GDSC_DRUG_COMBO_TOP_HITS.Rmd

---
title: "A multi-task learning framework to uncover feature interactions in large scale drug screening experiments"
author: "MI YANG"
date: "`r doc_date()`"
package: "`r pkg_ver('BiocStyle')`"
vignette: >
  %\VignetteIndexEntry{Bioconductor style for HTML documents}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
output: 
  BiocStyle::html_document
---

```{r include=FALSE, cache=FALSE}

# First generate processed data with Macau regarding TISSUE_SPECIFIC_GDSC 
# https://github.com/saezlab/Macau_project_1
#  $path/MACAU_PROJECT/DATA_RESULT_STORAGE/TISSUE_SPECIFIC_GDSC/target_progeny11/MEAN/
#  $path/MACAU_PROJECT/DATA_RESULT_STORAGE/TISSUE_SPECIFIC_GDSC/target_progeny11_PERMUTATION`

path <- "~/Documents/RWTH_Aachen"  
load(paste0(path,"/SANGER_DATA/MASTER_LIST_22112013.ro")) 
source(paste0(path,"/MACAU_PROJECT/interaction_matrix_Functions.R"))
source(paste0(path,"/FUNCTIONS/PLOT.R"))
source(paste0(path,"/FUNCTIONS/general_functions.R"))
DRUG_ANALYSIS_SET_update$Drug.Name <- DRUG_ANALYSIS_SET$DRUG_NAME

target <- read.csv(paste0(path,"/macau_work_dir/macau_test_sanger/DATA/target"), check.names = F) ; drug_names <- target[,1] ; target <- target[ ,-1]
target_to_remove <- c()
for (i in 1:length(colnames(target))) {
protein_target <- print_target_GDSC (protein_target= colnames(target)[i], target_matrix=target, drug_names=drug_names )
if ( length(protein_target[,1]) == 1 ) { target_to_remove <- c(target_to_remove, i) } }
target_to_remove <- c(target_to_remove , which(colnames(target) %in% c("others","not defined" ))  ) 

tissue_label_gdsc <- read.csv(paste0(path,"/SANGER_DATA/tissue_label_gdsc_ID"), row.names=1)
table(tissue_label_gdsc$tissue) ; tissue <- names(which(table(tissue_label_gdsc$tissue) > 20)) 
# print_target_GDSC (protein_target= "GSK3B", target_matrix=target, drug_names=drug_names , prediction=T)
tissue <- c("aero_dig_tract","bone","brain","breast","colon","kidney","leukemia","liver","lung_NSCLC","lung_SCLC","lymphoma","ovary","pancreas","skin","soft_tissue","stomach")
 

```

# Set up Pheatmap
```{r echo=FALSE, cache=FALSE, fig.height=12, fig.width=14, fig.keep='last'}
library(RColorBrewer) ; breaksList = seq(-0.45, 0.45, by = 0.01)
plot_pheatmap_angle <- function(mat, row_names, col_names , title ,cluster_rows=T,cluster_cols=T,fontsize=25,fontsize_row=25, fontsize_col=25, scale="none",pvalue,significance=0.40) {
  setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.95, name="vp", just=c("right","top"))), action="prepend")
  pheatmap(mat,color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(length(breaksList)), main=title, fontsize=fontsize, fontsize_row=fontsize_row,fontsize_col=fontsize_col,cluster_rows = cluster_rows, cluster_cols = cluster_cols,scale=scale,display_numbers = matrix(ifelse(pvalue < significance, "*", ""), nrow(pvalue)))
  setHook("grid.newpage", NULL, "replace")
  grid.text(row_names, x=-0.03, rot=90, gp=gpar(fontsize=30))
  grid.text(col_names, y=0.01, gp=gpar(fontsize=30)  )
}

show_target_interaction <- function(tissue_name,t) {
  mat<-retrieve_interaction_plot(drug_feature_name="target",cell_feature_name="progeny11",selection="conservative",cut_off="single",tissue_name = tissue)[[1]] ; mat <- mat[[tissue_number]]
  mat<-data.matrix(mat);mat_t<-mat[t,]
  pathway <- colSums(mat_t) ; pathway_ordered <- pathway[order(-pathway)] ; pathway_ordered ; mat_t <- mat_t[ , names(pathway_ordered) ]
  pathway_up <- which(pathway_ordered/pathway_ordered[1] > up_limit) ; pathway_up
  pathway_down <- which(pathway_ordered/pathway_ordered[length(pathway_ordered)] > down_limit) ; pathway_down
  
  pvalue <- retrieve_interaction_plot(drug_feature_name="target",cell_feature_name="progeny11",selection="conservative",cut_off="single",tissue_name = tissue)[[2]]; 
  pvalue <- pvalue[[tissue_number]][rownames(mat_t),colnames(mat_t)]
  plot_pheatmap_angle(mat_t, title="Target-PROGENy",cluster_cols=F, row_names="Protein Targets", col_names = "PROGENy pathways",pvalue=pvalue) ; 
  cor.test(mat_t[1, ],mat_t[2, ]) ; mat_t
}

