updated_resampling_strategy2.Rmd

---
title: "updated_resampling_strategy2"
author: "Haider Inam"
date: "5/30/2021"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir=rprojroot::find_rstudio_root_file())
```


```{r}
# rm(list=ls())
library(knitr)
library(tictoc)
library(workflowr)
library(VennDiagram)
library(dplyr)
library(foreach)
library(doParallel)
library(ggplot2)
library(reshape2)
library(RColorBrewer)
library(devtools)
library(ggsignif)
library(plotly)
library(BiocManager)

# library(ensembldb) #Loading this with Dplyr commands seems to throw an error in Rmd
# library(EnsDb.Hsapiens.v86) #Loading this with Dplyr commands seems to throw an error in Rmd

# source("code/contab_maker.R")
# source("code/alldata_compiler.R")
# source("code/quadratic_solver.R")
# source("code/mut_excl_genes_generator.R")
# source("code/mut_excl_genes_datapoints.R")
# source("code/simresults_generator.R")

source("../code/contab_maker.R")
source("../code/alldata_compiler.R")
source("../code/quadratic_solver.R")
source("../code/mut_excl_genes_generator.R")
source("../code/mut_excl_genes_datapoints.R")
source("../code/simresults_generator.R")


######################Cleanup for GGPlot2#########################################
cleanup=theme_bw() +
  theme(plot.title = element_text(hjust=.5),
        panel.grid.major = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.background = element_blank(),
        # axis.line = element_line(color = "black"),
        axis.text = element_text(face="bold",color="black",size="11"),
        text=element_text(size=11,face="bold"),
        axis.title=element_text(face="bold",size="11"))
```


```{r}
  nameposctrl1<-'BRAF'
  #Positive control 1
  nameposctrl2<-'NRAS'
  #Oncogene in Question
  namegene<-'ATI'
  #Mutation Boolean (Y or N)
  mtn<-'N'
  #Name Mutation for Positive Ctrl 1
  nameposctrl1mt<-'V600E'
  #Name of Mutation for Positive Ctrl 2
  nameposctrl2mt<-'Q61L'

# alldata=read.csv("output/all_data_skcm.csv",sep=",",header=T,stringsAsFactors=F)
alldata=read.csv("../output/all_data_skcm.csv",sep=",",header=T,stringsAsFactors=F)
head(alldata)
```
Contab downsampler function that the next code chunk uses:
```{r}
contab_downsampler<- function(contab,prob) {
  p11=contab[1,1]
  p10=contab[1,2]
  p01=contab[2,1]
  p00=contab[2,2]
  contab_downsampled=contab ###Just making a dummy contingency table
  contab_downsampled[1,1]=prob*p11/(p10+p11)
  contab_downsampled[1,2]=prob*p10/(p10+p11)
  contab_downsampled[2,1]=(1-prob)*p01/(p00+p01)
  contab_downsampled[2,2]=(1-prob)*p00/(p00+p01)
  contab_downsampled
}
```
Another function that takes in a contingency table, and resamples is based on its probabilities on a multinomial distribution. Number of resampling simulations are an input of the function
```{r}
contab_simulator=function(contab,nsims,cohort_size){
  contabs_resampled=rmultinom(nsims, #number of datasets to generate
          cohort_size, #sample size of the total population
          c(contab[1,1],
            contab[1,2],
            contab[2,1],
            contab[2,2])) #probability of positive event
  contabs_resampled_t=t(contabs_resampled)
  colnames(contabs_resampled_t)=c("p11","p10","p01","p00")
  contabs_resampled_t
}

# contab_simulator=function(contab,nsims,cohort_size){
#   contabs_resampled=rmultinom(nsims, #number of datasets to generate
#           1, #sample size of the total population
#           c(contab[1,1],
#             contab[1,2],
#             contab[2,1],
#             contab[2,2])) #probability of positive event
#   contabs_resampled_t=t(contabs_resampled)
#   colnames(contabs_resampled_t)=c("p11","p10","p01","p00")
#   contabs_resampled_t
# }
# contab=pc1pc2_contab_probabilities
# nsims=1000
# cohort_size=cohort_size_curr
# pc1rawpc2_contabs_sims=contab_simulator(pc1pc2_contab_probabilities,1000,cohort_size_curr)
```



```{r}
# rm(list=ls())
###Not mutation specific generation of counts###
alldata_comp=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,'N',"N/A","N/A")[[2]]
head(alldata_comp)

###Calculating Odds ratios and GOI frequencies for the raw data###
cohort_size=length(alldata_comp$Positive_Ctrl1)
pc1pc2_contab_counts=contab_maker(alldata_comp$Positive_Ctrl1,alldata_comp$Positive_Ctrl2,alldata_comp)[2:1, 2:1]
goipc1_contab_counts=contab_maker(alldata_comp$genex,alldata_comp$Positive_Ctrl1,alldata_comp)[2:1, 2:1]
###Had to add the 2:1 bits because the contab maker spits out NN YY whereas we wanted YNYN
goipc2_contab_counts=contab_maker(alldata_comp$genex,alldata_comp$Positive_Ctrl2,alldata_comp)[2:1, 2:1]

# pc1pc2_contab_counts=gene_pair_2_table
# goipc1_contab_counts=contab_maker(alldata_comp$genex,alldata_comp$Positive_Ctrl1,alldata_comp)[2:1, 2:1]
# goipc1_contab_counts=gene_pair_1_table

cohort_size_curr=cohort_size
# goipc2_contab_counts=contab_maker(alldata_comp$genex,alldata_comp$Positive_Ctrl2,alldata_comp)[2:1, 2:1]
pc1pc2_contab_probabilities=pc1pc2_contab_counts/cohort_size_curr
goipc1_contab_probabilities=goipc1_contab_counts/cohort_size_curr
goipc2_contab_probabilities=goipc2_contab_counts/cohort_size_curr
# pc1pc2_contab_probabilities=pc1pc2_contab_counts
# goipc1_contab_probabilities=goipc1_contab_counts
# goipc2_contab_probabilities=goipc2_contab_counts/cohort_size

or_pc1pc2=pc1pc2_contab_probabilities[1,1]*pc1pc2_contab_probabilities[2,2]/(pc1pc2_contab_probabilities[1,2]*pc1pc2_contab_probabilities[2,1])
or_goipc1=goipc1_contab_probabilities[1,1]*goipc1_contab_probabilities[2,2]/(goipc1_contab_probabilities[1,2]*goipc1_contab_probabilities[2,1])
or_goipc2=goipc2_contab_probabilities[1,1]*goipc2_contab_probabilities[2,2]/(goipc2_contab_probabilities[1,2]*goipc2_contab_probabilities[2,1])

goi_freq=goipc1_contab_probabilities[1,1]+goipc1_contab_probabilities[1,2]
goi_freq=.25
# class(goi_freq)

