Last updated: 2019-02-17

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library(knitr)
library(tictoc)
library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(VennDiagram)
Loading required package: grid
Loading required package: futile.logger
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(foreach)
library(doParallel)
Loading required package: iterators
Loading required package: parallel
library(ggplot2)
library(reshape2)
library(RColorBrewer)
library(devtools)
library(ggsignif)
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"))

P-value distribution plots for Figure 1C:

nsubsamples=12 # maybe this can be removed and instead calculated later.
  nsims<-100 #
  #Positive control 1
  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("../data/all_data.csv",sep=",",header=T,stringsAsFactors=F)
  alldata=read.csv("data/All_Data_V2.csv",sep=",",header=T,stringsAsFactors=F)
  nexperiments=7
alldata_comp=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,"N/A","N/A")[[2]]
genex_replication_prop=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,"N/A","N/A")[[1]]
simresults_comb=data.frame()
for(subsample_number in c(1:12)){
  nsubsamples=subsample_number
  simresults=simresults_generator(alldata_comp,7)
  simresults_comb=rbind(simresults_comb,simresults) ##iterative rbind this is not the most efficient way to do this
}
simresults_concat=simresults_comb%>%
  filter(exp_num%in%c(4))
# simresults_concat=simresults_comb
ggplot(simresults_concat,aes(x=factor(subsample_size),y=log10(p_val)))+
  geom_boxplot(aes(fill=factor(exp_num)))+
  cleanup+
  guides(fill=F)+
  scale_y_continuous(name="log(P-Value)")+
  scale_x_discrete(name="Subsample size")+
  # scale_color_manual(values="#E78AC3")+
  theme(plot.title = element_text(hjust=.5),
      text = element_text(size=26,face="bold"),
      axis.title = element_text(face="bold",size="26",color="black"),
      axis.text=element_text(face="bold",size="24",color="black"))

Expand here to see past versions of unnamed-chunk-3-1.png:
Version Author Date
0cad4ec haiderinam 2019-02-16 

# ggsave("output/ alkati_subsamplesize_pval_fig1c.pdf",width = 10,height = 10,units = "in",useDingbats=F)

Adding simulations for comparisons with mutations:

nsubsamples=12 # maybe this can be removed and instead calculated later.
  nsims<-100 #
  #Positive control 1
  nameposctrl1<-'BRAF'
  #Positive control 1
  nameposctrl2<-'NRAS'
  #Oncogene in Question
  namegene<-'ATI'
  #Mutation Boolean (Y or N)
  mtn<-'Y'
  #Name Mutation for Positive Ctrl 1
  nameposctrl1mt<-'V600E'
  #Name of Mutation for Positive Ctrl 2
  nameposctrl2mt<-'Q61L'
  
  # alldata=read.csv("../data/all_data.csv",sep=",",header=T,stringsAsFactors=F)
  alldata=read.csv("data/all_data.csv",sep=",",header=T,stringsAsFactors=F)
  nexperiments=7
###For mutation
alldata_comp=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,nameposctrl1mt,nameposctrl2mt)[[2]]
genex_replication_prop=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,nameposctrl1mt,nameposctrl2mt)[[1]]
  simresults=simresults_generator(alldata_comp,7)
simresults$mtn='Y'

####For no mutation
mtn='N'
alldata_comp=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,"N/A","N/A")[[2]]
genex_replication_prop=alldata_compiler(alldata,nameposctrl1,nameposctrl2,namegene,mtn,"N/A","N/A")[[1]]
  simresults_nomtn=simresults_generator(alldata_comp,7)
simresults_nomtn$mtn='N'

simresults=rbind(simresults,simresults_nomtn)