```


# BREAST TOP HITS
```{r echo=FALSE, cache=FALSE, fig.height=12, fig.width=14, fig.keep='last'}
### choose tissue
tissue_name <- "breast"  ; tissue_number = which(tissue==tissue_name) 

threshold <- 0.70
up_limit <- threshold ; down_limit <- threshold 

## BREAST 
show_target_interaction (tissue_name="breast", t=c("ALK","AKT1") )

result_folder <- paste0(path,"/MACAU_PROJECT/PLOTS/DRUG_COMBO_PREDICTION/target_progeny_profile/")
dir.create(result_folder,recursive = TRUE)


t=c("EGFR","AKT1")
pdf(file=paste0(result_folder,"01_",tissue_name,"_",paste(t,collapse = "_"),".pdf"), width = 10, height = 5,  compress=TRUE, onefile = F)
show_target_interaction(tissue_name="breast", t )
dev.off()

t=c("MTOR","AKT1")
pdf(file=paste0(result_folder,"02_",tissue_name,"_",paste(t,collapse = "_"),".pdf"), width = 10, height = 5,  compress=TRUE, onefile = F)
show_target_interaction(tissue_name="breast", t )
dev.off()

t=c("BCL2","MTOR")
pdf(file=paste0(result_folder,"03_",tissue_name,"_",paste(t,collapse = "_"),".pdf"), width = 10, height = 5,  compress=TRUE, onefile = F)
show_target_interaction(tissue_name="breast", t )
dev.off()

t=c("EGFR","MTOR")
pdf(file=paste0(result_folder,"04_",tissue_name,"_",paste(t,collapse = "_"),".pdf"), width = 10, height = 5,  compress=TRUE, onefile = F)
show_target_interaction(tissue_name="breast", t )  
dev.off()

t=c("AKT1","BCL2")
pdf(file=paste0(result_folder,"05_",tissue_name,"_",paste(t,collapse = "_"),".pdf"), width = 10, height = 5,  compress=TRUE, onefile = F)
show_target_interaction(tissue_name="breast", t )
dev.off()

t=c("ALK","AKT1")  ##  t=c("ALK","AKT1") 
pdf(file=paste0(result_folder,"06_",tissue_name,"_",paste(t,collapse = "_"),".pdf"), width = 10, height = 5,  compress=TRUE, onefile = F)
show_target_interaction(tissue_name="breast", t )
dev.off()

t=c("PARP1","AKT1")
pdf(file=paste0(result_folder,"07_",tissue_name,"_",paste(t,collapse = "_"),".pdf"), width = 10, height = 5,  compress=TRUE, onefile = F)
show_target_interaction(tissue_name="breast", t )
dev.off()


