel-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% 
+  mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='NU',RUN='NBUB');
+# Hon -------------
+phnh <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/puehon-nighon-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% 
+  mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='PN',RUN='PHNH');
+
+phuh <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/puehon-unihon-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='PU',RUN='PHUH');
+
+nhuh <- read.csv('../../2_output/08_popGen/06_GxP/smoothed/50kb/nighon-unihon-gemma.lm.50k.5k.smooth',sep='\t') %>% 
+  merge(.,(karyo %>% select(-GEND,-GROUP)),by='CHROM',allx=T) %>% 
+  mutate(POS=(BIN_START+BIN_END)/2,GPOS=POS+GSTART,COL='NU',RUN='NHUH');
+# ------------------------------------------
+data <- rbind(pn,pu,nu,ppnp,ppup,npup,pbnb,pbub,nbub,phnh,phuh,nhuh)
+data$RUN <- factor(as.character(data$RUN),
+                   levels=c('PN','NU','PU','PPNP','NPUP','PPUP','PBNB','NBUB','PBUB','PHNH','NHUH','PHUH'))
+data$COL <- factor(as.character(data$COL),levels=c('PN','PU','NU'))
+
+data2 <- read.csv('fst_outlier_windows.txt', sep='\t')
+musks <- read.csv('fst_outlier_ID.txt', sep='\t')
+
+clr <- c('#fb8620','#d93327','#1b519c')
+annoclr <- rgb(.4,.4,.4)
+lgclr <- rgb(.9,.9,.9)
+yl <- expression(paste('-log'[10],' (',italic('p'),'value)',sep=''))
+
+p1 <- ggplot()+
+  geom_rect(inherit.aes = F,data=karyo,aes(xmin=GSTART,xmax=GEND,
+                                           ymin=-Inf,ymax=Inf,fill=GROUP))+
+  geom_vline(data=data2,aes(xintercept=xmean),col=annoclr,lwd=.2)+
+  geom_point(data=data,aes(x=GPOS,y=avgp_wald,col=COL),size=.01)+
+  geom_text(data=musks,aes(x=muskX,y=9.5,label=musk))+
+  scale_y_continuous(name = yl,breaks=0:4*2.5,labels = c(0,'',5,'',10))+
+  scale_x_continuous(expand = c(0,0),breaks = (karyo$GSTART+karyo$GEND)/2,labels = 1:24,position = "top")+
+  scale_color_manual(name='comparison',values=clr)+
+  scale_fill_manual(values = c(NA,lgclr),guide=F)+
+  theme_ipsum(grid = F,
+              axis_title_just = 'c',
+              axis_title_family = 'bold',
+              strip_text_size = 9)+
+  theme(plot.margin = unit(c(2,25,40,0),'pt'),
+        panel.spacing.y = unit(3,'pt'),
+        legend.position = 'none',
+        axis.title.x = element_blank(),
+        axis.ticks.y = element_line(color = annoclr),
+        axis.line.y =  element_line(color = annoclr),
+        axis.text.y = element_text(margin = unit(c(0,3,0,0),'pt')),
+        axis.title.y = element_text(vjust = -1),
+        strip.background = element_blank(),
+        strip.text = element_blank()
+        )+
+  facet_grid(RUN~.)
