tags: QuPath Fiji imageJ R

Immunohistochemistry image processing and analysis

• Author: Lun-Hsien Chang
• Date created: May 2020

---

Using the R function in next section to annotate all IHC images

• Download the cell segmentation data file cell-seg-data-merged_CD8.txt from QIMR L drive L:/Lab_MarkS/lunC/work/Immunohistochemistry_images/data_output/AP_Exp108.1_PeterMac-lungCancer-CD8-PD1/analysis-results
• Download the folder with IHC image files from QIMR L drive L:/Lab_MarkS/lunC/work/Immunohistochemistry_images/data_input/AshR/1. mIHF/Exp 108 210728/CD8 path
• Create a new R script file. Copy the code chunk below and modify the path of input files and folders.
• Run the source() function to load the R function to the working environment.
  • Modify the file path within the double quotes. Make sure backword slashes \ are replaced by forward slashes / when the path is copied from Windows address bar.
  • In R, place your cursor anywhere in the line with the source() and run this function by pressing the Ctrl and Enter keys at the same time.
• Modify the values that will be taken by the following arguments in the function DrawAllIHCImagesTMAWithRectanglesInForm()
  • input.cell.seg.data.file.path= This is the full path of your cell segmentation file. Make sure the string value is within a pair of quotes.
  • input.images.folder.path= This is the full path of the folder with IHC image files to annotate. Do not end the path with / .
  • threshold.marker= Specify a threshold for positive marker measurement with a number.
  • threshold.confidence= Specify a threshold for confidence with a number.
  • rectangle.width.half= Specify 50% of the width of a square. This value is used to calculate the coordinates of the four points. rectangle.width.half=15 creates squares that are 30 by 30 pixels.
  • legend.location= Specify which corner you want your legend to be. Aceept four string values- "topright" "topleft" "bottomright" "bottomleft"
  • rectangle.color= Name the color of your rectangles. To use a different color from the default black, look up a color name in R An overview of color names in R
  • legend.text.line1= legend.text.line2= legend.text.line3= Allow a 3-line legned to be placed using the text you supply. Use line 1 for your project/study name. Use line 2 for your marker. Use line 3 for the thresholds.
  • legend.color= Name the color of the legend. To use a different color from the default black, look up a color name in R An overview of color names in R
  • legend.text.cex= Use a number to increase or decrease the font size of the legend. legend.text.cex=2.5 expands the size to 250%
  • output.folder.path= Create a new folder where the annotated image files will be exported. Add the folder path to the argument. Do not end the path with /
  • output.excluded.images.folder.path= Create another new folder where the unannotated image files will be exported. Add the folder path to the argument. Do not end the path with /

source("E:/Lab_MarkS/lunC_work/Immunohistochemistry_images/scripts/RFunction_annotate-all-IHC-images.R")

# Annotate CD8+
DrawAllIHCImagesTMAWithRectanglesInForm(input.cell.seg.data.file.path="E:/Lab_MarkS/lunC_work/Immunohistochemistry_images/data_output/AP_Exp108.1_PeterMac-lungCancer-CD8-PD1/analysis-results/cell-seg-data-merged_CD8.txt"
                                        ,input.images.folder.path="E:/Lab_MarkS/lunC_work/Immunohistochemistry_images/data_input/AshR/1. mIHF/Exp 108 210728/CD8 path"
                                        ,threshold.marker=5
                                        ,threshold.confidence=95
                                        ,rectangle.width.half=12.5
                                        ,legend.location="topright"
                                        ,rectangle.color="black"
                                        ,legend.text.line1="Project: Exp108"
                                        ,legend.text.line2="Marker: CD8+"
                                        ,legend.text.line3="Marker threshold: 5, confidence threshold: 95%"
                                        ,legend.color="black"
                                        ,legend.text.cex=2.5                                     ,output.folder.path="E:/Lab_MarkS/lunC_work/Immunohistochemistry_images/data_output/AP_Exp108.1_PeterMac-lungCancer-CD8-PD1/CD8_path_view"
                                        ,output.excluded.images.folder.path="E:/Lab_MarkS/lunC_work/Immunohistochemistry_images/data_output/AP_Exp108.1_PeterMac-lungCancer-CD8-PD1/CD8_path_view/excluded")