###

###Downsampling PC1 to the probability of GOI without changing ORs###
###The function below converts contingency table data to a new contingency table in which the data is downsampled to the desired frequency, aka the frequency of the GOI in this case###
pc1new_pc2_contab=contab_downsampler(pc1pc2_contab_probabilities,goi_freq)
goinew_pc1_contab=contab_downsampler(goipc1_contab_probabilities,goi_freq)
goinew_pc2_contab=contab_downsampler(goipc2_contab_probabilities,goi_freq)
##original contab:
head(pc1pc2_contab_probabilities)
###downsampled contab:
head(pc1new_pc2_contab)

pc1rawpc2_contabs_sims=contab_simulator(pc1pc2_contab_probabilities,1000,cohort_size_curr)
pc1pc2_contabs_sims=contab_simulator(pc1new_pc2_contab,1000,cohort_size_curr)
goipc1_contabs_sims=contab_simulator(goinew_pc1_contab,1000,cohort_size_curr)
goipc2_contabs_sims=contab_simulator(goinew_pc2_contab,1000,cohort_size_curr)
# goipc2_contabs_sims=contab_simulator(goinew_pc2_contab,1000,cohort_size)
# head(pc1pc2_contabs_sims) #each row in this dataset is a new contab
pc1rawpc2_contabs_sims=data.frame(pc1rawpc2_contabs_sims)
  pc1rawpc2_contabs_sims=pc1rawpc2_contabs_sims%>%
  mutate(or=p11*p00/(p10*p01))

pc1pc2_contabs_sims=data.frame(pc1pc2_contabs_sims)
  pc1pc2_contabs_sims=pc1pc2_contabs_sims%>%
  mutate(or=p11*p00/(p10*p01))
  
goipc1_contabs_sims=data.frame(goipc1_contabs_sims)
  goipc1_contabs_sims=goipc1_contabs_sims%>%
  mutate(or=p11*p00/(p10*p01))
  
goipc2_contabs_sims=data.frame(goipc2_contabs_sims)
goipc2_contabs_sims=goipc2_contabs_sims%>%
mutate(or=p11*p00/(p10*p01))
  
# goipc2_contabs_sims=data.frame(goipc2_contabs_sims)
#   goipc2_contabs_sims=goipc2_contabs_sims%>%
#   mutate(or=p11*p00/(p10*p01))
pc1rawpc2_contabs_sims$comparison="pc1rawpc2"
pc1pc2_contabs_sims$comparison="pc1pc2"
goipc1_contabs_sims$comparison="goipc1"
goipc2_contabs_sims$comparison="goipc2"
or_median_raw=quantile(pc1rawpc2_contabs_sims$or,na.rm = T)[3]
or_uq_raw=quantile(pc1rawpc2_contabs_sims$or,na.rm = T)[4]
or_median_downsampled=quantile(pc1pc2_contabs_sims$or,na.rm = T)[3]
or_uq_downsampled=quantile(pc1pc2_contabs_sims$or,na.rm = T)[4]

pc1rawpc2_contabs_sims=pc1rawpc2_contabs_sims%>%
  mutate(isgreater_raw_median=case_when(or>or_median_raw~1,
                             TRUE~0),
         isgreater_raw_uq=case_when(or>or_uq_raw~1,
                             TRUE~0),
         isgreater_median=case_when(or>or_median_downsampled~1,
                             TRUE~0),
         isgreater_uq=case_when(or>or_uq_downsampled~1,
                             TRUE~0)
         )
pc1pc2_contabs_sims=pc1pc2_contabs_sims%>%
  mutate(isgreater_raw_median=case_when(or>or_median_raw~1,
                             TRUE~0),
         isgreater_raw_uq=case_when(or>or_uq_raw~1,
                             TRUE~0),
         isgreater_median=case_when(or>or_median_downsampled~1,
                             TRUE~0),
         isgreater_uq=case_when(or>or_uq_downsampled~1,
                             TRUE~0)
         )
goipc1_contabs_sims=goipc1_contabs_sims%>%
  mutate(isgreater_raw_median=case_when(or>or_median_raw~1,
                             TRUE~0),
         isgreater_raw_uq=case_when(or>or_uq_raw~1,
                             TRUE~0),
         isgreater_median=case_when(or>or_median_downsampled~1,
                             TRUE~0),
         isgreater_uq=case_when(or>or_uq_downsampled~1,
                             TRUE~0)
         )

goipc2_contabs_sims=goipc2_contabs_sims%>%
  mutate(isgreater_raw_median=case_when(or>or_median_raw~1,
                             TRUE~0),
         isgreater_raw_uq=case_when(or>or_uq_raw~1,
                             TRUE~0),
         isgreater_median=case_when(or>or_median_downsampled~1,
                             TRUE~0),
         isgreater_uq=case_when(or>or_uq_downsampled~1,
                             TRUE~0)
         )
# pc1pc2_contabs_sims=pc1pc2_contabs_sims%>%
#   mutate(isgreater=case_when(or>=or_pc1pc2~1,
#                              TRUE~0))
# goipc1_contabs_sims=goipc1_contabs_sims%>%
#   mutate(isgreater=case_when(or>=or_pc1pc2~1,
#                              TRUE~0))
# goipc2_contabs_sims=goipc2_contabs_sims%>%
#   mutate(isgreater=case_when(or>=or_pc1pc2~1,
#                              TRUE~0))
pc1rawpc2_isgreater_raw_median=sum(pc1rawpc2_contabs_sims$isgreater_raw_median)
pc1rawpc2_isgreater_raw_uq=sum(pc1rawpc2_contabs_sims$isgreater_raw_uq)
pc1rawpc2_isgreater_median=sum(pc1rawpc2_contabs_sims$isgreater_median)
pc1rawpc2_isgreater_uq=sum(pc1rawpc2_contabs_sims$isgreater_uq)

pc1pc2_isgreater_raw_median=sum(pc1pc2_contabs_sims$isgreater_raw_median)
pc1pc2_isgreater_raw_uq=sum(pc1pc2_contabs_sims$isgreater_raw_uq)
pc1pc2_isgreater_median=sum(pc1pc2_contabs_sims$isgreater_median)
pc1pc2_isgreater_uq=sum(pc1pc2_contabs_sims$isgreater_uq)

goipc1_isgreater_raw_median=sum(goipc1_contabs_sims$isgreater_raw_median)
goipc1_isgreater_raw_uq=sum(goipc1_contabs_sims$isgreater_raw_uq)
goipc1_isgreater_median=sum(goipc1_contabs_sims$isgreater_median)
goipc1_isgreater_uq=sum(goipc1_contabs_sims$isgreater_uq)


plotting_df=rbind(pc1pc2_contabs_sims,goipc1_contabs_sims,goipc2_contabs_sims)
# plotting_df=rbind(pc1pc2_contabs_sims,goipc1_contabs_sims)
# 


ggplot(plotting_df,aes(x=(or),fill=comparison))+
  geom_histogram(bins=40,alpha=0.55,position="identity")+
  # geom_histogram(bins=50,alpha=0.55)+
  scale_y_continuous(expand=c(0,0),name="Count")+
  scale_x_continuous(expand=c(0,0),trans="log10",name="Odds Ratio")+
  scale_fill_brewer(palette="Set2")+
  # geom_vline(xintercept = or_pc1pc2)+
  cleanup

ggplot(plotting_df,aes(y=(or),x=comparison),fill=factor(comparison))+
  geom_boxplot()+
  scale_y_continuous(name="Odds Ratio",trans="log10")+
  scale_x_discrete(name="")+
  scale_fill_brewer(palette="Set2")+
  geom_hline(yintercept = or_uq_downsampled,linetype="dashed")+
  cleanup+
  theme(legend.position = "none",
        axis.ticks.x = element_blank())