```


# BREAST SENSITIVITY ANALYSIS
```{r echo=FALSE, cache=FALSE, fig.height=12, fig.width=14, fig.keep='last'}
### choose tissue
tissue_name <- "breast"  ; tissue_number = which(tissue==tissue_name)   ##  prediction_parameter

t <- c("EGFR","AKT1") 
t<-c("MTOR","AKT1") 
t<-c("BCL2","MTOR") 
t <- c("EGFR","MTOR")
t<-c("AKT1","BCL2") 
t<-c("ALK","AKT1") 
t <- c("PARP1","AKT1") 

up_limit <- 0.7 ; down_limit <- 0.5

mat<-retrieve_interaction_plot(drug_feature_name="target",cell_feature_name="progeny11",selection="conservative",cut_off="single",tissue_name = tissue)[[1]] ; 
mat <- mat[[tissue_number]]
mat<-data.matrix(mat);mat_t<-mat[t,]
pathway <- colSums(mat_t) ; pathway_ordered <- pathway[order(-pathway)] ; pathway_ordered ; mat_t <- mat_t[ , names(pathway_ordered) ]
pathway_up <- which(pathway_ordered/pathway_ordered[1] > up_limit) ; pathway_up
pathway_down <- which(pathway_ordered/pathway_ordered[length(pathway_ordered)] > down_limit) ; pathway_down

pvalue <- retrieve_interaction_plot(drug_feature_name="target",cell_feature_name="progeny11",selection="conservative",cut_off="single",tissue_name = tissue)[[2]]; 
pvalue <- pvalue[[tissue_number]][rownames(mat_t),colnames(mat_t)]
plot_pheatmap(mat_t, title="Target-PROGENy",cluster_cols=F, row_names="Protein Targets", col_names = "PROGENy pathways") 


## PLOT the result
library(openxlsx)
target_pairs <- c("AKT/EGFR","AKT/MTOR","BCL2/MTOR","EGFR/MTOR","AKT/BCL2","AKT/ALK","AKT/PARP1","BRAF/IR")

Line_plot <- function(df,title,X_label,Y_label,text_size=35) { 
g <-  ggplot(df, aes(x=x, y=y, group=GROUP)) + geom_line(aes(color=GROUP), size=2) + ggtitle(title) + ylab(Y_label) + xlab(X_label) + 
    theme(legend.position="bottom",legend.title = element_text(size=text_size),legend.text = element_text(size=text_size),plot.title=element_text(size=text_size+8,hjust=0.5),
          axis.text=element_text(colour="black",size=text_size),
          axis.title.x=element_text(colour="black",size=text_size),axis.title.y=element_text(colour="black",size=text_size),
          panel.background = element_rect(fill='white'),panel.grid.major = element_line(colour = "grey90") ) +
    scale_y_continuous( breaks=c(-0.25,0,0.25,0.5,0.75,1), limits=c(-0.3, 0.75)) 
return(g)
}


library(cowplot)
result_folder <- paste0(path,"/MACAU_PROJECT/PLOTS/DRUG_COMBO_PREDICTION/")

pdf(file=paste0(result_folder,paste("SENSITIVITY_ANALYSIS", sep="_"),".pdf"), width = 30, height = 30,  compress=TRUE, onefile = T)
par(mfrow=c(3,3))
  fig <- list()
  for(i in 1:length(target_pairs)) {
    df <- read.xlsx(paste0(path,"/MACAU_PROJECT/YangCombination2019/Tables/Sensitivity_analysis.xlsx"), i )
    colnames(df) <- c("x","y","x","y")
    df2 <- df[ ,1:2] ; df2 <- rbind(df2,df[ ,3:4])
    GROUP <- c(rep("Sensitive", 5), rep("Resistant", 5))
    df2 <- cbind(df2,GROUP)
    
    fig[[i]] <- Line_plot(df2,title=target_pairs[i],X_label = "Threshold of group membership", "Prediction performance") 
  }
plot_grid(fig[[1]],fig[[2]],fig[[3]],fig[[4]],fig[[5]],fig[[6]],fig[[7]],fig[[8]], nrow = 3, ncol = 3 , scale = 0.85)
dev.off()


```


# BREAST LOOCV parameter optimization (individual optimization)
```{r echo=FALSE, cache=FALSE, fig.height=12, fig.width=14, fig.keep='last'}

range <- c(0.5,0.6,0.7,0.8,0.9)

prediction_parameter <- c()
for(test in 1:(length(target_pairs)-1)) {  ##     test = 1
  
  training <- 1:(length(target_pairs)-1)
  training <- training[training != test]
  
  df_training <- c()
  for(train in training ) {  #  train=2
    df <- read.xlsx(paste0(path,"/MACAU_PROJECT/YangCombination2019/Tables/Sensitivity_analysis.xlsx"), train )
    df_training <- rbind(df_training, df)
  }
  
  training_result <- c()
  for(value in range ) {  #  value=0.5
    df_training_value <- df_training[ df_training$Top.sensitive.group.threshold.of.inclusion == value , ]
    training_result <- rbind(training_result, c(mean(df_training_value[ ,2]),mean(df_training_value[ ,4])))
  }
  
  prediction_parameter <- rbind(prediction_parameter , c( range[which.max(training_result[,1])],range[which.max(training_result[,2])] ) )
}

average_prediction_parameter <- colMeans(prediction_parameter)