+
+legGrob <- gTree(children=gList(pictureGrob(readPicture("../../0_data/0_img/legend-pw-cairo.svg"))))
+carGrob <- gTree(children=gList(pictureGrob(readPicture("../../0_data/0_img/caribbean-cairo.svg"))))
+panGrob <- gTree(children=gList(pictureGrob(readPicture("../../0_data/0_img/panama-cairo.svg"))))
+belGrob <- gTree(children=gList(pictureGrob(readPicture("../../0_data/0_img/belize-cairo.svg"))))
+honGrob <- gTree(children=gList(pictureGrob(readPicture("../../0_data/0_img/honduras-cairo.svg"))))
+
+ys <- .0765
+ysc <- .0057
+yd <- .2195
+boxes = data.frame(x=rep(.9675,4),y=c(ys,ys+yd+ysc,ys+(yd+ysc)*2,ys+(yd+ysc)*3))
+
+S02 <- ggdraw(p1)+
+  geom_rect(data = boxes, aes(xmin = x, xmax = x + .02, ymin = y, ymax = y + yd),
+            colour = NA, fill = c(rep(lgclr,3),annoclr))+
+  draw_label('Global', x = .9775, y = boxes$y[4]+.08,
+             size = 9, angle=-90,col='white')+
+  draw_label('Belize', x = .9775, y = boxes$y[2]+.08,
+             size = 9, angle=-90)+
+  draw_label('Honduras', x = .9775, y = boxes$y[1]+.08,
+             size = 9, angle=-90)+
+  draw_label('Panama', x = .9775, y = boxes$y[3]+.08,
+              size = 9, angle=-90)+
+  draw_grob(legGrob, 0.01, 0, 1, 0.08)+
+  draw_grob(carGrob, 0.954, boxes$y[4]+.78*yd, .045, .045)+
+  draw_grob(belGrob, 0.954, boxes$y[2]+.78*yd, .045, .045)+
+  draw_grob(honGrob, 0.954, boxes$y[1]+.78*yd, .045, .045)+
+  draw_grob(panGrob, 0.954, boxes$y[3]+.78*yd, .045, .045)
+
+ggsave(plot = S02,filename = '../output/S02.png',width = 183,height = 183,dpi = 200,units = 'mm')
+#ggsave('fst_mac02_weight_99.98.eps',width = 183,height = 183,dpi = 200,units = 'mm')

3_figures/F_scripts/S04.R

+#!/usr/bin/env Rscript
+library(tidyverse)
+library(cowplot)
+library(tximport)
+library(DESeq2)
+library(vsn)
+library(gplots)
+library(ggforce)
+library(grid)
+library(gridSVG)
+library(grImport2)
+library(grConvert)
+
+clp <- function(x){y<-substr(x,2,nchar(x));y}
+
+base_dir <- "../../2_output/09_expression/kallisto/"
+samples <- substr(dir(base_dir),10,nchar(dir(base_dir)))
+kal_dirs <- sapply(samples, function(id) file.path(base_dir, paste("kallisto_",id,sep='')))
+
+pops <- read.csv('../../0_data/0_resources/retina_samples_ID.txt',sep='\t')[1:24,] %>% mutate(pop=substr(population,4,6))
+s2c <- data.frame(path=kal_dirs, sample=samples, population = pops$pop, stringsAsFactors=FALSE)
+
+t2g <- read.csv('../../0_data/0_resources/transcripts2genname.txt',sep='\t',stringsAsFactors = F)
+retDesign <- read.csv('../../0_data/0_resources/retina_samples_ID_altVersion.csv',sep='\t',row.names=1)
+
+txi.kallisto <- tximport(paste(kal_dirs,"/abundance.tsv",sep=''),
+                         type = "kallisto", txOut=T,tx2gene = t2g,
+                         geneIdCol="transcripts2genname.txt")
+
+testdf <- data.frame(abund = row.names(txi.kallisto$abundance),
+                     count = row.names(txi.kallisto$counts),
+                     length=row.names(txi.kallisto$length),
+                     t1=t2g$target_id,
+                     t2=t2g$HPgene) %>% mutate(check = (abund!=count)+(abund!=length)+(abund!=t1))
+
+row.names(txi.kallisto$abundance) <- t2g$HPgene
