Update 3 files

- /01_Differential_Expression_Analysis/merging/merge_rm.pdf
- /01_Differential_Expression_Analysis/merging/merge_main.html
- /01_Differential_Expression_Analysis/merging/merge_main.Rmd

01_Differential_Expression_Analysis/merging/merge_main.Rmd

+---
+title: "Untitled"
+output:
+  html_document:
+    df_print: paged
+---
+
+```{r}
+#data with ".top.table.tsv" are found under folder data_for_merging.
+library(readr)
+library(data.table)
+library(GEOquery)
+library(dplyr)
+#require(hta20transcriptcluster.db)
+###############miRNA1##########################################
+#read the top significant genes found by using GEO2R tool:
+GSE43732_top_table <- read_delim("GSE43732.top.table.tsv", 
+                                 delim = "\t", escape_double = FALSE, 
+                                 trim_ws = TRUE)
+#filter with the help of padj. values
+resFilt_GSE43732 <- GSE43732_top_table[which(GSE43732_top_table$adj.P.Val < 0.05 & abs(GSE43732_top_table$logFC) > 0.263), ]
+
+
+# load series and platform data from GEO
+gset1 <- getGEO("GSE43732", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
+# choose the index
+if (length(gset1) > 1) idx <- grep("GPL6480", attr(gset1, "names")) else idx <- 1
+gset1 <- gset1[[idx]]
+#load the expression data.
+t0<- gset1@assayData[["exprs"]]
+
+# extract the information of cancer/normal/adenoma from the metadata.
+title0<- gset1@phenoData@data[["characteristics_ch1"]]
+#extract the sample names from the expression data.
+clname<- colnames(t0)
+# make dataframe of the sample names and their corresponding information about cancer/non cancer/adema
+d_col<- as.data.frame(title0,clname)
+# get metadata out of S4 object
+gene_data_frame = fData(gset1)
+d<-merge(t0,gene_data_frame, by.x=0, by.y= "ID")
+d_f<- merge(d,resFilt_GSE43732, by.x="Row.names", by.y= "ID")
+duplicated_genes <- d_f$Row.names[duplicated(d_f$Row.names)]
+d_f1<- d_f %>% distinct(`Row.names`, .keep_all = T)
+
+```
+```{r}
+###############miRNA2####this was just a try(not included in the project)######################################
+#read the top significant genes found by using GEO2R tool:
+GSE67269_top_table <- read_delim("GSE67269.top.table.tsv", 
+                                 delim = "\t", escape_double = FALSE, 
+                                 trim_ws = TRUE)
+#filter with the help of padj. values
+resFilt_GSE67269 <- GSE67269_top_table[which(GSE67269_top_table$adj.P.Val < 0.05 & abs(GSE67269_top_table$logFC) > 0.263), ]
+
+
+# load series and platform data from GEO
+gset1 <- getGEO("GSE67269", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
+# choose the index
+if (length(gset1) > 1) idx <- grep("GPL19823 ", attr(gset1, "names")) else idx <- 1
+gset1 <- gset1[[1]]
+mi_ids<- as.data.frame(gset1@featureData@data[["miRNA_ID"]])
+#load the expression data.
+t0<- gset1@assayData[["exprs"]]
+h_exprs<- merge(mi_ids,t0,by=0)
+h_exprs["Row.names"]<- NULL
+#head (h_exprs,20)
+colnames(h_exprs)[1] <- "miRNA_ID"
+h_exprs$miRNA_ID <- gsub("\\*", "", h_exprs$miRNA_ID)
+# Aggregate the data by miRNA_ID and take maximum values
+aggregated_data <- aggregate(. ~  miRNA_ID, h_exprs, max)
+
+# extract the information of cancer/normal/adenoma from the metadata.
+title0<- gset1@phenoData@data[["characteristics_ch1"]]
+#extract the sample names from the expression data.
+clname<- colnames(aggregated_data[,-1])
+# make dataframe of the sample names and their corresponding information about cancer/non cancer/adema
+h_col<- as.data.frame(title0,clname)
+# get metadata out of S4 object
+gene_data_frame = fData(gset1)
+h<-aggregated_data
+
+h_f<- merge(h,resFilt_GSE67269, by.x="miRNA_ID", by.y= "miRNA_ID")
+duplicated_genes <- h_f$Row.names[duplicated(h_f$Row.names)]
+h_f1<- h_f %>% distinct(`miRNA_ID`, .keep_all = T)
+#merge all the data with the help of genesymbols.
+#ab<- merge(d_f1,h_f1,by.x="Row.names",by.y="miRNA_ID")
+####################################################
+#clean any duplicate gene symbols:
+occurrenceClean <- d_f1[!duplicated(d_f1$Row.names),]
+cleany<- occurrenceClean[ , colSums(is.na(occurrenceClean))==0]# remove columns with NA
+# remove unnecessary columns
+cleanyed<- cleany[, grep("GSM|Row.names", colnames(cleany))]
+#again remove more columns
+last_cleany<- na.omit(cleanyed)# remove any rows with NA.