```



# COLON TOP HITS 
```{r echo=FALSE, cache=FALSE, fig.height=12, fig.width=14, fig.keep='last'}
### choose tissue
tissue_name <- "colon"  ; tissue_number = which(tissue==tissue_name) 

threshold <- 0.70
up_limit <- threshold ; down_limit <- threshold 
 
## COLON from SANGER
t <- c("BRAF","IR"); high<-c("TNFa","Hypoxia","MAPK"); low<-c("NFkB","VEGF","PI3K"); genomics<-c(); conc <- "min"
t <- c("BRAF","IGF1R","IR"); high<-c("Hypoxia","p53","MAPK"); low<-c("Trail","VEGF","PI3K"); genomics<-c(); conc <- "max"


result_folder <- paste0(path,"/MACAU_PROJECT/PLOTS/DRUG_COMBO_PREDICTION/target_progeny_profile/")

t <- c("BRAF","IGF1R","IR")
pdf(file=paste0(result_folder,"08_",tissue_name,"_",paste(t,collapse = "_"),".pdf"), width = 10, height = 5,  compress=TRUE, onefile = F)
show_target_interaction(tissue_name="colon", t )
dev.off()

```

# COLON SENSITIVITY ANALYSIS
```{r echo=FALSE, cache=FALSE, fig.height=12, fig.width=14, fig.keep='last'}
### choose tissue
tissue_name <- "colon"  ; tissue_number = which(tissue==tissue_name) 

t<-c("BRAF","IR","IGF1R") 
t<-c("EGFR","PLK3")
t<-c("EGFR","PLK3")
t<-c("EGFR","PLK3")
t<-c("EGFR","PLK3")

up_limit <- 0.67 ; down_limit <- 0.52   ##   up_limit <- 0.67 ; down_limit <- 0.52

mat<-retrieve_interaction_plot(drug_feature_name="target",cell_feature_name="progeny11",selection="conservative",cut_off="single",tissue_name = tissue)[[1]] ; mat <- mat[[tissue_number]]
mat<-data.matrix(mat);mat_t<-mat[t,]
pathway <- colSums(mat_t) ; pathway_ordered <- pathway[order(-pathway)] ; pathway_ordered ; mat_t <- mat_t[ , names(pathway_ordered) ]
pathway_up <- which(pathway_ordered/pathway_ordered[1] > up_limit) ; pathway_up
pathway_down <- which(pathway_ordered/pathway_ordered[length(pathway_ordered)] > down_limit) ; pathway_down

pvalue <- retrieve_interaction_plot(drug_feature_name="target",cell_feature_name="progeny11",selection="conservative",cut_off="single",tissue_name = tissue)[[2]]; 
pvalue <- pvalue[[tissue_number]][rownames(mat_t),colnames(mat_t)]
plot_pheatmap(mat_t, title="Target-PROGENy",cluster_cols=F, row_names="Protein Targets", col_names = "PROGENy pathways") 