+row.names(txi.kallisto$counts) <- t2g$HPgene
+row.names(txi.kallisto$length) <- t2g$HPgene
+
+samples <- pops%>%select(rID,pop);row.names(samples) <- samples$rID 
+dds <- DESeqDataSetFromTximport(txi.kallisto, samples, ~pop)
+dds <- DESeq(dds)
+
+res <- list()
+res[[1]] <- results(dds,contrast = c("pop","pue","nig"))
+res[[1]] <- res[[1]][order(res[[1]]$padj),]
+
+res[[2]] <- results(dds,contrast = c("pop","pue","uni"))
+res[[2]] <- res[[2]][order(res[[2]]$padj),]
+
+res[[3]] <- results(dds,contrast = c("pop","nig","uni"))
+res[[3]] <- res[[3]][order(res[[3]]$padj),]
+
+selGene <- c(rownames(res[[1]])[1:3],
+             rownames(res[[3]])[1:3],
+             rownames(res[[2]])[1:3],
+             "SWS2abeta","SWS2aalpha","SWS2b","LWS",
+             "Casz1_2-1","Casz1_2-2","Casz1_2-3","invs",
+             "RORB")
+
+select <- (counts(dds,normalized=TRUE) %>% rownames()) %in% selGene
+
+hmcol <- colorRampPalette(viridis::plasma(9))(100)
+
+annodat <- data.frame(xmin=c(.5,9.5,19.5),
+                      xmax=c(9.5,19.5,24.5),
+                      ymin=rep(.1,3),
+                      ymax=rep(.4,3),
+                      clr=c('black',"#d45500",'black'),
+                      fll=c('black',"#d45500",'white'))
+
+rld <- rlogTransformation(dds, blind=TRUE)
+
+testdat <- as.data.frame(assay(rld)[select,order(retDesign$population)])%>%
+  tibble::rownames_to_column(var = 'gene') %>% 
+  gather(.,key = 'sample',value = 'counts',2:25)
+
+ordS <- retDesign %>% tibble::rownames_to_column(var = 'Sample') %>% arrange(population) 
+
+testdat$sample <- factor(as.character(testdat$sample),levels = ordS$Sample)
+testdat$gene <- factor(as.character(testdat$gene),levels =rev(selGene[!duplicated(selGene)]))
+
+d2 <- data.frame(name = factor(rev(selGene[!duplicated(selGene)]),
+                               levels=rev(selGene[!duplicated(selGene)])))
+
+d3 <- left_join(data.frame(name=selGene,comp=c(rep('np',3),rep('nu',3),
+                                               rep('pu',3),rep('vision',9))),d2,by='name') %>%
+  group_by(name) %>% mutate(n = 1/length(comp),s=(row_number()-1)/2*2*pi,e=s+n*2*pi) %>% 
+  ungroup() %>% mutate(name = as.character(name),clr=c('#fb8620','#d93327','#1b519c','#333333')[as.numeric(comp)])
+
+d3$name <- factor(d3$name,levels=rev(selGene[!duplicated(selGene)]))
+testdat <- testdat %>% mutate(lab = tolower(gene))
+
+YL <- expression(italic("rorb"),italic("invs"),
+                 italic("casz1"^3),
+                 italic("casz1"^2),italic("casz1"^1),italic("lws"),
+                 italic("sws2b"),italic(paste(sws2a,"\u03B1")),
+                 italic(paste(sws2a,"\u03B2")),italic("hpv1g3179"),
+                 italic("wdfy3_1"),italic("hmgxb3"),italic("tuba1a"),
+        bolditalic("cyp3a27"),bolditalic("sema4b"),italic("fam96b"),bolditalic("chac1_1"))
+
+testdat$sample <- factor(clp(as.character(testdat$sample)),levels=clp(levels(testdat$sample)))
+
+p2 <- ggplot(testdat,aes(x=sample,y=gene,fill=counts))+
+  geom_tile()+coord_equal(xlim = c(0.5,26.1))+
+  geom_rect(inherit.aes = F,data=annodat,