+#save the data
+#fwrite(d_col, file = "miRNA_DS_metadata_col_info.csv", sep = ",",row.names = TRUE)
+#fwrite(last_cleany, file = "miRNA_DS_preprocessed_data.csv",sep= ",")
+
+
+
+```
+```{r}
+######################mRNA############################################################
+GSE70409_top_table <- read_delim("GSE70409.top.table.tsv", 
+                                 delim = "\t", escape_double = FALSE, 
+                                 trim_ws = TRUE)
+#filter with the help of padj. values
+resFilt_GSE70409 <- GSE70409_top_table[which(GSE70409_top_table$adj.P.Val < 0.05 & abs(GSE70409_top_table$logFC) > 0.263), ]
+
+
+# load series and platform data from GEO
+gset1 <- getGEO("GSE70409", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
+# choose the index
+if (length(gset1) > 1) idx <- grep("GPL6480", attr(gset1, "names")) else idx <- 1
+gset1 <- gset1[[idx]]
+#load the expression data.
+t0<- gset1@assayData[["exprs"]]
+
+# extract the information of cancer/normal/adenoma from the metadata.
+title0<- gset1@phenoData@data[["characteristics_ch1"]]
+#extract the sample names from the expression data.
+clname<- colnames(t0)
+# make dataframe of the sample names and their corresponding information about cancer/non cancer/adema
+a_col<- as.data.frame(title0,clname)
+#pull out the gene information from metadata of GEO
+gene_data_frame = fData(gset1)
+#make the data ready:
+a<-merge(t0,gene_data_frame, by.x=0, by.y= "ID")
+a_f<- merge(a,resFilt_GSE70409, by.x="Row.names", by.y= "ID")
+a_f1<- a_f %>% distinct(`Gene_symbol`, .keep_all = T)
+
+```
+```{r}
+####################do the same for another data##########################################################
+GSE20347_top_table <- read_delim("GSE20347.top.table.tsv", 
+                                 delim = "\t", escape_double = FALSE, 
+                                 trim_ws = TRUE)
+#filter with the help of padj. values
+resFilt_GSE20347 <- GSE20347_top_table[which(GSE20347_top_table$adj.P.Val < 0.05 & abs(GSE20347_top_table$logFC) > 0.263), ]
+
+
+# load series and platform data from GEO
+gset1 <- getGEO("GSE20347", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
+# choose the index
+if (length(gset1) > 1) idx <- grep("GPL6480", attr(gset1, "names")) else idx <- 1
+gset1 <- gset1[[idx]]
+#load the expression data.
+t0<- gset1@assayData[["exprs"]]
+
+# extract the information of cancer/normal/adenoma from the metadata.
+title0<- gset1@phenoData@data[["characteristics_ch1"]]
+#extract the sample names from the expression data.
+clname<- colnames(t0)
+# make dataframe of the sample names and their corresponding information about cancer/non cancer/adema
+c_col<- as.data.frame(title0,clname)
+
+gene_data_frame = fData(gset1)
+
+cc<-merge(t0,gene_data_frame, by.x=0, by.y= "ID")
+c_f<- merge(cc,resFilt_GSE20347, by.x="Row.names", by.y= "ID")
+c_f1<- c_f %>% distinct(`Gene symbol`, .keep_all = T)
+
+
+```
+```{r}
+
+#################do the same for another data##############################################
+GSE29001_top_table <- read_delim("GSE29001.top.table.tsv", 
+                                 delim = "\t", escape_double = FALSE, 
+                                 trim_ws = TRUE)
+resFilt_GSE29001 <- GSE29001_top_table[which(GSE29001_top_table$adj.P.Val < 0.05 & abs(GSE29001_top_table$logFC) > 0.263), ]
+
+gset1 <- getGEO("GSE29001", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
+if (length(gset1) > 1) idx <- grep("GPL6480", attr(gset1, "names")) else idx <- 1
+gset1 <- gset1[[idx]]
+t0<- gset1@assayData[["exprs"]]
+
+gene_data_frame = fData(gset1)
+
+
+title0<- gset1@phenoData@data[["characteristics_ch1.1"]]
+clname<- colnames(t0)
+
+b_col<- as.data.frame(title0,clname)
+
+
+b<-merge(t0,gene_data_frame, by.x=0, by.y= "ID")
+
+b_f<- merge(b,resFilt_GSE29001, by.x="Row.names", by.y= "ID")
+b_f1<- b_f %>% distinct(`Gene symbol`, .keep_all = T)
+
+#############################################
+
+```
+```{r}
+#################do the same for another data##############################################
+GSE23400_top_table <- read_delim("GSE23400.top.table.tsv", 
+                                 delim = "\t", escape_double = FALSE, 
+                                 trim_ws = TRUE)
+resFilt_GSE23400 <- GSE23400_top_table[which(GSE23400_top_table$adj.P.Val < 0.05 & abs(GSE23400_top_table$logFC) > 0.263), ]
+
+gset1 <- getGEO("GSE23400", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
+if (length(gset1) > 1) idx <- grep("GPL6480", attr(gset1, "names")) else idx <- 1
+gset1 <- gset1[[1]]
+t0<- gset1@assayData[["exprs"]]
+
+gene_data_frame = fData(gset1)
+
+
+title0<- gset1@phenoData@data[["source_name_ch1"]]
+clname<- colnames(t0)
+
+g_col<- as.data.frame(title0,clname)
+
+
+g<-merge(t0,gene_data_frame, by.x=0, by.y= "ID")
+
+g_f<- merge(g,resFilt_GSE23400, by.x="Row.names", by.y= "ID")
+g_f1<- g_f %>% distinct(`Gene symbol`, .keep_all = T)
+
+
+```
+```{r}
+###############################################################################
+#merge all the data with the help of genesymbols.
