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GWAS2TBACK.Rmd
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---
output: pdf_document
geometry: margin=1in
mainfont: Times New Roman
---
```{r,fig.width=7.5, fig.height=4,warning=F, message=F, echo=F,comment="K",results='asis'}
library(pander)
library(dplyr)
library(ggplot2)
library(data.table)
library(grid)
library(stringr)
library(gridExtra)
library(knitr)
library(tidyr)
library(scales)
library(gtable)
panderOptions('knitr.auto.asis', TRUE)
setwd("/Users/kristen/Documents/transposon_figure_data/data")
load("Processed_Transposon_Mappings_2.Rda")
#load("Processed_Transposon_Mappings_SUBSET.Rda")
#load("Processed_Transposon_Mappings_SUBSET2.Rda")
#load("position_QTL.Rda")
load("count_QTL.Rda")
pg<-read.table("paragraphs.txt",sep="\t",header=TRUE)
# kexpand=function(){
# cat(knit(
# text=knit_expand(text=
# "<<yfig-{{cap}}-,fig.cap='{{cap}}',results='markup',echo=FALSE,fig.height={{figheight}},out.height={{outheight}}>>=\n
# .q\n
# @"
# )
# ))}
# .q=qplot(1:10);cap="first caption";figheight=9;outheight=90
# kexpand()
# make cure C and frac are not getting confused
#PxG function
hm<-processed_mapping_df
hm<-distinct(processed_mapping_df, trait,strain,peak_id)
hm$allele <- factor(hm$allele,
levels = c(-1,1),
labels = c("REF", "ALT"))
c<-filter(processed_mapping_df, log10p> BF)
#d<-filter(processed_mapping_df,peak_id=="2", trait=="absent_TRANS_CER1_C")
e<-filter(processed_mapping_df,!is.na(peak_id), trait=="absent_TRANS_CER1_C")
f<-distinct(e,POS,peak_id)
gwasPxG <- function(trt,specific_peak){
#load("~/Dropbox/AndersenLab/RCode/Stefan/good_gwasMappingsINlinkage_phenotypes.Rda")
hm %>%
filter(trait==trt,peak_id==specific_peak,!is.na(allele))%>%
ggplot(.)+
aes(x=allele,y = value,fill=as.factor(allele))+
geom_boxplot(outlier.shape=NA,size =.5,color="gray52")+
geom_point(size = 1, alpha = .8,position=position_jitter(w=.4, h=.025),na.rm=TRUE)+
#scale_fill_brewer(palette = "Set2")+
#geom_point(size = 1, alpha = .8,position=position_jitter(w=.4, h=.025),na.rm=TRUE)+
# geom_jitter(size = 3, alpha = .8,postion=position_jitter(0, 0)) +
theme_bw()+
theme(axis.text.x = element_text(size=9, , color="black"),
axis.text.y = element_text(size=9, color="black"),
axis.title.x = element_text(size=9, color="black"),
axis.title.y = element_text(size=9, color="black",vjust=1),
strip.text.x = element_text(size=9, color="black"),
strip.text.y = element_text(size=9, color="black"),
plot.title = element_text(size=9, vjust=1),
legend.title = element_text(size=9),
panel.border = element_rect(size=1, colour = "black"),
plot.margin = unit(c(.05,.05,.05,.05), "cm"),
legend.position = "none")+
scale_y_continuous(breaks= pretty_breaks())+
labs( x = "Genotype",y="Value")+
scale_fill_manual( values = c("darkgray", "burlywood2", "darkolivegreen","black"))
}
# pull unique combos, remove strain column(don't need specific strain info at this point)
#processed_mapping_df <- distinct(select(processed_mapping_df, -strain,-allele,-value))
processed_mapping_df<- processed_mapping_df %>% distinct(trait,marker,strain)
#create family and method columns
processed_mapping_df$family <- stringr::str_split_fixed(processed_mapping_df$trait, "_TRANS_",2)[,2]
processed_mapping_df$method <- stringr::str_split_fixed(processed_mapping_df$trait, "_TRANS_",2)[,1]
#read in position data and create family column
positions <- read.table("CtCp_all_nonredundant.txt")
names(positions)<-c("CHROM","start","end","TE","orientation","method","strain","class")
positions$family<- stringr::str_split_fixed(positions$TE, regex("_(non-)?reference"),2)[,1]
positions$family<- paste(stringr::str_split_fixed(positions$family, "_",4)[,3],stringr::str_split_fixed(positions$family, "_",4)[,4],sep="_")
positions$family <- gsub("_$" ,"",positions$family)
positions$family <- gsub("_non-reference(.*)$" ,"",positions$family)
#select traits above BF.....this step not needed, double checking everything is above BF
selection<-filter(processed_mapping_df, log10p > BF)
#extract the count base traits
base_traits <-selection[(selection$method=="absent"| selection$method=="new" |selection$method=="reference"|selection$method=="ZERO_new"|selection$method=="ONE_new"), ]
counts<-subset(base_traits, grepl("_C$", base_traits$family))
counts$family <- gsub("_C$" ,"",counts$family)
#
#
###REMOVE LATER!!!!!