# ggsave("paircon_boxplot.pdf",width = 3,height=2,units="in",useDingbats=F)

ggplot(plotting_df%>%filter(comparison%in%"pc1pc2"),aes(x=or,fill=comparison))+
  geom_histogram(bins=50,alpha=0.55,position="identity")+
  geom_density(alpha=.6)+
  scale_y_continuous(expand=c(0,0),name="Count")+
  scale_x_continuous(expand=c(0,0),name="Odds Ratio")+
  # geom_vline(xintercept = or_pc1pc2)+
  cleanup




```
```{r}
# rm(list=ls())
###Not mutation specific generation of counts###
alldata_comp=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,'N',"N/A","N/A")[[2]]

###Mutation specific
####Decided to not look at mutation specific counts too closely because co-occurrence of brafv600e and nrasq61L is a VERY high bar
# alldata_comp=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,'Y',"V600E","Q61L")[[2]]
head(alldata_comp)

###Calculating Odds ratios and GOI frequencies for the raw data###
cohort_size=length(alldata_comp$Positive_Ctrl1)
pc1pc2_contab_counts=contab_maker(alldata_comp$Positive_Ctrl1,alldata_comp$Positive_Ctrl2,alldata_comp)[2:1, 2:1]
pc1pc2_contab_counts=rbind(c(15,25),c(81,230)) ###NRAS ATI
goipc1_contab_counts=contab_maker(alldata_comp$genex,alldata_comp$Positive_Ctrl1,alldata_comp)[2:1, 2:1]
# goipc1_contab_counts=rbind(c(1,110),c(186,365)) ###EGFR KRAS
fisher.test(goipc1_contab_counts)
###Had to add the 2:1 bits because the contab maker spits out NN YY whereas we wanted YNYN
goipc2_contab_counts=contab_maker(alldata_comp$genex,alldata_comp$Positive_Ctrl2,alldata_comp)[2:1, 2:1]

# pc1pc2_contab_counts=gene_pair_2_table
# goipc1_contab_counts=contab_maker(alldata_comp$genex,alldata_comp$Positive_Ctrl1,alldata_comp)[2:1, 2:1]
# goipc1_contab_counts=gene_pair_1_table

cohort_size_curr=cohort_size
# goipc2_contab_counts=contab_maker(alldata_comp$genex,alldata_comp$Positive_Ctrl2,alldata_comp)[2:1, 2:1]
pc1pc2_contab_probabilities=pc1pc2_contab_counts/cohort_size_curr
goipc1_contab_probabilities=goipc1_contab_counts/cohort_size_curr
goipc2_contab_probabilities=goipc2_contab_counts/cohort_size_curr
# pc1pc2_contab_probabilities=pc1pc2_contab_counts
# goipc1_contab_probabilities=goipc1_contab_counts
# goipc2_contab_probabilities=goipc2_contab_counts/cohort_size

or_pc1pc2=pc1pc2_contab_probabilities[1,1]*pc1pc2_contab_probabilities[2,2]/(pc1pc2_contab_probabilities[1,2]*pc1pc2_contab_probabilities[2,1])
or_goipc1=goipc1_contab_probabilities[1,1]*goipc1_contab_probabilities[2,2]/(goipc1_contab_probabilities[1,2]*goipc1_contab_probabilities[2,1])
or_goipc2=goipc2_contab_probabilities[1,1]*goipc2_contab_probabilities[2,2]/(goipc2_contab_probabilities[1,2]*goipc2_contab_probabilities[2,1])

goi_freq=goipc1_contab_probabilities[1,1]+goipc1_contab_probabilities[1,2]
# goi_freq=.25
# class(goi_freq)

###

###Downsampling PC1 to the probability of GOI without changing ORs###
###The function below converts contingency table data to a new contingency table in which the data is downsampled to the desired frequency, aka the frequency of the GOI in this case###
pc1new_pc2_contab=contab_downsampler(pc1pc2_contab_probabilities,goi_freq)
goinew_pc1_contab=contab_downsampler(goipc1_contab_probabilities,goi_freq)
goinew_pc2_contab=contab_downsampler(goipc2_contab_probabilities,goi_freq)
##original contab:
head(pc1pc2_contab_probabilities)
###downsampled contab:
head(pc1new_pc2_contab)

pc1rawpc2_contabs_sims=contab_simulator(pc1pc2_contab_probabilities,1000,cohort_size_curr)
pc1pc2_contabs_sims=contab_simulator(pc1new_pc2_contab,1000,cohort_size_curr)
goipc1_contabs_sims=contab_simulator(goinew_pc1_contab,1000,cohort_size_curr)
goipc2_contabs_sims=contab_simulator(goinew_pc2_contab,1000,cohort_size_curr)
# goipc2_contabs_sims=contab_simulator(goinew_pc2_contab,1000,cohort_size)
# head(pc1pc2_contabs_sims) #each row in this dataset is a new contab
pc1rawpc2_contabs_sims=data.frame(pc1rawpc2_contabs_sims)
  pc1rawpc2_contabs_sims=pc1rawpc2_contabs_sims%>%
  mutate(or=p11*p00/(p10*p01))

pc1pc2_contabs_sims=data.frame(pc1pc2_contabs_sims)
  pc1pc2_contabs_sims=pc1pc2_contabs_sims%>%
  mutate(or=p11*p00/(p10*p01))
  
goipc1_contabs_sims=data.frame(goipc1_contabs_sims)
  goipc1_contabs_sims=goipc1_contabs_sims%>%
  mutate(or=p11*p00/(p10*p01))
  
goipc2_contabs_sims=data.frame(goipc2_contabs_sims)
goipc2_contabs_sims=goipc2_contabs_sims%>%
mutate(or=p11*p00/(p10*p01))
  