+ab<- merge(a_f1,b_f1,by.x="Gene_symbol",by.y="Gene symbol")
+abc<- merge(ab,c_f1,by.x = "Gene_symbol", by.y= "Gene.symbol")
+abcd<- merge(abc,g_f1,by.x = "Gene_symbol", by.y= "Gene.symbol")
+############################################################
+#order the genesymbol with help of a column:
+occurrence <- abcd[order(abcd$Gene_symbol, abcd$GSM1727130, decreasing=TRUE),]
+#clean any duplicate gene symbols:
+occurrenceClean <- occurrence[!duplicated(occurrence$Gene_symbol),]
+cleany<- occurrenceClean[ , colSums(is.na(occurrenceClean))==0]# remove columns with NA
+# remove unnecessary columns
+cleanyed<- cleany[, grep("GSM|Gene_symbol", colnames(cleany))]
+#again remove more columns
+last_cleany<- na.omit(cleanyed)# remove any rows with NA.
+#bind all metadata cols
+binded_col<- rbind(a_col,b_col,c_col,g_col)
+#write it and save:
+#fwrite(binded_col, file = "mRNA_DS_metadata_col_info.csv", sep = ",",row.names = TRUE)
+```
+```{r a, message=FALSE, warning=FALSE}
+library("readxl")
+#merge with the results of multimir(just a try)
+multi<- read_excel("multimir_results_final.xlsx")
+mer<- merge(last_cleany,multi,by.x="Gene_symbol",by.y="target_symbol")
+mer1<- mer %>% distinct(`Gene_symbol`, .keep_all = T)
+mer1<- mer1[, grep("GSM|Gene_symbol", colnames(mer1))]
+
+
+```
+```{r}
+#############test_data############################
+#GSE38129
+GSE38129_top_table <- read_delim("GSE38129.top.table.tsv", 
+                                 delim = "\t", escape_double = FALSE, 
+                                 trim_ws = TRUE)
+resFilt_GSE38129 <- GSE38129_top_table[which(GSE38129_top_table$adj.P.Val < 0.05 & abs(GSE38129_top_table$logFC) > 0.263), ]
+
+gset1 <- getGEO("GSE38129", GSEMatrix =TRUE, AnnotGPL=TRUE,getGPL= T)
+if (length(gset1) > 1) idx <- grep("GPL6480", attr(gset1, "names")) else idx <- 1
+gset1 <- gset1[[1]]
+t0<- gset1@assayData[["exprs"]]
+
+gene_data_frame = fData(gset1)
+
+
+title0<- gset1@phenoData@data[["source_name_ch1"]]
+clname<- colnames(t0)
+
+t_col<- as.data.frame(title0,clname)
+
+
+t<-merge(t0,gene_data_frame, by.x=0, by.y= "ID")
+
+t_f<- merge(t,resFilt_GSE38129, by.x="Row.names", by.y= "ID")
+t_f1<- t_f %>% distinct(`Gene symbol`, .keep_all = T)
+mer11<- t_f1[, grep("GSM|Gene symbol", colnames(t_f1))]
+mer11 <- mer11 [1: ncol(mer11)-1 ]
+mer11<-na.omit(mer11)
+
+#Save them all:
+
+#fwrite(t_col, file = "mRNA_DS_metadata_col_test_info.csv", sep = ",",row.names = TRUE)
+#fwrite(mer11, file = "mRNA_DS_test_data.csv",sep= ",")
+mer12<- merge(x =mer1 , y = mer11, by.x = "Gene_symbol", by.y="Gene symbol", all.y = TRUE)
+merged_data <- mer12[, names(mer1)]
+merged_data<-na.omit(merged_data)
+#fwrite(merged_data, file = "mRNA_DS_preprocessed_training_data.csv",sep= ",")
+```
+
+
+
+
+
+
+
+
+