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IMC-ROI-annotation_v0.1.R

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+# IMC ROI annotation script v0.1
+    # Thomas Ashhurst
+    # 2019-07-03
+    # https://sydneycytometry.org.au
+
+
+############################## START USER INPUT ##############################
+
+### 1. Installing packages, loading packages, setting the working directory
+
+    ## Install packages if required
+        if(!require('flowCore')) {install.packages('flowCore')}
+        if(!require('Biobase')) {install.packages('Biobase')}
+        if(!require('data.table')) {install.packages('data.table')}
+
+    ## Load packages
+        library('flowCore')
+        library('Biobase')
+        library('data.table')
+
+    ## Create Y-axis inversion function
+        all.neg <- function(test) -1*abs(test)
+
+    ## In order for this to work, a) rstudioapi must be installed and b) the location of this .r script must be in your desired working directory
+        dirname(rstudioapi::getActiveDocumentContext()$path)            # Finds the directory where this script is located
+        setwd(dirname(rstudioapi::getActiveDocumentContext()$path))     # Sets the working directory to where the script is located
+        getwd()
+        PrimaryDirectory <- getwd()
+        PrimaryDirectory
+
+    ## Use this to manually set the working directory
+        #setwd("/Users/Tom/Desktop/Experiment")                          # Set your working directory here (e.g. "/Users/Tom/Desktop/") -- press tab when selected after the '/' to see options
+        #getwd()                                                         # Check your working directory has changed correctly
+        #PrimaryDirectory <- getwd()                                     # Assign the working directory as 'PrimaryDirectory'
+        #PrimaryDirectory
+
+### 2. Input data
+
+    ## Use to list the .csv files in the working directory -- important, the only CSV files in the directory should be the one desired for analysis. If more than one are found, only the first file will be used
+        FileNames <- list.files(path=PrimaryDirectory, pattern = ".csv")     # see a list of CSV files
+        as.matrix(FileNames) # See file names in a list
+
+    ## Read data from Files into list of data frames
+        DataList=list() # Creates and empty list to start 
+
+        for (File in FileNames) { # Loop to read files into the list
+          tempdata <- fread(File, check.names = FALSE)
+          File <- gsub(".csv", "", File)
+          DataList[[File]] <- tempdata
+        }
+
+        rm(tempdata)
+        AllSampleNames <- names(DataList)
+
+    ## Chech data quality
+       head(DataList)
+
+
+    ## Review column names in dataset 
+        colNam <- list() # creates an empty list
+        for (i in c(1:length(DataList))){colNam[[i]] <- colnames(DataList[[1]])} # creates a table of all column names
+        colNam <- data.frame(matrix(unlist(colNam), nrow=length(colNam), byrow=T)) # condenses table
+
+        # Review column names
+        colNam
+
+        #################################################################################################
+        ### It is important that all column names in each file are consistent for this script to work ###
+        #################################################################################################
+
+
+    ## What kind of files would you like to generate
+        do.csv <- 1      # CSV files, yes = 1, no = 0
+        do.fcs <- 1      # FCS files, yes = 1, no = 0
+
+
+### 3. Invert Y-axis -- do you wish to create an inverted Y-axis -- coverts all the values to negative values, to correct the direction of the Y-axis
+
+    ## Examine column names
+        as.matrix(names(DataList[[1]]))
+
+    ## Specify a demo sample
+        num <- 1
+
+    ## Specify the parameter that denotes the X-axis and Y-axis position
+        xaxis <- "X_position"
+        yaxis <- "Y_position"
+
+    ## Plot X vs y for one sample
+        demo.invert <- DataList[[num]]
+        plot(demo.invert[[xaxis]], demo.invert[[yaxis]])
+
+    ## Invert y-xais in test image
+        d <- demo.invert[[yaxis]]
+        d.res <- all.neg(d)
+        demo.invert[["y-axis-invert"]] <- d.res
+
+    ## Compare results
+        plot(demo.invert[[xaxis]], demo.invert[[yaxis]])              # View uninverted data
+        plot(demo.invert[[xaxis]], demo.invert[["y-axis-invert"]])    # View inverted data
+
+    ## Do you wish to create an inverted version of the Y-aix (yes = 1, no = 0). This will no replace the y-axis values, but rather add a new column.