# goipc2_contabs_sims=data.frame(goipc2_contabs_sims)
#   goipc2_contabs_sims=goipc2_contabs_sims%>%
#   mutate(or=p11*p00/(p10*p01))
pc1rawpc2_contabs_sims$comparison="pc1rawpc2"
pc1pc2_contabs_sims$comparison="pc1pc2"
goipc1_contabs_sims$comparison="goipc1"
goipc2_contabs_sims$comparison="goipc2"
or_median_raw=quantile(pc1rawpc2_contabs_sims$or,na.rm = T)[3]
or_uq_raw=quantile(pc1rawpc2_contabs_sims$or,na.rm = T)[4]
or_median_downsampled=quantile(pc1pc2_contabs_sims$or,na.rm = T)[3]
or_uq_downsampled=quantile(pc1pc2_contabs_sims$or,na.rm = T)[4]

pc1rawpc2_contabs_sims=pc1rawpc2_contabs_sims%>%
  mutate(isgreater_raw_median=case_when(or>=or_median_raw~1,
                             TRUE~0),
         isgreater_raw_uq=case_when(or>or_uq_raw~1,
                             TRUE~0),
         isgreater_median=case_when(or>or_median_downsampled~1,
                             TRUE~0),
         isgreater_uq=case_when(or>or_uq_downsampled~1,
                             TRUE~0)
         )
pc1pc2_contabs_sims=pc1pc2_contabs_sims%>%
  mutate(isgreater_raw_median=case_when(or>or_median_raw~1,
                             TRUE~0),
         isgreater_raw_uq=case_when(or>or_uq_raw~1,
                             TRUE~0),
         isgreater_median=case_when(or>or_median_downsampled~1,
                             TRUE~0),
         isgreater_uq=case_when(or>or_uq_downsampled~1,
                             TRUE~0)
         )
goipc1_contabs_sims=goipc1_contabs_sims%>%
  mutate(isgreater_raw_median=case_when(or>or_median_raw~1,
                             TRUE~0),
         isgreater_raw_uq=case_when(or>or_uq_raw~1,
                             TRUE~0),
         isgreater_median=case_when(or>or_median_downsampled~1,
                             TRUE~0),
         isgreater_uq=case_when(or>or_uq_downsampled~1,
                             TRUE~0)
         )

goipc2_contabs_sims=goipc2_contabs_sims%>%
  mutate(isgreater_raw_median=case_when(or>or_median_raw~1,
                             TRUE~0),
         isgreater_raw_uq=case_when(or>or_uq_raw~1,
                             TRUE~0),
         isgreater_median=case_when(or>or_median_downsampled~1,
                             TRUE~0),
         isgreater_uq=case_when(or>or_uq_downsampled~1,
                             TRUE~0)
         )
# pc1pc2_contabs_sims=pc1pc2_contabs_sims%>%
#   mutate(isgreater=case_when(or>=or_pc1pc2~1,
#                              TRUE~0))
# goipc1_contabs_sims=goipc1_contabs_sims%>%
#   mutate(isgreater=case_when(or>=or_pc1pc2~1,
#                              TRUE~0))
# goipc2_contabs_sims=goipc2_contabs_sims%>%
#   mutate(isgreater=case_when(or>=or_pc1pc2~1,
#                              TRUE~0))
pc1rawpc2_isgreater_raw_median=sum(pc1rawpc2_contabs_sims$isgreater_raw_median)
pc1rawpc2_isgreater_raw_uq=sum(pc1rawpc2_contabs_sims$isgreater_raw_uq)
pc1rawpc2_isgreater_median=sum(pc1rawpc2_contabs_sims$isgreater_median)
pc1rawpc2_isgreater_uq=sum(pc1rawpc2_contabs_sims$isgreater_uq)

pc1pc2_isgreater_raw_median=sum(pc1pc2_contabs_sims$isgreater_raw_median)
pc1pc2_isgreater_raw_uq=sum(pc1pc2_contabs_sims$isgreater_raw_uq)
pc1pc2_isgreater_median=sum(pc1pc2_contabs_sims$isgreater_median)
pc1pc2_isgreater_uq=sum(pc1pc2_contabs_sims$isgreater_uq)

goipc1_isgreater_raw_median=sum(goipc1_contabs_sims$isgreater_raw_median)
goipc1_isgreater_raw_uq=sum(goipc1_contabs_sims$isgreater_raw_uq)
goipc1_isgreater_median=sum(goipc1_contabs_sims$isgreater_median)
goipc1_isgreater_uq=sum(goipc1_contabs_sims$isgreater_uq)


plotting_df=rbind(pc1pc2_contabs_sims,goipc1_contabs_sims,goipc2_contabs_sims)
# plotting_df=rbind(pc1pc2_contabs_sims,goipc1_contabs_sims)
# 

# a=plotting_df%>%filter(comparison%in%"pc1pc2")
ggplot(plotting_df,aes(x=(or),fill=comparison))+
  geom_histogram(bins=40,alpha=0.55,position="identity")+
  # geom_histogram(bins=50,alpha=0.55)+
  scale_y_continuous(expand=c(0,0),name="Count")+
  scale_x_continuous(expand=c(0,0),trans="log10",name="Odds Ratio")+
  scale_fill_brewer(palette="Set2")+
  # geom_vline(xintercept = or_pc1pc2)+
  cleanup

ggplot(plotting_df,aes(y=(or),x=comparison),fill=factor(comparison))+
  geom_boxplot()+
  scale_y_continuous(name="Odds Ratio",trans="log10")+
  scale_x_discrete(name="")+
  scale_fill_brewer(palette="Set2")+
  geom_hline(yintercept = or_uq_downsampled,linetype="dashed")+
  cleanup+
  theme(legend.position = "none",
        axis.ticks.x = element_blank())

ggsave("paircon_boxplot_alkati.pdf",width = 3,height=2,units="in",useDingbats=F)

ggplot(plotting_df%>%filter(comparison%in%"pc1pc2"),aes(x=or,fill=comparison))+
  geom_histogram(bins=50,alpha=0.55,position="identity")+
  geom_density(alpha=.6)+
  scale_y_continuous(expand=c(0,0),name="Count")+
  scale_x_continuous(expand=c(0,0),name="Odds Ratio")+
  # geom_vline(xintercept = or_pc1pc2)+
  cleanup




```



```{r}

# rm(list=ls())
or_pair1=c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1)
or_pair2=seq(.01,.2,by=.02)
incidence=seq(4,36,by=2)
cohort_size=seq(100,1000,by=200)