```


# REFERENCE PROTEIN: all combinations with a SPECIFIC protein target

```{r include=FALSE, cache=FALSE}

###################################################### CHOOSE TISSUE 
tissue_name <- "colon"  ; tissue_number = which(tissue==tissue_name) 
protein <- "BRAF"

x <- retrieve_interaction_plot(drug_feature_name="target",cell_feature_name="progeny11",selection="conservative",cut_off="single",tissue_name = tissue)[[1]] ; x <- x[[tissue_number]]
reference <- as.numeric(x[protein, ]) ; y <- x[ -which(rownames(x)==protein) , ]

functional_similarity <- c()
for(i in 1:length(y[,1])) { t <- cor.test(reference, as.numeric(y[i, ]) ) ; p <- t$estimate ; functional_similarity <- c(functional_similarity,p) }
names(functional_similarity) <- rownames(y) ; functional_similarity <- functional_similarity[order(-functional_similarity)] ;  functional_similarity[1:10]

############################## check top PATHWAY ranking ##############################

reference_pathway <- "MAPK"

pathway_rank <- c()
for(i in 1:length(functional_similarity)) { 
  t <- c(protein,names(functional_similarity[i]))
  mat<-retrieve_interaction_plot(drug_feature_name="target",cell_feature_name="progeny11",selection="conservative",cut_off="single",tissue_name = tissue)[[1]] ; mat <- mat[[tissue_number]]
  mat<-data.matrix(mat);mat_t<-mat[t,]
  pathway <- colSums(mat_t) ; pathway_ordered <- pathway[order(-pathway)] ; pathway_ordered
  pathway_rank <- c(pathway_rank, which(names(pathway_ordered)==reference_pathway))
}
#    print_target_GDSC (protein_target= "AR" , target_matrix=target, drug_names=drug_names, T)
##########
Nb_target_per_drug <- c()
for(i in 1:length(functional_similarity)) { 
d <- print_target_GDSC (protein_target= names(functional_similarity[i]) , target_matrix=target, drug_names=drug_names, T)
t <- d$PUTATIVE_TARGET ; t <- unlist(strsplit(t," ")) ; Nb <- length(t)/length(d$PUTATIVE_TARGET)
Nb_target_per_drug <- c(Nb_target_per_drug, Nb)
}
##########
result <- cbind(functional_similarity,pathway_rank,Nb_target_per_drug)
colnames(result)[2] <- paste0(reference_pathway,"_rank")
#    print_target_GDSC (protein_target= "IR" , target_matrix=target, drug_names=drug_names, T)

subset <- result[ result[ ,3]<3 , ] #  too many targets: NOT reliable.
subset <- result[ result[ ,2]<=3 , ]  ## desired rank of reference pathway
 
########## check on PUBMED which target most often associated with progeny pathways ##########

if(!dir.exists(paste0(path,"/MACAU_PROJECT/DATA_RESULT_STORAGE/Pubmed/"))) dir.create(paste0(path,"/MACAU_PROJECT/DATA_RESULT_STORAGE/Pubmed/"));

rownames(subset)[1] <- "insulin receptor"
m <- find_pubmed_association(rownames(subset),colnames(mat),constant_term=tissue_name)
m2 <- cbind(subset,m)
write.csv(m2, paste0(path,"/MACAU_PROJECT/DATA_RESULT_STORAGE/Pubmed/drug_combo_candidates_",tissue_name,"_",protein,"_",reference_pathway))

```


# MINING all possible combinations

```{r include=FALSE, cache=FALSE}
############################################### MINING all possible combinations ###############################################

### AZ

for(tissue_name in c("breast","colon","lung_NSCLC")) {

  tissue_number = which(tissue==tissue_name) ##  breast colon lung_NSCLC
  
  x <- retrieve_interaction_plot(drug_feature_name="target",cell_feature_name="progeny11",selection="conservative",cut_off="single",tissue_name = tissue)[[1]] ; x <- x[[tissue_number]]
  target_SANGER <- rownames(x)
  target_combo <- read.csv(paste0(path,"/DREAM_AZ/DATA/target_combo"), row.names=1, check.names = F)
  target_AZ <- colnames(target_combo)
  match <- match (target_SANGER,  target_AZ) ; table <- cbind(target_SANGER, match) ; table <- table[complete.cases(table), ]
  target_common <- table[,1]
  
  all_combo_target <- t(combn(target_common, m=2))
  corr <- c()
  for(i in 1:length(all_combo_target[,1])) {   c <- cor.test(as.numeric(x[ all_combo_target[i,1], ]),as.numeric(x[ all_combo_target[i,2], ]) ) ; corr <- c(corr, c$estimate) }
  all_combo_target <- cbind(all_combo_target , corr )
  all_combo_target <- all_combo_target[order(-as.numeric(all_combo_target[ ,3]) ) , ]  ; colnames(all_combo_target)[1:2] <- c("target1","target2") 
  rownames(all_combo_target) <- paste(all_combo_target[ ,1],all_combo_target[ ,2],sep="/")
  
  write.csv(all_combo_target, paste0(path,"/DREAM_AZ/DATA/target_common_AZ_SANGER_",tissue_name))
  
}