• Download R script file annotate-IHC-images_Exp108v1.R with the code above from QIMR L drive L:/Lab_MarkS/lunC/work/Immunohistochemistry_images/scripts

---

A R function to annotate IHC images using a cell segmentation data file

• Download the R script file RFunction_annotate-all-IHC-images.R from QIMR network drive L L:/Lab_MarkS/lunC/work/Immunohistochemistry_images/scripts
• The input cell segmentation data file should be
  • A tab-separated file
  • Having at least five columns with headers in this order. The function identifies the columns by their positions, not by their names
    • column 1 : image file names
    • column 2 : X coordinates
    • column 3 : Y coordinates
    • column 4 : Marker measurement
    • column 5 : Confidence
• The input image files can be in different formats such as .tif, .tiff, .jpg, .jpeg, and .png (not tested yet) *

# Check if required packages are already installed
if (!"dplyr" %in% rownames(installed.packages())) install.packages("dplyr")
if (!"foreach" %in% rownames(installed.packages())) install.packages("foreach")
if (!"doParallel" %in% rownames(installed.packages())) install.packages("doParallel")
if (!"magick" %in% rownames(installed.packages())) install.packages("magick")
if (!"graphics" %in% rownames(installed.packages())) install.packages("graphics")
if (!"grDevices" %in% rownames(installed.packages())) install.packages("grDevices")
if (!"tools" %in% rownames(installed.packages())) install.packages("tools")

# Load packages
library(dplyr) # to use %>% operator 
library(doParallel) # to use %dopar% operator

DrawAllIHCImagesTMAWithRectanglesInForm<- function( input.cell.seg.data.file.path="D:/googleDrive/copy-to-lunC_work/Merged_data/190529 batch analysis_lung cancer_cell_seg_data.txt"
                                                    ,column.position.image.file.name=1
                                                    ,column.position.x.cooridnate=2
                                                    ,column.position.y.cooridnate=3
                                                    ,column.position.marker.measurement=4
                                                    ,column.position.confidence=5
                                                    ,input.images.folder.path
                                                    ,threshold.marker=8
                                                    ,threshold.confidence=0
                                                    ,rectangle.width.half=12.5
                                                    ,legend.location="topright"
                                                    ,rectangle.color="black"
                                                    ,legend.text.line1="Project: demo"
                                                    ,legend.text.line2="Marker: CD8+"
                                                    ,legend.text.line3="Marker threshold: 8, confidence threshold: 0"
                                                    ,legend.color="black"
                                                    ,legend.text.cex=2.5
                                                    ,output.folder.path="D:/googleDrive/copy-to-lunC_work/CD8_annotated"
                                                    ,output.excluded.images.folder.path="E:/Lab_MarkS/lunC_work/Immunohistochemistry_images/data_output/AP_Exp108.1_PeterMac-lungCancer-CD8-PD1/CD8_path_view/excluded"){
  # Check file existence
  if(file.exists(input.cell.seg.data.file.path)!=TRUE){
    cat("Input file for cell segmentation data could not be found")
  } else {
    
    # Import cell segmentation data file
    data <- read.delim(file=input.cell.seg.data.file.path, header = TRUE, sep = "\t", stringsAsFactors = F) %>%
      dplyr::select(c( column.position.image.file.name
                       ,column.position.x.cooridnate
                       ,column.position.y.cooridnate
                       ,column.position.marker.measurement
                       ,column.position.confidence)) %>%
      dplyr::mutate(
        Confidence_percent= as.numeric(stringr::str_replace_all(string=.[,5]
                                                                ,pattern="%"
                                                                ,replacement=""))) # dim(data) 2110587 6
    
    data.subset <- data %>% dplyr::filter(data[,4] > threshold.marker & data[,5] > threshold.confidence) # dim(data.subset)  52173 6
    cat((nrow(data)-nrow(data.subset))/nrow(data)*100, "% data are filtered out by the marker threshold of ", threshold.marker, "and confidence threshold of", threshold.confidence)
  }
  
  if(dir.exists(input.images.folder.path)!=TRUE){
    cat("Input image file could not be found")
  } else{
    # Read input image file paths as a vector
    image.file.paths <- list.files(path = input.images.folder.path, full.names = TRUE) # length(image.file.paths) 104
    cat("There are",length(image.file.paths),"images in the folder")
    