# or_pair1=c(.01,.05)
# or_pair2=seq(.01,.1,by=.05)
# incidence=seq(4,36,by=20)
# cohort_size=seq(100,1000,by=400)

    # or_pair1=.05
    # or_pair2=.01
    # incidence=12
    # cohort_size=500
    # i=1
    # j=1
    # k=1
    # l=1

true_or_vals=or_pair1
cohort_size_vals=cohort_size
gene1_total_vals=incidence

simresults_compiled_alldata=as.list(length(or_pair2)*length(or_pair1)*length(incidence)*length(cohort_size))
simresults_compiled=matrix(,length(or_pair2)*length(or_pair1)*length(incidence)*length(cohort_size),ncol=16)
ct=1
for(j in 1:length(incidence)){
  tic()
for(l in 1:length(or_pair2)){
  for(k in 1:length(cohort_size_vals)){
    for(i in 1:length(true_or_vals)){
gene_pair_1=unlist(mut_excl_genes_generator(cohort_size[k],incidence[j],or_pair1[i],or_pair2[l])[1])
gene_pair_1_table=rbind(c(gene_pair_1[1],gene_pair_1[2]),c(gene_pair_1[3],gene_pair_1[4])) ###make sure these are the right indices
gene_pair_2=unlist(mut_excl_genes_generator(cohort_size[k],incidence[j],or_pair1[i],or_pair2[l])[2])
gene_pair_2_table=rbind(c(gene_pair_2[1],gene_pair_2[2]),c(gene_pair_2[3],gene_pair_2[4]))
# alldata_1=mut_excl_genes_datapoints(gene_pair_1)
# 
# alldata_2=mut_excl_genes_datapoints(gene_pair_2)
# alldata_comp_1=alldata_compiler(alldata_1,"gene2","gene3","gene1",'N',"N/A","N/A")[[2]]
# 
# genex_replication_prop_1=alldata_compiler(alldata_1,"gene2","gene3","gene1",'N',"N/A","N/A")[[1]]
# alldata_comp_2=alldata_compiler(alldata_2,"gene2","gene3","gene1",'N',"N/A","N/A")[[2]]
# genex_replication_prop_2=alldata_compiler(alldata_2,"gene2","gene3","gene1",'N',"N/A","N/A")[[1]]


###Calculating Odds ratios and GOI frequencies for the raw data###
# cohort_size_curr=length(alldata_comp$Positive_Ctrl1)
cohort_size_curr=cohort_size[k]
# pc1pc2_contab_counts=contab_maker(alldata_comp$Positive_Ctrl1,alldata_comp$Positive_Ctrl2,alldata_comp)[2:1, 2:1]
pc1pc2_contab_counts=gene_pair_2_table
# goipc1_contab_counts=contab_maker(alldata_comp$genex,alldata_comp$Positive_Ctrl1,alldata_comp)[2:1, 2:1]
goipc1_contab_counts=gene_pair_1_table


# goipc2_contab_counts=contab_maker(alldata_comp$genex,alldata_comp$Positive_Ctrl2,alldata_comp)[2:1, 2:1]
# pc1pc2_contab_probabilities=pc1pc2_contab_counts/cohort_size_curr
# goipc1_contab_probabilities=goipc1_contab_counts/cohort_size_curr
pc1pc2_contab_probabilities=pc1pc2_contab_counts
goipc1_contab_probabilities=goipc1_contab_counts
# goipc2_contab_probabilities=goipc2_contab_counts/cohort_size

or_pc1pc2=pc1pc2_contab_probabilities[1,1]*pc1pc2_contab_probabilities[2,2]/(pc1pc2_contab_probabilities[1,2]*pc1pc2_contab_probabilities[2,1])
or_goipc1=goipc1_contab_probabilities[1,1]*goipc1_contab_probabilities[2,2]/(goipc1_contab_probabilities[1,2]*goipc1_contab_probabilities[2,1])
# or_goipc2=goipc2_contab_probabilities[1,1]*goipc2_contab_probabilities[2,2]/(goipc2_contab_probabilities[1,2]*goipc2_contab_probabilities[2,1])

goi_freq=goipc1_contab_probabilities[1,1]+goipc1_contab_probabilities[1,2]
# goi_freq=.01
# class(goi_freq)

###

###Downsampling PC1 to the probability of GOI without changing ORs###
###The function below converts contingency table data to a new contingency table in which the data is downsampled to the desired frequency, aka the frequency of the GOI in this case###
pc1new_pc2_contab=contab_downsampler(pc1pc2_contab_probabilities,goi_freq)
goinew_pc1_contab=contab_downsampler(goipc1_contab_probabilities,goi_freq)
# goinew_pc2_contab=contab_downsampler(goipc2_contab_probabilities,goi_freq)
###original contab:
# head(pc1pc2_contab_probabilities) 
###downsampled contab:
# head(pc1new_pc2_contab) 

pc1rawpc2_contabs_sims=contab_simulator(pc1pc2_contab_probabilities,1000,cohort_size_curr)
pc1pc2_contabs_sims=contab_simulator(pc1new_pc2_contab,1000,cohort_size_curr)
goipc1_contabs_sims=contab_simulator(goinew_pc1_contab,1000,cohort_size_curr)
# goipc2_contabs_sims=contab_simulator(goinew_pc2_contab,1000,cohort_size)
# head(pc1pc2_contabs_sims) #each row in this dataset is a new contab
pc1rawpc2_contabs_sims=data.frame(pc1rawpc2_contabs_sims)
  pc1rawpc2_contabs_sims=pc1rawpc2_contabs_sims%>%
  mutate(or=p11*p00/(p10*p01))

pc1pc2_contabs_sims=data.frame(pc1pc2_contabs_sims)
  pc1pc2_contabs_sims=pc1pc2_contabs_sims%>%
  mutate(or=p11*p00/(p10*p01))
  
goipc1_contabs_sims=data.frame(goipc1_contabs_sims)
  goipc1_contabs_sims=goipc1_contabs_sims%>%
  mutate(or=p11*p00/(p10*p01))
  

  
# goipc2_contabs_sims=data.frame(goipc2_contabs_sims)
#   goipc2_contabs_sims=goipc2_contabs_sims%>%
#   mutate(or=p11*p00/(p10*p01))
pc1rawpc2_contabs_sims$comparison="pc1rawpc2"
pc1pc2_contabs_sims$comparison="pc1pc2"
goipc1_contabs_sims$comparison="goipc1"
# goipc2_contabs_sims$comparison="goipc2"
or_median_raw=quantile(pc1rawpc2_contabs_sims$or,na.rm = T)[3]
or_uq_raw=quantile(pc1rawpc2_contabs_sims$or,na.rm = T)[4]
or_median_downsampled=quantile(pc1pc2_contabs_sims$or,na.rm = T)[3]
or_uq_downsampled=quantile(pc1pc2_contabs_sims$or,na.rm = T)[4]

