diff --git a/MAVEN/Example_Data/lapatinib/.Rhistory b/MAVEN/Example_Data/lapatinib/.Rhistory
deleted file mode 100755
index 7564a46..0000000
--- a/MAVEN/Example_Data/lapatinib/.Rhistory
+++ /dev/null
@@ -1,512 +0,0 @@
-foreach(i = compound_names) %dopar% { # Loop over every compound
-print(i)
-fname = paste0(i,".txt") # Set the file name for the signature file
-compound_sig = cbind(test_df[1],test_df[i]) # Take the gene names and corresponding measurements
-write.table(compound_sig,fname,sep="\t",quote=F,row.names=F,col.names=T,append=T) # Write
-df = read.table(fname,sep="\t",header=T,row.names=1) # Read in the signature .txt file
-TF_genesymbol<-try( # run DoRothEA, confidence levels A, B and C
-runDoRothEA(df, regulon=viper_regulon, confidence_level=c('A','B','C')),
-silent = T
-)
-if(inherits(TF_genesymbol,"try-error")){ # If there is an error for some reason, skip the compound
-next
-}
-TF_uniprot<-GeneSymbol2Uniprot(TF_genesymbol, map, 1, 2) # Map to UniProt
-folder = paste0(i,"_measurements/") # Set folder name
-generate_measfile(measurements=TF_uniprot, topnumber=50, write2folder=folder) # Write TF activities to folder
-}
-# Stop the cluster
-stopCluster(myCluster)
-# Script to prepare DoRoTHea TF activities
-# Input: Matrix of gene expression data (Rows are genes (Entrez), columns are compounds)
-# Output: 1. .txt file for each signature found in the input matrix
-#         2. Folder for each compound (compoundname_measurements)
-#         with TF activities (meas_50.txt) as UniProt ID
-# Import packages
-library(CARNIVAL)
-library(org.Hs.eg.db)
-library(foreach)
-library(doParallel)
-library(plyr)
-library(data.table)
-# Initialise cluster
-n = 2 # change to number of cores needed
-myCluster <- makeCluster(n, type="FORK",outfile="")
-registerDoParallel(myCluster)
-# Load files for dorothea
-file.copy(from=system.file("dorothea_TF_mapping.csv",package="CARNIVAL"),to=getwd(),overwrite=TRUE)
-load(file = system.file("BEST_viperRegulon.rdata",package="CARNIVAL"))
-map<-read.csv("dorothea_TF_mapping.csv")
-#Open matrix
-gexfile = "HT29_6h_10uM.csv"
-gex_df = fread(gexfile,header=TRUE,sep=",") # First row is python header
-gex_df = as.data.frame(gex_df) # Change into df
-compound_names = names(gex_df) # Column names are compounds
-compound_names = compound_names[!compound_names %in% 'Compound_id'] # Get rid of 'Compound_id'
-#Now change gene ids to gene symbols using metadata
-gene_info = fread('gene_info.csv',header=TRUE) # Import metadata
-gene_info = as.data.frame(gene_info) # Read as df
-converted = merge(test_df,gene_info,by.x='Compound_id',by.y='pr_gene_id') # Map to gene symbol
-converted_symbols = converted$pr_gene_symbol # Extract symbols
-test_df$Compound_id = converted_symbols # Make row names into symbols
-compound_names = compound_names[0:10] # test e.g. 10 compounds
-# Run
-output <-
-foreach(i = compound_names) %dopar% { # Loop over every compound
-print(i)
-fname = paste0(i,".txt") # Set the file name for the signature file
-compound_sig = cbind(test_df[1],test_df[i]) # Take the gene names and corresponding measurements
-write.table(compound_sig,fname,sep="\t",quote=F,row.names=F,col.names=T,append=T) # Write
-df = read.table(fname,sep="\t",header=T,row.names=1) # Read in the signature .txt file
-TF_genesymbol<-try( # run DoRothEA, confidence levels A, B and C