    # Use image file name to look up data for the image in the cell segmentation data
    foreach::foreach(i=1:length(image.file.paths)
                     ,.combine = 'c'
                     ,.packages=c("foreach","dplyr")) %dopar% {
                       print(paste0("=========== Working on image ",i,"============"))
                       
                       # If data can be found, then subset. If data cannot be found, then copy the input image to the output
                       image.file.path <-image.file.paths[i]
                       image.file.name <-basename(image.file.path)
                       
                       # Use image file name to subset data. If no data can be found, copy the image file to the excluded folder
                       d <- data.subset %>% dplyr::filter(file.name==image.file.name) # dim(d) 99 6 # nrow(d)
                       if(nrow(d)==0){
                         file.copy( from = image.file.path
                                    ,to=file.path(output.excluded.images.folder.path, image.file.name)
                                    ,copy.date = TRUE
                         )
                       } else{
                         # Handle errors when the pattern fails to find the matched image file
                         # Calculate coordinates for four points of rectangles
                         tryCatch({
                           # Read input image as a magick-image object
                           image <- magick::image_read(path=image.file.path) # class(image) "magick-image"
                           
                           # Draw the imported image. 
                           image.drew <- magick::image_draw(image)
                           
                           # Create pixel coordinates for rectangles
                           rect.x.left <- d[,2] - rectangle.width.half # length(rect.x.left) 556
                           rect.x.right <- d[,2] + rectangle.width.half # length(rect.x.right) 556
                           rect.y.top <- d[,3] - rectangle.width.half # length(rect.y.top) 556
                           rect.y.bottom <- d[,3] + rectangle.width.half # length(rect.y.bottom) 556
                           
                           graphics::legend(legend.location #"topleft"
                                            ,legend = c(  legend.text.line1
                                                          ,legend.text.line2
                                                          ,stringr::str_wrap(string=legend.text.line3, width=80)
                                                          ,paste0(rectangle.width.half*2, " x ", rectangle.width.half*2, " pixels")
                                            )
                                            ,bty = "n"
                                            ,pt.cex = legend.text.cex
                                            ,cex = legend.text.cex
                                            ,text.col = c(legend.color))
                           
                           
                           ## Draw rectangles for individual stained cells
                           graphics::rect( xleft= rect.x.left
                                           ,ybottom=rect.y.bottom
                                           ,xright=rect.x.right
                                           ,ytop=rect.y.top
                                           ,col = NA # color(s) to fill or shade the rectangle(s) with. The default NA (or also NULL) means do not fill, i.e., draw transparent rectangles, unless density is specified.
                                           ,border = rectangle.color # color for rectangle border(s).
                                           ,lty = par("lty")
                                           ,lwd = 2)
                           
                           # Close the image device
                           grDevices::dev.off()
                           #--------------------------
                           # Export the painted image 
                           #--------------------------
                           # Make output file name
                           output.image.file.name <- paste("annotated", basename(image.file.path)
                                                           ,sep = "_") # ,TMA.core.ID
                           
                           # Make output file path without file extension
                           output.image.file.path <- file.path(output.folder.path, output.image.file.name)
                           
                           # Make output file name without file extension
                           output.image.file.name.ext.rm <- tools::file_path_sans_ext(output.image.file.name)
                           
                           if(nchar(output.image.file.path) < 260){
                             magick::image_write( image= image.drew
                                                  ,path= output.image.file.path)
                             
                           } else {
                             # Truncate file name to 259 character long from the end
                             cut.length <- nchar(output.image.file.path) - 259 # 4
                             end.position <- nchar(output.image.file.name.ext.rm) -  cut.length
                             