# pc1rawpc2_contabs_sims=pc1rawpc2_contabs_sims%>%
#   mutate(isgreater_raw_median=case_when(or>=or_median_raw~1,
#                              TRUE~0),
#          isgreater_raw_uq=case_when(or>=or_uq_raw~1,
#                              TRUE~0),
#          isgreater_median=case_when(or>=or_median_downsampled~1,
#                              TRUE~0),
#          isgreater_uq=case_when(or>=or_uq_downsampled~1,
#                              TRUE~0)
#          )
# pc1pc2_contabs_sims=pc1pc2_contabs_sims%>%
#   mutate(isgreater_raw_median=case_when(or>=or_median_raw~1,
#                              TRUE~0),
#          isgreater_raw_uq=case_when(or>=or_uq_raw~1,
#                              TRUE~0),
#          isgreater_median=case_when(or>=or_median_downsampled~1,
#                              TRUE~0),
#          isgreater_uq=case_when(or>=or_uq_downsampled~1,
#                              TRUE~0)
#          )
# goipc1_contabs_sims=goipc1_contabs_sims%>%
#   mutate(isgreater_raw_median=case_when(or>=or_median_raw~1,
#                              TRUE~0),
#          isgreater_raw_uq=case_when(or>=or_uq_raw~1,
#                              TRUE~0),
#          isgreater_median=case_when(or>=or_median_downsampled~1,
#                              TRUE~0),
#          isgreater_uq=case_when(or>=or_uq_downsampled~1,
#                              TRUE~0)
#          )


pc1rawpc2_contabs_sims=pc1rawpc2_contabs_sims%>%
  mutate(isgreater_raw_median=case_when(or>=or_median_raw~1,
                             TRUE~0),
         isgreater_raw_uq=case_when(or>or_uq_raw~1,
                             TRUE~0),
         isgreater_median=case_when(or>or_median_downsampled~1,
                             TRUE~0),
         isgreater_uq=case_when(or>or_uq_downsampled~1,
                             TRUE~0)
         )
pc1pc2_contabs_sims=pc1pc2_contabs_sims%>%
  mutate(isgreater_raw_median=case_when(or>=or_median_raw~1,
                             TRUE~0),
         isgreater_raw_uq=case_when(or>or_uq_raw~1,
                             TRUE~0),
         isgreater_median=case_when(or>or_median_downsampled~1,
                             TRUE~0),
         isgreater_uq=case_when(or>or_uq_downsampled~1,
                             TRUE~0)
         )
goipc1_contabs_sims=goipc1_contabs_sims%>%
  mutate(isgreater_raw_median=case_when(or>or_median_raw~1,
                             TRUE~0),
         isgreater_raw_uq=case_when(or>or_uq_raw~1,
                             TRUE~0),
         isgreater_median=case_when(or>or_median_downsampled~1,
                             TRUE~0),
         isgreater_uq=case_when(or>or_uq_downsampled~1,
                             TRUE~0)
         )
# pc1pc2_contabs_sims=pc1pc2_contabs_sims%>%
#   mutate(isgreater=case_when(or>=or_pc1pc2~1,
#                              TRUE~0))
# goipc1_contabs_sims=goipc1_contabs_sims%>%
#   mutate(isgreater=case_when(or>=or_pc1pc2~1,
#                              TRUE~0))
# goipc2_contabs_sims=goipc2_contabs_sims%>%
#   mutate(isgreater=case_when(or>=or_pc1pc2~1,
#                              TRUE~0))
pc1rawpc2_isgreater_raw_median=sum(pc1rawpc2_contabs_sims$isgreater_raw_median)
pc1rawpc2_isgreater_raw_uq=sum(pc1rawpc2_contabs_sims$isgreater_raw_uq)
pc1rawpc2_isgreater_median=sum(pc1rawpc2_contabs_sims$isgreater_median)
pc1rawpc2_isgreater_uq=sum(pc1rawpc2_contabs_sims$isgreater_uq)

pc1pc2_isgreater_raw_median=sum(pc1pc2_contabs_sims$isgreater_raw_median)
pc1pc2_isgreater_raw_uq=sum(pc1pc2_contabs_sims$isgreater_raw_uq)
pc1pc2_isgreater_median=sum(pc1pc2_contabs_sims$isgreater_median)
pc1pc2_isgreater_uq=sum(pc1pc2_contabs_sims$isgreater_uq)

goipc1_isgreater_raw_median=sum(goipc1_contabs_sims$isgreater_raw_median)
goipc1_isgreater_raw_uq=sum(goipc1_contabs_sims$isgreater_raw_uq)
goipc1_isgreater_median=sum(goipc1_contabs_sims$isgreater_median)
goipc1_isgreater_uq=sum(goipc1_contabs_sims$isgreater_uq)


# pc1rawpc2_isgreater=sum(pc1rawpc2_contabs_sims$isgreater)
# pc1pc2_isgreater=sum(pc1pc2_contabs_sims$isgreater)
# goipc1_isgreater=sum(goipc1_contabs_sims$isgreater)

simresults=c(cohort_size[k],
             incidence[j],
             or_pair1[i],
             or_pair2[l],
             pc1rawpc2_isgreater_raw_median,
             pc1rawpc2_isgreater_raw_uq,
             pc1rawpc2_isgreater_median,
             pc1rawpc2_isgreater_uq,
             pc1pc2_isgreater_raw_median,
             pc1pc2_isgreater_raw_uq,
             pc1pc2_isgreater_median,
             pc1pc2_isgreater_uq,
             goipc1_isgreater_raw_median,
             goipc1_isgreater_raw_uq,
             goipc1_isgreater_median,
             goipc1_isgreater_uq)

simresults_alldata=c(cohort_size[k],
             incidence[j],
             or_pair1[i],
             or_pair2[l],
    list(goipc1_contabs_sims$or),
    list(pc1pc2_contabs_sims$or))

######Comment out this section to not gather all the 1,000 ORs for each simulation####
simresults_compiled_alldata[[ct]]=simresults_alldata
# a=simresults_compiled_alldata[[1]]

simresults_compiled[ct,]=simresults

ct=ct+1
    }
  }
}
  toc()
}

```

```{r}
simresults_compiled=data.frame(simresults_compiled)
colnames(simresults_compiled)=c("cohort_size",
                                "incidence",
                                "or1",
                                "or2",
                                "pc1rawpc2_isgreater_raw_median",
                                "pc1rawpc2_isgreater_raw_uq",
                                "pc1rawpc2_isgreater_median",
                                "pc1rawpc2_isgreater_uq",
                                "pc1pc2_isgreater_raw_median",
                                "pc1pc2_isgreater_raw_uq",
                                "pc1pc2_isgreater_median",
                                "pc1pc2_isgreater_uq",
                                "goipc1_isgreater_raw_median",
                                "goipc1_isgreater_raw_uq",
                                "goipc1_isgreater_median",
                                "goipc1_isgreater_uq")
simresults_compiled$delta_median=simresults_compiled$goipc1_isgreater_raw_median-simresults_compiled$goipc1_isgreater_median
simresults_compiled$delta_uq=simresults_compiled$goipc1_isgreater_raw_uq-simresults_compiled$goipc1_isgreater_uq

simresults_compiled=simresults_compiled%>%
  mutate(fp_corrected_95=case_when(
    goipc1_isgreater_raw_uq<950~-1,
     (goipc1_isgreater_raw_uq>=950&(goipc1_isgreater_raw_uq-delta_uq)<=950)~1,
    TRUE~0),
    fp_corrected_99=case_when(
      goipc1_isgreater_raw_uq<990~-1,
    (goipc1_isgreater_raw_uq>=990&(goipc1_isgreater_raw_uq-delta_uq)<=990)~1,
    TRUE~0))