-runDoRothEA(df, regulon=viper_regulon, confidence_level=c('A','B','C')),
-silent = T
-)
-if(inherits(TF_genesymbol,"try-error")){ # If there is an error for some reason, skip the compound
-next
-}
-TF_uniprot<-GeneSymbol2Uniprot(TF_genesymbol, map, 1, 2) # Map to UniProt
-folder = paste0(i,"_measurements/") # Set folder name
-generate_measfile(measurements=TF_uniprot, topnumber=50, write2folder=folder) # Write TF activities to folder
-}
-# Stop the cluster
-stopCluster(myCluster)
-# Load files for progeny
-file.copy(from=system.file("model_NatComm+14_human.csv",package="CARNIVAL"),to=getwd(),overwrite=TRUE)
-weight_matrix<-read.csv("model_NatComm+14_human.csv")
-View(weight_matrix)
-# Import matrix
-gexfile = "HT29_6h_10uM.csv"
-# Import matrix
-gexfile = "HT29_6h_10uM.csv"
-gex_df = fread(gexfile,header=TRUE,sep=",") # First row is python header
-gex_df = as.data.frame(gex_df) # As df
-compound_names = names(gex_df) # Column names are compounds
-compound_names = compound_names[!compound_names %in% 'Compound_id'] # Get rid of 'Compound_id'
-i = compound_names[[1]]
-print(i)
-fname <- paste0("/",i, ".txt") # get signature .txt
-fname <- paste0(i, ".txt") # get signature .txt
-df = read.table(fname,sep="\t",header=TRUE,row.names=1) # Read back in file
-df_genenames <- data.frame('gene'=rownames(df),df)
-df_genenames
-#Run progeny
-pathway_scores <- try(
-runPROGENy(df_genenames,weight_matrix,z_scores=F),
-silent = T)
-pathway_scores
-#Generate input files
-folder = paste0(i,"_measurements/scores_")
-scores <- rbind(rownames(pathway_scores),pathway_scores[,1])
-scores
-write.table(scores,paste0(folder,i,".txt"),col.names=F,row.names=F,quote=F,sep='\t') # save
-# Script to prepare PROGENy pathway scores
-# Input: Matrix of gene expression data (Rows are genes (Entrez), columns are compounds), and
-#        The .txt file for each signature (From prepare_input_parellel.R)
-#         And a measurement folder for each compound (From prepare_input_parellel.R)
-# Output: PROGEny pathway weights .txt in each compound's measurement folder
-# Import packages
-library(CARNIVAL)
-library(org.Hs.eg.db)
-library(foreach)
-library(doParallel)
-library(plyr)
-library(data.table)
-# set n to number of cores
-myCluster <- makeCluster(n, type="FORK",outfile="")
-registerDoParallel(myCluster)
-# Load files for progeny
-file.copy(from=system.file("model_NatComm+14_human.csv",package="CARNIVAL"),to=getwd(),overwrite=TRUE)
-weight_matrix<-read.csv("model_NatComm+14_human.csv")
-# Import matrix
-gexfile = "HT29_6h_10uM.csv"
-gex_df = fread(gexfile,header=TRUE,sep=",") # First row is python header
-gex_df = as.data.frame(gex_df) # As df
-compound_names = names(gex_df) # Column names are compounds
-compound_names = compound_names[!compound_names %in% 'Compound_id'] # Get rid of 'Compound_id'
-# test for e.g. 10 compounds
-compound_names = compound_names[1:10]
-output <-
-foreach(i = compound_names) %dopar% {  # loop over each compound
-print(i)
-fname <- paste0(i, ".txt") # get signature .txt
-df = read.table(fname,sep="\t",header=TRUE,row.names=1) # Read back in file
-df_genenames <- data.frame('gene'=rownames(df),df) # make df with rownames = gene symbols
-#Run progeny
-pathway_scores <- try(
-runPROGENy(df_genenames,weight_matrix,z_scores=F),
-silent = T)
-if(inherits(pathway_scores,"try-error")){ # if it fails then skip
-next
-}
-#Generate input files