Generating the P-value plots for Figure 2. Will show ati vs braf, ati vs nras, for with-muts and without-muts

simresults[simresults$exp_num==1,]$exp_name="BRAF & ALKATI"
simresults[simresults$exp_num==3,]$exp_name="NRAS & ALKATI"
simresults[simresults$exp_num==4,]$exp_name="BRAF & NRAS"
simresults$exp_name=factor(simresults$exp_name,levels=c("1","5","6","7","BRAF & ALKATI","NRAS & ALKATI","BRAF & NRAS"))
simresults$mtn_tag='N'
simresults[simresults$mtn=='Y',]$mtn_tag="Mutation-specific"
simresults[simresults$mtn=='N',]$mtn_tag="Non mutation-specific"

simresults$mtn_tag=factor(simresults$mtn_tag,levels=c("Non mutation-specific","Mutation-specific"))
simresults_concat=simresults%>%
  filter(exp_num==c(1,3,4))
Warning in exp_num == c(1, 3, 4): longer object length is not a multiple of
shorter object length
ggplot(simresults_concat,aes(x=factor(exp_name),y=log10(p_val)))+
  geom_boxplot(aes(fill=factor(exp_name)))+
  facet_wrap(~factor(mtn_tag))+
  cleanup+
  guides(fill=F)+
  scale_y_continuous(name="log(P-Value)")+
  scale_x_discrete(name="Gene Pair")+
  scale_fill_brewer(palette = "Set2",name="Gene Pair")+
  theme(plot.title = element_text(hjust=.5),
      text = element_text(size=26,face="bold"),
      axis.title = element_text(face="bold",size="26",color="black"),
      axis.text=element_text(face="bold",size="20",color="black"))

Expand here to see past versions of unnamed-chunk-5-1.png:
Version Author Date
0cad4ec haiderinam 2019-02-16 

# ggsave("output/alkati_mtn_pval_fig2B.pdf",width = 16,height = 10,units = "in",useDingbats=F)

Session information

sessionInfo()
R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.3

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  grid      stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] bindrcpp_0.2.2      ggsignif_0.4.0      usethis_1.4.0      
 [4] devtools_2.0.1      RColorBrewer_1.1-2  reshape2_1.4.3     
 [7] ggplot2_3.1.0       doParallel_1.0.14   iterators_1.0.10   
[10] foreach_1.4.4       dplyr_0.7.8         VennDiagram_1.6.20 
[13] futile.logger_1.4.3 workflowr_1.1.1     tictoc_1.0         
[16] knitr_1.21         

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5     xfun_0.4             remotes_2.0.2       
 [4] purrr_0.3.0          colorspace_1.4-0     htmltools_0.3.6     
 [7] yaml_2.2.0           rlang_0.3.1          pkgbuild_1.0.2      
[10] R.oo_1.22.0          pillar_1.3.1         glue_1.3.0          
[13] withr_2.1.2          R.utils_2.7.0        sessioninfo_1.1.1   
[16] lambda.r_1.2.3       bindr_0.1.1          plyr_1.8.4          
[19] stringr_1.3.1        munsell_0.5.0        gtable_0.2.0        
[22] R.methodsS3_1.7.1    codetools_0.2-16     evaluate_0.12       
[25] memoise_1.1.0        labeling_0.3         callr_3.1.1         
[28] ps_1.3.0             Rcpp_1.0.0           backports_1.1.3     
[31] scales_1.0.0         formatR_1.5          desc_1.2.0          
[34] pkgload_1.0.2        fs_1.2.6             digest_0.6.18       
[37] stringi_1.2.4        processx_3.2.1       rprojroot_1.3-2     
[40] cli_1.0.1            tools_3.5.2          magrittr_1.5        
[43] lazyeval_0.2.1       tibble_2.0.1         futile.options_1.0.1
[46] crayon_1.3.4         whisker_0.3-2        pkgconfig_2.0.2     
[49] prettyunits_1.0.2    assertthat_0.2.0     rmarkdown_1.11      
[52] rstudioapi_0.9.0     R6_2.3.0             git2r_0.24.0        
[55] compiler_3.5.2

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