# simresults_compiled$goipc1_isgreater_percent=simresults_compiled$goipc1_isgreater*100/1000
simresults_subset=simresults_compiled%>%filter(cohort_size%in%500)

ggplot(simresults_compiled%>%filter(or1<=.4),aes(x=incidence,y=or2))+geom_tile(aes(fill=goipc1_isgreater_median/10))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")

ggplot(simresults_compiled%>%filter(or1<=.4),aes(x=incidence,y=or2))+geom_tile(aes(fill=goipc1_isgreater_raw_median/10))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Percent succesful trials")




ggplot(simresults_compiled%>%filter(or1<=.4),aes(x=incidence,y=or2))+geom_tile(aes(fill=goipc1_isgreater_uq/10))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Percent succesful trials")

ggplot(simresults_compiled%>%filter(or1<=.4),aes(x=incidence,y=or2))+geom_tile(aes(fill=goipc1_isgreater_raw_uq/10))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Percent succesful trials")


ggplot(simresults_compiled%>%filter(or1<=.7,or1>=.2),aes(x=incidence,y=or2))+geom_tile(aes(fill=delta_median))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",high ="blue",name="Delta bw raw and downsampled")

ggplot(simresults_compiled%>%filter(or1<=.7,or1>=.2),aes(x=incidence,y=or2))+geom_tile(aes(fill=delta_uq))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",high ="blue",name="Delta bw raw and downsampled")

ggplot(simresults_compiled%>%filter(or1<=.7,or1>=.2),aes(x=incidence,y=or2))+geom_tile(color="black",aes(fill=fp_corrected_95))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",high ="blue",name="Regions where downsampling helps")

ggplot(simresults_compiled%>%filter(or1<=.7,or1>=.2),aes(x=incidence,y=or2))+geom_tile(color="black",aes(fill=fp_corrected_99))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",high ="blue",name="Regions where downsampling helps")


ggplot(simresults_compiled%>%filter(or1<=.4),aes(x=incidence,y=or2))+geom_tile(aes(fill=goipc1_isgreater_median/10))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")

ggplot(simresults_compiled%>%filter(or1<=.4,cohort_size%in%500),aes(x=incidence,y=or2))+geom_tile(aes(fill=goipc1_isgreater_median/10))+facet_wrap(~or1)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")

ggplot(simresults_compiled%>%filter(or1<=.4,cohort_size%in%500),aes(x=incidence,y=or2))+geom_tile(aes(fill=goipc1_isgreater_median/10))+facet_wrap(~or1,ncol=6)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")

ggplot(simresults_compiled%>%filter(or1%in%c(.05,0.1,.5),cohort_size%in%500),aes(x=incidence,y=or2))+geom_tile(color="black",aes(fill=goipc1_isgreater_median/10))+facet_wrap(~or1,ncol=6)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_continuous(expand = c(0,0),name="Incidence")+
  scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1vsPC2")+ggtitle("Odds ratio of GOIvsPC1")+
  theme(plot.title = element_text(hjust = 0.5))

# ggsave("score_heatmap.pdf",width=8,heigh=3,units="in",useDingbats=F)

ggplot(simresults_compiled%>%filter(or1%in%c(.05,0.1,.5),cohort_size%in%c(100,500,900)),aes(x=incidence,y=or2))+geom_tile(color="black",aes(fill=goipc1_isgreater_median/10))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_continuous(expand = c(0,0),name="Incidence")+
  scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1vsPC2")+ggtitle("Odds ratio of GOIvsPC1")+
  theme(plot.title = element_text(hjust = 0.5))

# ggsave("score_heatmap_supplement.pdf",width=8,heigh=7,units="in",useDingbats=F)


ggplot(simresults_compiled%>%filter(or2%in%.05,cohort_size%in%c(500)),aes(x=factor(or1),y=goipc1_isgreater_median/10))+geom_boxplot()+scale_x_discrete(name="Odds ratio of PC1vsPC2")+
  scale_y_continuous(name="Score")+
  theme(plot.title = element_text(hjust = 0.5))+cleanup

# ggsave("score_plot.pdf",width=8,heigh=3,units="in",useDingbats=F)



ggplot(simresults_compiled%>%filter(incidence%in%c(6,16,26,36),cohort_size%in%c(100,300,500,900)),aes(x=factor(or1),y=or2))+geom_tile(color="black",aes(fill=goipc1_isgreater_uq/10))+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_discrete(expand = c(0,0),name="Odds ratio of GOI and PC1")+facet_grid(cohort_size~incidence)+
  scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1 and PC2")+theme(plot.title = element_text(hjust = 0.5))

ggplot(simresults_compiled%>%filter(incidence%in%c(6,16,26,36),cohort_size%in%500),aes(x=factor(or1),y=or2))+geom_tile(color="black",aes(fill=goipc1_isgreater_uq/10))+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_discrete(expand = c(0,0),name="Odds ratio of GOI and PC1")+facet_wrap(~incidence,ncol=4)+
  scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1 and PC2")+theme(plot.title = element_text(hjust = 0.5),axis.text.x=element_text(angle=90,hjust=.5,vjust=.5),axis.text=element_text(face="bold",size="9",color="black"))
# ggsave("score_heatmap_bestoption1.pdf",width=8,heigh=2.5,units="in",useDingbats=F)
# sort(unique(simresults_compiled$or2))

ggplot(simresults_compiled%>%filter(cohort_size%in%500),aes(x=factor(or1),y=or2))+geom_tile(color="black",aes(fill=goipc1_isgreater_median/10))+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_discrete(expand = c(0,0),name="Odds ratio of GOI and PC1")+
  scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1 and PC2")+theme(plot.title = element_text(hjust = 0.5))
# ggsave("score_heatmap_bestoption.pdf",width=4,heigh=3,units="in",useDingbats=F)

ggplot(simresults_compiled%>%filter(or1%in%.3,or2%in%.09),aes(x=incidence/cohort_size,y=goipc1_isgreater_median/10))+geom_point()
ggplot(simresults_compiled%>%filter(or1%in%.1,or2%in%.05,cohort_size%in%500),aes(x=incidence,y=goipc1_isgreater_median/10))+geom_point()
  # scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_discrete(expand = c(0,0),name="Odds ratio of GOI and PC1")+
  # scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1 and PC2")+theme(plot.title = element_text(hjust = 0.5))
# or1%in%c(.05,0.1,.5),cohort_size%in%c(100,500,900)
ggplot(simresults_compiled%>%filter(cohort_size%in%c(100,300,500)),aes(x=incidence,y=or2))+geom_tile(color="black",aes(fill=goipc1_isgreater_median/10))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_continuous(expand = c(0,0),name="Incidence")+
  scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1vsPC2")+ggtitle("Odds ratio of GOIvsPC1")+
  theme(plot.title = element_text(hjust = 0.5))
```
Choosing plots for putting in paper
```{r}
ggplot(simresults_compiled%>%filter(cohort_size%in%500),aes(x=factor(or1),y=or2))+geom_tile(color="black",aes(fill=goipc1_isgreater_uq/10))+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_discrete(expand = c(0,0),name="Odds ratio of GOI and PC1")+
  scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1 and PC2")+theme(plot.title = element_text(hjust = 0.5))+theme(plot.title = element_text(hjust = 0.5),axis.text.x=element_text(angle=90,hjust=.5,vjust=.5),axis.text=element_text(face="bold",size="9",color="black"))
# ggsave("score_heatmap_bestoption.pdf",width=4,heigh=3,units="in",useDingbats=F)