-folder = paste0(i,"_measurements/scores_") # get folder name
-scores <- rbind(rownames(pathway_scores),pathway_scores[,1]) # put into correct format
-write.table(scores,paste0(folder,i,".txt"),col.names=F,row.names=F,quote=F,sep='\t') # save
-}
-stopCluster(myCluster)
-# Script to prepare PROGENy pathway scores
-# Input: Matrix of gene expression data (Rows are genes (Entrez), columns are compounds), and
-#        The .txt file for each signature (From prepare_input_parellel.R)
-#         And a measurement folder for each compound (From prepare_input_parellel.R)
-# Output: PROGEny pathway weights .txt in each compound's measurement folder
-# Import packages
-library(CARNIVAL)
-library(org.Hs.eg.db)
-library(foreach)
-library(doParallel)
-library(plyr)
-library(data.table)
-# set n to number of cores
-myCluster <- makeCluster(n, type="FORK",outfile="")
-registerDoParallel(myCluster)
-# Load files for progeny
-file.copy(from=system.file("model_NatComm+14_human.csv",package="CARNIVAL"),to=getwd(),overwrite=TRUE)
-weight_matrix<-read.csv("model_NatComm+14_human.csv")
-# Import matrix
-gexfile = "HT29_6h_10uM.csv"
-gex_df = fread(gexfile,header=TRUE,sep=",") # First row is python header
-gex_df = as.data.frame(gex_df) # As df
-compound_names = names(gex_df) # Column names are compounds
-compound_names = compound_names[!compound_names %in% 'Compound_id'] # Get rid of 'Compound_id'
-# test for e.g. 10 compounds
-compound_names = compound_names[1:10]
-output <-
-foreach(i = compound_names) %dopar% {  # loop over each compound
-print(i)
-fname <- paste0(i, ".txt") # get signature .txt
-df = read.table(fname,sep="\t",header=TRUE,row.names=1) # Read back in file
-df_genenames <- data.frame('gene'=rownames(df),df) # make df with rownames = gene symbols
-#Run progeny
-pathway_scores <- try(
-runPROGENy(df_genenames,weight_matrix,z_scores=F),
-silent = T)
-if(inherits(pathway_scores,"try-error")){ # if it fails then skip
-next
-}
-#Generate input files
-folder = paste0(i,"_measurements/scores_") # get folder name
-scores <- rbind(rownames(pathway_scores),pathway_scores[,1]) # put into correct format
-write.table(scores,paste0(folder,i,".txt"),col.names=F,row.names=F,quote=F,sep='\t') # save
-}
-stopCluster(myCluster)
-# Script to prepare PROGENy pathway scores
-# Input: Matrix of gene expression data (Rows are genes (Entrez), columns are compounds), and
-#        The .txt file for each signature (From prepare_input_parellel.R)
-#         And a measurement folder for each compound (From prepare_input_parellel.R)
-# Output: PROGEny pathway weights .txt in each compound's measurement folder
-# Import packages
-library(CARNIVAL)
-library(org.Hs.eg.db)
-library(foreach)
-library(doParallel)
-library(plyr)
-library(data.table)
-# set n to number of cores
-n = 2
-myCluster <- makeCluster(n, type="FORK",outfile="")
-registerDoParallel(myCluster)
-# Load files for progeny
-file.copy(from=system.file("model_NatComm+14_human.csv",package="CARNIVAL"),to=getwd(),overwrite=TRUE)
-weight_matrix<-read.csv("model_NatComm+14_human.csv")
-# Import matrix
-gexfile = "HT29_6h_10uM.csv"
-gex_df = fread(gexfile,header=TRUE,sep=",") # First row is python header
-gex_df = as.data.frame(gex_df) # As df
-compound_names = names(gex_df) # Column names are compounds
-compound_names = compound_names[!compound_names %in% 'Compound_id'] # Get rid of 'Compound_id'