#
#
processed_mapping_df <- distinct(select(processed_mapping_df, -strain,-allele,-value))
processed_mapping_df<- processed_mapping_df %>% distinct(trait,marker)
#pull out only position traits from mappings dataframe
position_traits<-subset(selection,
grepl('^I', selection$trait) |
grepl('^V', selection$trait) |
grepl('^X', selection$trait))
#create family column
position_traits$family <- paste(stringr::str_split_fixed(position_traits$trait, "_",4)[,3],stringr::str_split_fixed(position_traits$trait, "_",4)[,4],sep="_")
position_traits$family <- gsub("_$" ,"",position_traits$family)
position_traits$family <- gsub("_non-reference(.*)$" ,"",position_traits$family)
# add position TRAIT_col family info to processed_mapping_df
processed_mapping_df<-processed_mapping_df %>%mutate(family = ifelse(processed_mapping_df$trait %in% position_traits$trait, (paste(stringr::str_split_fixed(processed_mapping_df$trait, "_",4)[,3],stringr::str_split_fixed(processed_mapping_df$trait, "_",4)[,4],sep="_")), processed_mapping_df$family))
#bind count and position traits option...choose oen of below three
#selection<-rbind(counts,position_traits)
selection<-counts
#selection<-position_traits
#
#strip count marker and remnant marks from dataframes
selection$trait <- gsub("_C$" ,"",selection$trait)
hm$trait <- gsub("_C$" ,"",hm$trait)
processed_mapping_df$trait <- gsub("_C$" ,"",processed_mapping_df$trait)
processed_mapping_df$family <- gsub("_C$" ,"",processed_mapping_df$family)
processed_mapping_df$family <- gsub("_$" ,"",processed_mapping_df$family)
processed_mapping_df$family <- gsub("_non-reference(.*)$" ,"",processed_mapping_df$family)
processed_mapping_df<-mutate(processed_mapping_df,ID=paste(trait,peak_id,sep="_"))
copy<-processed_mapping_df
##FIX HERE
processed_mapping_df<-mutate(processed_mapping_df,SNP_col=ifelse(is.na(peak_id), "NO","PASS"))
#### NEED TO FIX HERE
count_QTL<-mutate(count_QTL, trait2=gsub("_\\d+$","",trait))
#position_QTL<-mutate(position_QTL, trait2=gsub("_\\d+$","",trait))
#skip the below
#selection <- filter(selection, (trait %in% count_QTL$trait2 |trait %in% position_QTL$trait2))
processed_mapping_df<-filter(processed_mapping_df,CHROM != "MtDNA")
#load("reciprocal_removals.Rda")
#processed_mapping_df<-filter(processed_mapping_df,!(trait %in% reciprocal_removals$TE))
#selection<-filter(selection,!(trait %in% reciprocal_removals$TE))
selection<-filter(selection,grepl('total',family))
#HERE
class_subset<- positions %>% distinct(class,family) %>% select(class,family)
#skip the below
#selection <-merge(selection, class_subset, by="family")
selection<-arrange(selection,family,method)
label<-expression(bold(-log["10"](p)))
i="ONE_new_TRANS_total"
for (i in unique(selection$trait)){
specific_trait<- processed_mapping_df[processed_mapping_df$trait == i, ]