```


# Distribution of target functional similarity

```{r include=FALSE, cache=FALSE}

target_common_AZ_SANGER_breast <- read.csv(paste0(path,"/DREAM_AZ/DATA/target_common_AZ_SANGER_breast"), row.names = 1)
target_common_AZ_SANGER_colon <- read.csv(paste0(path,"/DREAM_AZ/DATA/target_common_AZ_SANGER_colon"), row.names = 1)
target_common_AZ_SANGER_lung_NSCLC <- read.csv(paste0(path,"/DREAM_AZ/DATA/target_common_AZ_SANGER_lung_NSCLC"), row.names = 1)

nice_plot_list <- function (result, title, text_size, title_size, Y_label="Pearson correlation (r)" ,X_label="features") {
  result_transformed <- c()
  for (i in 1:length(result)) {
    v <- na.omit(result[[i]]) ; v <- cbind(rep(i,each=length(na.omit(result[[i]]))), v)
    result_transformed <- rbind(result_transformed, v)
    colnames(result_transformed) <- c("features", "correlation")
  }
  result_transformed <- data.frame(result_transformed)
  result_transformed$features <- factor(result_transformed$features,levels=1:length(names(result)), labels=names(result)) 
    r <- ggplot(data = result_transformed, aes(y = correlation, x = features, fill = features )) + geom_point(colour="black",size=0.5) + geom_violin(trim = FALSE) + 
    geom_point(stat = "summary", fun.y = "mean", size = I(5), color = I("black") ) + 
    ggtitle(title) + ylab(Y_label) + xlab(X_label) + 
    theme(legend.position="none",plot.title=element_text(size=text_size+8,hjust=0.5),
          axis.text=element_text(colour="black",size=text_size, angle = 45,hjust=1 ),
          axis.title.x=element_text(colour="black",size=text_size),axis.title.y=element_text(colour="black",size=text_size),
          panel.background = element_rect(fill='white'),panel.grid.major = element_line(colour = "grey90") )  + 
    scale_y_continuous( breaks=c(-1,-0.5,0,0.5,1), limits=c(-1,1))  +
   geom_hline(yintercept = 0.7 , col="red", size=1) +
   geom_hline(yintercept = -0.7 , col="red", size=1)
  grid.arrange(r, ncol = 1)

}

df_list <- list(as.numeric(target_common_AZ_SANGER_breast$corr),as.numeric(target_common_AZ_SANGER_colon$corr),as.numeric(target_common_AZ_SANGER_lung_NSCLC$corr))
names(df_list) <- c("breast","colon","lung NSCLC")

result_folder <- paste0(path,"/MACAU_PROJECT/PLOTS/DRUG_COMBO_PREDICTION/target_progeny_profile/")
pdf(file=paste0(result_folder,"Distribution_target_functional_similarity.pdf"), width = 10, height = 10,  compress=TRUE, onefile = F)
nice_plot_list (df_list, title = "Distribution of Target functional similarity",text_size = 22, title_size = 6, X_label="tissue")
dev.off()


```


# check functional similarity for a given pair across tissues

```{r include=FALSE, cache=FALSE}

###################################################### CHOOSE TISSUE 
search_parwise_similarity_across_tissues <- function(target_1,target_2) {
  pair_table <- expand.grid(target_1,target_2)
  pair_table$Var1 <- as.character(pair_table$Var1)
  pair_table$Var2 <- as.character(pair_table$Var2)
  
  corr_table <- c()
  for(pair in 1:length(pair_table[ ,1])) { #   pair = 3
    corr_store <- c()
    for(tissue_number in 1:length(tissue)) {  # tissue_number=1 
      x <- retrieve_interaction_plot(drug_feature_name="target",cell_feature_name="progeny11",selection="conservative",cut_off="single",tissue_name = tissue)[[1]] ; x <- x[[tissue_number]]
      pcorr <- cor.test(as.numeric(x[pair_table[pair,1], ]),as.numeric(x[pair_table[pair,2], ]))
      corr_store <- c(corr_store, pcorr$estimate)
    }
    corr_table <- rbind(corr_table, corr_store)
  }
  colnames(corr_table) <- tissue
  
  pair_table[ ,1] <- as.character(pair_table[ ,1])
  pair_table[ ,2] <- as.character(pair_table[ ,2])
  rownames(corr_table) <- paste0(pair_table[ ,1],"/",pair_table[ ,2])
  return(corr_table)
}

target_1 <- c("MEK1")
target_2 <- c("MTOR","MTORC1","MTORC2")
corr_table_1 <- search_parwise_similarity_across_tissues(target_1,target_2)

target_1 <- c("BRAF")
target_2 <- c("MEK1","MEK2")
corr_table_2 <- search_parwise_similarity_across_tissues(target_1,target_2)


target_1 <- c("ABL")
target_2 <- c("PI3K (class 1)","PI3Kgamma")
corr_table_2 <- search_parwise_similarity_across_tissues(target_1,target_2)


```