-# test for e.g. 10 compounds
-compound_names = compound_names[1:10]
-output <-
-foreach(i = compound_names) %dopar% {  # loop over each compound
-print(i)
-fname <- paste0(i, ".txt") # get signature .txt
-df = read.table(fname,sep="\t",header=TRUE,row.names=1) # Read back in file
-df_genenames <- data.frame('gene'=rownames(df),df) # make df with rownames = gene symbols
-#Run progeny
-pathway_scores <- try(
-runPROGENy(df_genenames,weight_matrix,z_scores=F),
-silent = T)
-if(inherits(pathway_scores,"try-error")){ # if it fails then skip
-next
-}
-#Generate input files
-folder = paste0(i,"_measurements/scores_") # get folder name
-scores <- rbind(rownames(pathway_scores),pathway_scores[,1]) # put into correct format
-write.table(scores,paste0(folder,i,".txt"),col.names=F,row.names=F,quote=F,sep='\t') # save
-}
-stopCluster(myCluster)
-# Create output dir
-dir.create(file.path("RESULTS_CARNIVAL"),showWarnings = FALSE)
-compound_folders = list.dirs(recursive=FALSE)
-compound_dirs
-compound_folders
-# get ones that have already finished and exclude (checkpointing)
-done_folders = list.dirs(path="RESULTS_CARNIVAL",full.names=FALSE,recursive=FALSE)
-info = file.info(list.dirs(path="RESULTS_CARNIVAL",recursive=FALSE))
-info = info[with(info, order(as.POSIXct(ctime))),]
-donecomps = rownames(info)
-exclude = tail(donecomps,n=1)
-exclude = unlist(strsplit(exclude,"RESULTS_CARNIVAL/"))[2]
-done_final = done_folders[!done_folders %in% exclude]
-compound_folders
-compound = compound_folders[1]
-compound
-drug = unlist(strsplit(unlist(strsplit(compound,"/"))[2],"_"))[1] # get the compound name
-drug
-#dir.create(file.path(paste0("RESULTS_CARNIVAL/",drug)),showWarnings = FALSE)
-results_dir = paste0("RESULTS_CARNIVAL/",drug)
-results_dir
-#results_dir = "RESULTS_CARNIVAL/"
-tf_activities = list.files(path=compound,pattern="_50.txt",full.names=TRUE)
-progeny_pathways = list.files(path=compound,pattern="scores_",full.names=TRUE)
-tf_activities
-dir.create(file.path(paste0("RESULTS_CARNIVAL/",drug)),showWarnings = FALSE)
-# load tf + progeny
-tf_activities = list.files(path=compound,pattern="_50.txt",full.names=TRUE)
-progeny_pathways = list.files(path=compound,pattern="scores_",full.names=TRUE)
-progeny_pathways
-R.version.string
-# Script to install packages required
-install_required <- function(x){
-for( i in x ){
-#  require returns TRUE invisibly if it was able to load package
-if( ! require( i , character.only = TRUE ) ){
-#  If package was not able to be loaded then re-install
-install.packages( i , dependencies = TRUE )
-#  Load package after installing
-require( i , character.only = TRUE )
-}
-}
-}
-install_required(c(shiny,shinyjs))
-install_required(c("shiny","shinyjs"))
-install_required(c("shiny","shinyjs","igraph","DT","miniUI","shinysky","shinyalert"))
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-library(shiny)
-library(shinyjs)
-library(igraph)
-library(DT)
-library(miniUI)
-library(chemdoodle)
-library(rhandsontable)
-library(shinysky)
-library(shinyBS)
-library(shinythemes)
-library(shinyFiles)
-library(org.Hs.eg.db)
-library(dorothea)
-library(dplyr)
-library(tibble)
-library(ggplot2)
-library(progeny)
-library(CARNIVAL)
-library(visNetwork)
-library(piano)
-library(HGNChelper)
-library(shinyalert)
-library(shinyWidgets)
-shiny::runApp('OneDrive - University Of Cambridge/MoA_Tool')