ggplot(simresults_compiled%>%filter(or1%in%c(.05,.3,.6),incidence%in%c(4,8,12,16,20,24,28,32,36),cohort_size%in%500),aes(x=factor(incidence),y=or2))+geom_tile(color="black",aes(fill=goipc1_isgreater_median/10))+facet_wrap(~or1,ncol=6)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_discrete(expand = c(0,0),name="Incidence")+
  scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1 and PC2")+theme(strip.text=element_blank(),plot.title = element_text(hjust = 0.5),axis.text.x=element_text(angle=90,hjust=.5,vjust=.5),axis.text=element_text(face="bold",size="9",color="black"),legend.position = "none")

# ggsave("score_heatmap_bestoption1.pdf",width=6,heigh=2.5,units="in",useDingbats=F)

ggplot(simresults_compiled%>%filter(or1%in%c(.1,.2,.5),incidence%in%c(4,8,12,16,20,24,28,32,36),cohort_size%in%500),aes(x=factor(incidence),y=or2))+geom_tile(color="black",aes(fill=goipc1_isgreater_raw_median/10))+facet_wrap(~or1,ncol=6)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_discrete(expand = c(0,0),name="Incidence")+
  scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1 and PC2")+theme(strip.text=element_blank(),plot.title = element_text(hjust = 0.5),axis.text.x=element_text(angle=90,hjust=.5,vjust=.5),axis.text=element_text(face="bold",size="9",color="black"),legend.position = "none")
ggsave("score_heatmap_bestoption1.pdf",width=6,heigh=2.5,units="in",useDingbats=F)

ggplot(simresults_compiled%>%filter(cohort_size%in%500,incidence%in%c(4,8,12,16,20,24,28,32,36)),aes(x=factor(incidence),y=or2))+geom_tile(color="black",aes(fill=goipc1_isgreater_raw_median/10))+facet_wrap(~or1,ncol=4)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_discrete(expand = c(0,0),name="Incidence")+
  scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1 and PC2")+theme(plot.title = element_text(hjust = 0.5),axis.text.x=element_text(angle=90,hjust=.5,vjust=.5),axis.text=element_text(face="bold",size="9",color="black"),legend.position = "none")
ggsave("score_heatmap_bestoption1_supplement.pdf",width=6,heigh=6,units="in",useDingbats=F)

ggplot(simresults_compiled%>%filter(or1%in%c(.05,.1,.5),cohort_size%in%c(100,300,500)),aes(x=incidence,y=or2))+geom_tile(color="black",aes(fill=goipc1_isgreater_median/10))+facet_grid(cohort_size~or1)+scale_fill_gradient2(low ="red" ,mid ="white",midpoint=50,high ="blue",name="Score")+scale_x_discrete(expand = c(0,0),name="Incidence")+
  scale_y_continuous(expand = c(0,0),name="Odds ratio of PC1 and PC2")


# a=simresults_compiled%>%filter(cohort_size%in%500,or1%in%.7,or2%in%.05,incidence%in%)
```



```{r}
# simresults_subset=simresults_compiled%>%filter(or2%in%0.01,or1%in%c(0.01,.1,1))
simresults_subset=unlist(simresults_compiled_alldata)

simresults_unlisted=data.frame(unlist(lapply(simresults_compiled_alldata,'[[',1)),
             unlist(lapply(simresults_compiled_alldata,'[[',2)),
             unlist(lapply(simresults_compiled_alldata,'[[',3)),
             unlist(lapply(simresults_compiled_alldata,'[[',4)))
simresults_unlisted$list1=(lapply(simresults_compiled_alldata,'[[',5))
simresults_unlisted$list2=(lapply(simresults_compiled_alldata,'[[',6))

colnames(simresults_unlisted)=c("cohort_size","incidence","or1","or2","or1_list","or2_list")

simresults_unlisted=simresults_unlisted%>%filter(or2%in%0.01,or1%in%c(0.05,.1,1),cohort_size%in%500,incidence%in%c(4,8,12,16,20))
# library(reshape2)
a=simresults_unlisted%>%filter(incidence%in%8,or2%in%0.01,or1%in%.05)
b=unnest(a)
median(b$or2_list)>median(b$or1_list)

library(tidyr)
simresults_melted=unnest(simresults_unlisted)
simresults_melted2=melt(simresults_melted,
                        id.vars = c("cohort_size","or1","or2","incidence"),
                        measure.vars =c("or1_list","or2_list"),
                        variable.name = "Comparison",
                        value.name = "OR"
                        )
ggplot(simresults_melted2,aes(x=factor(incidence),y=OR,fill=Comparison))+facet_wrap(~or1)+geom_boxplot()+scale_y_continuous(trans="log2")+cleanup

plotly=ggplot(simresults_melted2,aes(x=factor(incidence),y=OR,fill=Comparison))+facet_wrap(~or1)+geom_boxplot()+cleanup
ggplotly(plotly)










simresults_unlisted=data.frame(unlist(lapply(simresults_compiled_alldata,'[[',1)),
             unlist(lapply(simresults_compiled_alldata,'[[',2)),
             unlist(lapply(simresults_compiled_alldata,'[[',3)),
             unlist(lapply(simresults_compiled_alldata,'[[',4)))
simresults_unlisted$list1=(lapply(simresults_compiled_alldata,'[[',5))
simresults_unlisted$list2=(lapply(simresults_compiled_alldata,'[[',6))

colnames(simresults_unlisted)=c("cohort_size","incidence","or1","or2","or1_list","or2_list")
simresults_unlisted=simresults_unlisted%>%filter(or1%in%.5,or2%in%c(0.05,.11,.19),cohort_size%in%500,incidence%in%c(4,8,12,16,20))

simresults_melted=unnest(simresults_unlisted)
simresults_melted2=melt(simresults_melted,
                        id.vars = c("cohort_size","or1","or2","incidence"),
                        measure.vars =c("or1_list","or2_list"),
                        variable.name = "Comparison",
                        value.name = "OR"
                        )

ggplot(simresults_melted2,aes(x=factor(incidence),y=OR,fill=Comparison))+facet_wrap(~or2)+geom_boxplot()+scale_y_continuous(trans="log2")+cleanup

plotly=ggplot(simresults_melted2,aes(x=factor(incidence),y=OR,fill=Comparison))+facet_wrap(~or2)+geom_boxplot()+cleanup
ggplotly(plotly)
# sort(unique(simresults_unlisted$or2))

```