export median data for the challenge

export_dream_median_phospho.R

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+
+
+# 3 parts:
+# 1. export data for public use 
+#	- remove the conditions used for the scoring
+# 2. export prediction coinditions:
+#	- export the conditions and reporters masked with NAs for aim2
+# 3. export the validation data. 
+
+
+median_interpolated_data <- read_rds("./data/median_data/interpolated_median_all_reporters_mine.rds")
+median_data <- median_interpolated_data %>% spread(reporter,value)
+# cell-line in aims
+cell_line_sheet <- readxl::read_excel("./data/cell_line_distribution.xlsx",sheet = 1,range = "A1:I69")
+
+
+# 1. export data for public use --------------------------------------------------
+# export median phosphorylation data after removing the conditions that are to be predicted
+# Export data for the public: all data except the test conditions   
+# we remove imTOR from all cell-lines. 
+# then depending which cell-line is used in which AIM, we remove a subset of the data. 
+
+
+public_data <- median_data %>% filter(treatment != "imTOR")  %>%# remove imTOR condition from all
+	group_by(cell_line) %>% nest(.key = "data") %>% 
+	mutate(cleaned_data = map2(data,cell_line,function(data,cell_line){
+
+		current_cell_line = cell_line
+
+		purpose = cell_line_sheet %>% filter(cell_line == current_cell_line)
+		purpose[1,as.logical(is.na(purpose[1,]))] = ""  
+
+
+		if(purpose$AIM_1_1 =="test"){
+			# remove psites to be predicted
+			data[,c("p.ERK", "p.Akt.Ser473.","p.S6","p.HER2", "p.PLCg2")] = NA_real_
+
+		}else if(purpose$AIM_1_2_1 == "test"){
+			# remove all data in condition to be predicted
+
+			data = data %>% filter(treatment!="iPKC")
+
+		}else if(purpose$AIM_1_2_2 == "test"){
+
+			# nothing to do here, imTOR condition already removed. 
+
+		}else if(purpose$AIM2 == "test"){
+			# providing only the full condition for the test
+			data = data %>% filter(treatment=="full")
+
+		}
+
+		return(data)
+	})) %>% unnest(cleaned_data)
+
+
+write_csv(public_data,path = "./challenge_data/median_phospho/median_phospho_data.csv")	
+
+### 2. export prediction conditions --------------------------------------------
+# export the conditions and use NA fo the values that the participants ahve to predict
+
+prediction_data <- median_data %>% filter(treatment != "imTOR")  %>%# remove imTOR condition from all
+	group_by(cell_line) %>% nest(.key = "data") %>% 
+	mutate(cleaned_data = map2(data,cell_line,function(data,cell_line){
+
+		current_cell_line = cell_line
+
+		purpose = cell_line_sheet %>% filter(cell_line == current_cell_line)
+		purpose[1,as.logical(is.na(purpose[1,]))] = ""  
+
+		reporters = colnames(data)[-1:-2]
+
+		 if(purpose$AIM2 == "test"){
+			# providing only the full condition for the test
+			data = data %>% filter(treatment !="full") %>%
+				mutate_at(reporters,~NA_real_)
+
+		}else return(tibble())
+
+		return(data)
+
+	})) %>% unnest(cleaned_data)
+
+
+write_csv(prediction_data,path = "./challenge_data/predict_conditions/AIM_2_median_data.csv")	
+
+
+### # 3. export the validation data.  --------------------------------------------
+# export the conditions for validaion
+
+validation_data <- median_data %>% filter(treatment != "imTOR")  %>%# remove imTOR condition from all
+	group_by(cell_line) %>% nest(.key = "data") %>% 
+	mutate(cleaned_data = map2(data,cell_line,function(data,cell_line){
+
+		current_cell_line = cell_line
+
+		purpose = cell_line_sheet %>% filter(cell_line == current_cell_line)
+		purpose[1,as.logical(is.na(purpose[1,]))] = ""  
+
+		reporters = colnames(data)[-1:-2]
+
+		if(purpose$AIM2 == "test"){
+			# providing only the full condition for the test
+			data = data %>% filter(treatment !="full") 
+
+		}else return(tibble())
+
+		return(data)
+
+	})) %>% unnest(cleaned_data)
+
+
+write_csv(validation_data,path = "./challenge_data/validation_data/AIM_2_median_data.csv")