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-shiny::runApp('OneDrive - University Of Cambridge/MoA_Tool')
-install.packages("sortable")
-runApp('OneDrive - University Of Cambridge/MoA_Tool')
-# Version info: R 3.2.3, Biobase 2.30.0, GEOquery 2.40.0, limma 3.26.8
-################################################################
-#   Differential expression analysis with limma
-library(GEOquery)
-BiocManager::install("GEOQuery")
-BiocManager::install("GEOquery")
-# Version info: R 3.2.3, Biobase 2.30.0, GEOquery 2.40.0, limma 3.26.8
-################################################################
-#   Differential expression analysis with limma
-library(GEOquery)
-library(limma)
-library(umap)
-gset <- getGEO("GSE129254", GSEMatrix =TRUE, AnnotGPL=TRUE)
-if (length(gset) > 1) idx <- grep("GPL10558", attr(gset, "names")) else idx <- 1
-gset <- gset[[idx]]
-# make proper column names to match toptable
-fvarLabels(gset) <- make.names(fvarLabels(gset))
-# group membership for all samples
-gsms <- "XXXXXXXXX000111XXX"
-sml <- strsplit(gsms, split="")[[1]]
-# filter out excluded samples (marked as "X")
-sel <- which(sml != "X")
-sml <- sml[sel]
-gset <- gset[ ,sel]
-# log2 transformation
-ex <- exprs(gset)
-qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
-LogC <- (qx[5] > 100) ||
-(qx[6]-qx[1] > 50 && qx[2] > 0)
-if (LogC) { ex[which(ex <= 0)] <- NaN
-exprs(gset) <- log2(ex) }
-# assign samples to groups and set up design matrix
-gs <- factor(sml)
-groups <- make.names(c("Control","Treatment"))
-levels(gs) <- groups
-gset$group <- gs
-design <- model.matrix(~group + 0, gset)
-colnames(design) <- levels(gs)
-fit <- lmFit(gset, design)  # fit linear model
-# set up contrasts of interest and recalculate model coefficients
-cts <- paste(groups[1], groups[2], sep="-")
-cont.matrix <- makeContrasts(contrasts=cts, levels=design)
-fit2 <- contrasts.fit(fit, cont.matrix)
-# compute statistics and table of top significant genes
-fit2 <- eBayes(fit2, 0.01)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=250)
-?topTable
-fit2
-design
-# group membership for all samples
-gsms <- "XXXXXXXXX111000XXX"
-sml <- strsplit(gsms, split="")[[1]]
-# filter out excluded samples (marked as "X")
-sel <- which(sml != "X")
-sml <- sml[sel]
-gset <- gset[ ,sel]
-gset <- getGEO("GSE129254", GSEMatrix =TRUE, AnnotGPL=TRUE)
-if (length(gset) > 1) idx <- grep("GPL10558", attr(gset, "names")) else idx <- 1
-gset <- gset[[idx]]
-# make proper column names to match toptable
-fvarLabels(gset) <- make.names(fvarLabels(gset))
-# group membership for all samples
-gsms <- "XXXXXXXXX111000XXX"
-sml <- strsplit(gsms, split="")[[1]]
-# filter out excluded samples (marked as "X")
-sel <- which(sml != "X")
-sml <- sml[sel]
-gset <- gset[ ,sel]
-# log2 transformation
-ex <- exprs(gset)
-qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
-LogC <- (qx[5] > 100) ||
-(qx[6]-qx[1] > 50 && qx[2] > 0)
-if (LogC) { ex[which(ex <= 0)] <- NaN
-exprs(gset) <- log2(ex) }
-# assign samples to groups and set up design matrix
-gs <- factor(sml)
-groups <- make.names(c("Treatment","Control"))
-levels(gs) <- groups
-gset$group <- gs
-design <- model.matrix(~group + 0, gset)
-colnames(design) <- levels(gs)
-fit <- lmFit(gset, design)  # fit linear model
-design
-fit
-design
-# set up contrasts of interest and recalculate model coefficients
-cts <- paste(groups[1], groups[2], sep="-")