empty <-specific_trait[specific_trait$method==NA,]
#specific_trait_mx <- max(specific_trait$log10p)
class_TE<-unique(filter(selection,trait==i)$class)
pvalues<-filter(specific_trait,log10p !="Inf") #
specific_trait_mx <- max(pvalues$log10p) #
TE<-specific_trait$family[1]
rect_data<-filter(specific_trait,SNP_col==ifelse(is.na(peak_id), "NO", "PASS"))
plot_method<-unique(filter(selection,trait==i)$method)
plot_title<-gsub(".*_TRANS_","",i)
plot_title<-gsub("_CE$","",plot_title)
plot_title<-gsub("WBTransposon","WBT",plot_title)
plot_title<-gsub("total","Total",plot_title)
plot_title<-gsub("Total$","Total Transposons",plot_title)
plot_title<-gsub("_"," ",plot_title)
plot_title<-gsub("retrotransposon","Retrotransposons",plot_title)
plot_title<-gsub("dnatransposon","DNA Transposons",plot_title)
plot_title<-paste(plot_title,ifelse(plot_method=="ZERO_new","(ins)",ifelse(plot_method=="absent","(abs)",ifelse(plot_method=="ONE_new", "(ins)",ifelse(plot_method=="new", "(ins)", "(ref)")))),sep="")
plot_title
##check for NAs
#sapply(Mappings, function(x)all(is.na(x)))
A<- processed_mapping_df %>%
filter(trait == i)%>%
.[order(.$peak_id,na.last=FALSE),]%>%
ggplot(.)+
aes(x=POS/1e6,y=log10p)+
geom_rect(data=rect_data,mapping=aes(xmin=startPOS/1e6, xmax=endPOS/1e6, ymin=0, ymax= Inf),fill="thistle1", alpha=1)+
geom_point(aes( color=ifelse(log10p> BF & SNP_col=="PASS", 'red', 'black')),size=1)+
facet_grid(.~CHROM,scale="free_x",space = "free_x")+scale_color_identity() +
ggtitle(plot_title)+
geom_hline(aes(yintercept=BF),color="grey60",linetype="dashed")+
theme(strip.background = element_rect(fill = "white"),
strip.text.x = element_text(size = 9, colour = "black",face="bold"),
panel.background = element_rect(fill = "white"),
panel.border = element_rect(color="black", size=0.5, linetype="solid", fill=NA),
panel.margin = unit(.6, "lines"),
axis.ticks =element_line(colour = "black"),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"),
axis.title=element_text(size=9,face="bold"),
plot.margin=unit(c(.1,.1,.1,.1), "cm"),
#ADDD COLOR HERE STILLLL
# plot.title = element_text(colour=ifelse(class_TE=="dnatransposon","navy",ifelse(class_TE=="retrotransposon","brown3","darkgoldenrod2"))),
legend.position=('none'))+
labs(x="Chromosome Position (Mb)",y=label)+
scale_y_continuous(expand=c(0,0),limits=c(0,specific_trait_mx+.075*specific_trait_mx),labels = function(x) format(x,width = 4),breaks= pretty_breaks())
plot(A)
plist<-vector()
for (p in rect_data$peak_id){
plist<-c(plist,p)
}
plist<-sort(plist)
box_list <- lapply(c(plist),FUN=function(x){gwasPxG(i,x)})
(do.call("grid.arrange", c(box_list, ncol=length(plist))))
TE_info<-filter(pg,trait==i)
TE_info<-select(TE_info,info)
TE_info<-TE_info[1,]
TE_info<-toString(TE_info)
pandoc.p(TE_info)
cat("\n\n\\pagebreak\n")
}
```