+        do.invert <- 1
+
+
+### 4. Investigate arcsinh transformation for data (if desired)
+
+    ## Test a transform value on a demo parameter
+        as.matrix(colNam)
+
+        test.sampleNum <- 3    # Choose a sample for demonstration
+        test.colNum1   <- 8    # Choose the first column for a demonstration transformation
+        test.colNum2   <- 25   # Choose the second column for a demonstration transformation
+
+    ## Create transformed versions of selected parameters
+        transf.test <- data.frame(DataList[[test.sampleNum]][[test.colNum1]], DataList[[test.sampleNum]][[test.colNum2]])
+        names(transf.test) <- c("One","Two")
+        transf.test["One.transf"] <- transf.test["One"]
+        transf.test["Two.transf"] <- transf.test["Two"]
+
+    ## Plot the untransferred parameters
+        plot(transf.test$One, transf.test$Two)
+
+    ## Set and apply an demonstration arcsinh scale value (recommended 0.5 - 2)
+        test.asinh.scale <- 2
+
+        transf.test["One.transf"] <- asinh(transf.test[, "One"] / test.asinh.scale)
+        transf.test["Two.transf"] <- asinh(transf.test[, "Two"] / test.asinh.scale)
+
+    ## Plot untransformed and transformed data
+        plot(transf.test$One, transf.test$Two)                    # Untransformed
+        plot(transf.test$One.transf, transf.test$Two.transf)      # Transformed
+
+        plot(transf.test$One, transf.test$One.transf)             # Untransformed vs transformed
+
+    ## Choose columns to be transformed
+        col.names.dl <- names(DataList[[1]])    # show data with headings
+        as.matrix(col.names.dl)                 # view the column 'number' for each parameter
+
+        col.nos.scale <- c(3:50)                # specify column numbers to be transformed - e.g. c(11, 23, 10)] 
+
+        col.names.dl[col.nos.scale]             # Columns to transform
+        col.names.dl[-col.nos.scale]            # Columns NOT to transform
+
+    ## Choose the co-factor for arcsinh tranformation (recommended = 1)
+        do.transform <- 1   
+        asinh.scale <- 1
+
+
+############################## END USER INPUT ##############################
+
+### Create new output directory 
+
+      x <- Sys.time()
+      x <- gsub(":", "-", x)
+      x <- gsub(" ", "_", x)
+
+      newdir <- paste0("Output_IMC-ROI", "_", "transf=", do.transform, "_", "cf=", asinh.scale ,"yaxis-invert=", do.invert, "_", x)
+
+      setwd(PrimaryDirectory)
+      dir.create(paste0(newdir), showWarnings = FALSE)
+      setwd(newdir)
+
+### Sample loop
+
+    for(i in c(1:length(AllSampleNames))){
+      data_subset <- DataList[i]
+      data_subset <- rbindlist(as.list(data_subset))
+      data_subset <- as.data.frame(data_subset)
+      dim(data_subset)
+      a <- names(DataList)[i]
+
+      ## Invert Y-axis
+      if(do.invert == 1){
+        invert <- data_subset[[yaxis]]
+        invert.res <- all.neg(invert)
+        data_subset[["y-axis-invert"]] <- invert.res
+      }
+
+      ## Perform transform (if selected in preferences, otherwise transformation will not run)
+      if(do.transform == 1){
+
+        col.names.dl <- names(data_subset)
+        col.names.SCALE <- col.names.dl[col.nos.scale]
+        data_subset[, col.names.SCALE] <- asinh(data_subset[, col.names.SCALE] / asinh.scale)
+        head(data_subset)
+        summary(data_subset)
+      }
+
+      if(do.csv == 1){
+        fwrite(x = data_subset, file = paste0(a, ".csv"), row.names=FALSE)
+      }
+
+      if(do.fcs == 1){
+        ## Metadata
+        metadata <- data.frame(name=dimnames(data_subset)[[2]],desc=paste('column',dimnames(data_subset)[[2]]))
+
+        ## Create FCS file metadata - ranges, min, and max settings
+        #metadata$range <- apply(apply(data_subset,2,range),2,diff)
+        metadata$minRange <- apply(data_subset,2,min)
+        metadata$maxRange <- apply(data_subset,2,max)
+
+        data_subset.ff <- new("flowFrame",exprs=as.matrix(data_subset), parameters=AnnotatedDataFrame(metadata)) # in order to create a flow frame, data needs to be read as matrix by exprs
+        head(data_subset.ff)
+        write.FCS(data_subset.ff, paste0(a, ".fcs"))
+      }
+
+    }
+
+