+       aes(xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax),col='black',fill=annodat$fll)+
+  geom_circle(inherit.aes = F,data=d2, aes(x0 = 25.2,y0=as.numeric(name),r=.4),fill=rgb(.9,.9,.9))+
+  geom_arc(inherit.aes = F,
+           aes(x0 = 25.2,y0 = as.numeric(as.factor(name)), r = .4, start = s, end = e,col=comp),size=1.5,
+           data = d3)+
+  scale_x_discrete(name='',expand = c(0,0))+
+  scale_y_discrete(name='Gene',labels=YL)+
+  viridis::scale_fill_viridis(name="Expression: ",option = 'C',
+                              guide = guide_colorbar(direction = "horizontal",title.position = "top"))+
+  scale_color_manual(name="",values=c('#fb8620','#d93327','#1b519c','#666666'),guide=F)+
+  theme_minimal(base_size = 13)+
+  theme(axis.text.x = element_text(angle=90),
+        plot.margin = unit(c(1,1,50,1),'pt'),
+        axis.text.y =  element_text(face = 'italic'),
+        legend.position = c(.05,-.28),panel.grid = element_blank(),legend.key.width = unit(30,'pt'))
+
+legGrob <- gTree(children=gList(pictureGrob(readPicture("../../0_data/0_img/legend-expression-cairo.svg"))))
+
+Cdf3 <- as.data.frame(assay(rld)[,order(retDesign$population)]) %>% 
+  tibble::rownames_to_column(var = 'gene') %>% 
+  gather(key = "Sample",value = "Count",R28385:RPL17_01)
+
+selGene2 <- c("SWS2abeta","SWS2aalpha","SWS2b","LWS",
+             "Casz1_2-1","Casz1_2-2","Casz1_2-3",
+             "invs","RORB")
+
+Cgenes2 <- as.data.frame(assay(rld)[,order(retDesign$population)]) %>% 
+  tibble::rownames_to_column(var = 'gene') %>% 
+  filter(gene %in% selGene2) %>%
+  gather(key = "Sample",value = "Count",R28385:RPL17_01)
+
+Flab <- expression("All",italic("casz1"^1),italic("casz1"^2),italic("casz1"^3),
+                   italic("invs"),italic("lws"),italic("rorb"),
+                   italic(paste("  sws2a","\u03B1")),italic(paste("  sws2a","\u03B2")),italic("sws2b"))
+
+
+p3 <- ggplot(Cdf3,aes(x=0,y=Count))+
+  geom_boxplot(aes(fill='all'))+
+  geom_boxplot(inherit.aes = F,
+               data=Cgenes2,
+               aes(x=as.numeric(as.factor(gene)),y=Count,
+                   group=gene,fill=gene))+
+  scale_x_continuous('Gene',expand=c(0,0),breaks=0:9,labels=Flab)+
+  scale_y_continuous(expression(Count~(italic(rld(n)))),expand=c(0,0))+
+  scale_fill_manual('Gene',values = c('lightgray',viridis::viridis(9)),
+                    labels=Flab)+
+  theme_minimal(base_size = 13)+
+  theme(legend.position = 'none',
+        axis.text.x = element_text(angle=90))
+
+S04 <- ggdraw()+
+  draw_plot(p2,0,0,.6,1)+
+  draw_plot(p3,.61,.17,.39,.775)+
+  draw_grob(legGrob, 0.2, 0, 0.8, 0.17)+
+  draw_plot_label(x=c(0,.6),y=c(.99,.99),label = c('a','b'))
+
+ggsave(plot = S04, filename = '../output/S04.pdf',width=12,height=6,device = cairo_pdf)

3_figures/F_scripts/S05.R

+#!/usr/bin/env Rscript
+library(grid)
+library(gridSVG)
+library(grImport2)
+library(grConvert)
+library(tidyverse)
+library(cowplot)
+library(hrbrthemes)
+karyo <- read.csv('../../0_data/0_resources/F2.karyo.txt',sep='\t') %>% 
+  mutate(GSTART=lag(cumsum(END),n = 1,default = 0),
+         GEND=GSTART+END,GROUP=rep(letters[1:2],12)) %>% 
+  select(CHROM,GSTART,GEND,GROUP)