-cont.matrix <- makeContrasts(contrasts=cts, levels=design)
-fit2 <- contrasts.fit(fit, cont.matrix)
-# compute statistics and table of top significant genes
-fit2 <- eBayes(fit2, 0.01)
-fit2
-47318+5
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=47323)
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))
-View(tT)
-View(tT)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))
-tT2 <- tT %>%
-dplyr::select(Gene.symbol, t) %>%
-na.omit() %>%
-distinct() %>%
-group_by(Gene.symbol) %>%
-summarize(t=mean(t))
-R
-version()
-version
-sessionInfo(0)
-sessionInfo()
-library(dplyr)
-sessionInfo()
-# Version info: R 4.0.3, GEOquery 2.58.0, limma 3.46.0, dplyr 1.0.6
-# R code from GEO2R
-################################################################
-#   Differential expression analysis with limma
-library(GEOquery)
-library(limma)
-library(dplyr)
-# load series and platform data from GEO
-gset <- getGEO("GSE129254", GSEMatrix =TRUE, AnnotGPL=TRUE)
-if (length(gset) > 1) idx <- grep("GPL10558", attr(gset, "names")) else idx <- 1
-gset <- gset[[idx]]
-# make proper column names to match toptable
-fvarLabels(gset) <- make.names(fvarLabels(gset))
-# group membership for all samples
-gsms <- "XXXXXXXXX111000XXX"
-sml <- strsplit(gsms, split="")[[1]]
-# filter out excluded samples (marked as "X")
-sel <- which(sml != "X")
-sml <- sml[sel]
-gset <- gset[ ,sel]
-# log2 transformation
-ex <- exprs(gset)
-qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
-LogC <- (qx[5] > 100) ||
-(qx[6]-qx[1] > 50 && qx[2] > 0)
-if (LogC) { ex[which(ex <= 0)] <- NaN
-exprs(gset) <- log2(ex) }
-# assign samples to groups and set up design matrix
-gs <- factor(sml)
-groups <- make.names(c("Treatment","Control"))
-levels(gs) <- groups
-gset$group <- gs
-design <- model.matrix(~group + 0, gset)
-colnames(design) <- levels(gs)
-fit <- lmFit(gset, design)  # fit linear model
-# set up contrasts of interest and recalculate model coefficients
-cts <- paste(groups[1], groups[2], sep="-")
-cont.matrix <- makeContrasts(contrasts=cts, levels=design)
-fit2 <- contrasts.fit(fit, cont.matrix)
-# compute statistics and table of top significant genes
-fit2 <- eBayes(fit2, 0.01)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
-#
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))
-tT2 <- tT %>%
-dplyr::select(Gene.symbol, t) %>%
-na.omit() %>%
-distinct() %>%
-group_by(Gene.symbol) %>%
-summarize(t=mean(t))
-#write.table(tT, file=, row.names=F, sep="\t")
-setwd("~/OneDrive - University Of Cambridge/MoA_Tool/lapatinib")
-View(tT2)
-View(tT)
-?eBayes
-# get rid of empty gene symbol rows
-tT = tT[complete.cases(tT),]
-View(tT)
-# compute statistics and table of top significant genes
-fit2 <- eBayes(fit2, 0.01)
-tT <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)
-tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))
-# get rid of empty gene symbol rows, get mean t-stat per gene symbol
-tT[tT==""] <- NA
-tT2 <- tT %>%
-dplyr::select(Gene.symbol, t) %>%
-na.omit() %>%
-distinct() %>%
-group_by(Gene.symbol) %>%
-summarize(t=mean(t))
-View(tT2)
-# save tt
-write.table(tT2, file="GSE129254_lapatinib_BT474", row.names=F, sep="\t")
-# save tt
-write.table(tT2, file="GSE129254_lapatinib_BT474.txt", row.names=F, sep="\t")
-# save tt
-write.table(tT2, file="GSE129254_lapatinib_BT474.txt", row.names=F, sep="\t",quote=F)