                             output.image.file.path.shortened <- file.path(output.folder.path
                                                                           ,paste0( stringr::str_sub( string= tools::file_path_sans_ext(output.image.file.name)
                                                                                                      ,start = 1
                                                                                                      ,end= end.position)
                                                                                    # Append file extension to shortened file name
                                                                                    ,"."
                                                                                    ,tools::file_ext(output.image.file.name)
                                                                           )
                             ) 
                             magick::image_write( image= image.drew
                                                  ,path= output.image.file.path.shortened)
                           } 
                         }, error=function(e){cat("ERROR :",conditionMessage(e), "\n")} 
                         ) # End tryCatch() function
                       }
                     } # End %dopar%
  } # End else
} # End the main function

---

Using the R function in next section to summarise cell segmentation data to counts of single, double positive cells

• Download the cell segmentation data file AP_Exp108.1_PeterMac-lungCancer-CD8-PD1_cell-seg-data-merged_CD8_PD1.tsv from QIMR L drive L:/Lab_MarkS/lunC/work/Immunohistochemistry_images/data_output/AP_Exp108.1_PeterMac-lungCancer-CD8-PD1/analysis-results
• Create a new R script file. Copy the code chunk and modify the paths of input and output files and folders
• Run the source() function to load the R function to the working environment.
  • Modify the file path within the double quotes. Make sure backword slashes \ are replaced by forward slashes / when the path is copied from Windows address bar.
  • In R, place your cursor anywhere in the line with the source() and run this function by pressing the Ctrl and Enter keys at the same time.
• Modify the values that will be taken by the following arguments in the function SummariseTwoMarkersCellSegmentationDataInForm()
  • file.path.cell.seg.data.file= This is the full path of your cell segmentation file. Make sure the string value is within a pair of quotes.
  • input.images.folder.path= This is the full path of the folder with IHC image files to annotate. Do not end the path with / .
  • marker.1.info.list= Specify a list of three values for marker 1 (1) Name your marker 1 (e.g., CD8), (2) a threshold that dichotimises marker measurement values into negative or positive, (3) a threshold that dichotimises confidence.
  • marker.2.info.list= Specify a list of three values for marker 2 (1) Name your marker 1 (e.g., CD226), (2) a threshold that dichotimises marker measurement values into negative or positive, (3) a threshold that dichotimises confidence.
  • output.folder.path= Create a new folder where the summary data will be exported. Add the folder path to the argument. Do not end the path with /

source("E:/Lab_MarkS/lunC_work/Immunohistochemistry_images/scripts/RFunction_summarise-cell-segmentation-files.R")
SummariseTwoMarkersCellSegmentationDataInForm( file.path.cell.seg.data.file="E:/Lab_MarkS/lunC_work/Immunohistochemistry_images/data_output/AP_Exp108.1_PeterMac-lungCancer-CD8-PD1/analysis-results/AP_Exp108.1_PeterMac-lungCancer-CD8-PD1_cell-seg-data-merged_CD8_PD1.tsv"
                                               ,marker.1.info.list=list(c("CD8", 1.8, 98))
                                               ,marker.2.info.list=list(c("CD226", 3, 98))                                               ,output.folder.path="E:/Lab_MarkS/lunC_work/Immunohistochemistry_images/data_output/AP_Exp109.1_PeterMac-lungCancer-CD8-CD226/analysis-results")

• The R code above is in the R script file annotate-IHC-images_Exp108v1.R at QIMR L drive L:/Lab_MarkS/lunC/work/Immunohistochemistry_images/scripts

---

A R function to summarise cell segmentation data to counts of single, double positive cells

• Location of the script file on QIMR network drive L L:/Lab_MarkS/lunC/work/Immunohistochemistry_images/scripts/RFunction_summarise-cell-segmentation-files.R
• The input cell segmentation data file should be
  • A tab-separated file
  • Having at least 4 columns with headers in this order. The function identifies the columns by their positions, not by their names
    • column 1 : A grouping variable that uniquely identifies TMA core location
    • column 2 : Confidence variable with string values (e.g., 95%)
    • column 3 : measurement of marker 1
    • column 4 : measurement of marker 2

# Check if required packages are already installed
if (!"dplyr" %in% rownames(installed.packages())) install.packages("dplyr")
if (!"tools" %in% rownames(installed.packages())) install.packages("tools")

# Load packages
library(dplyr) # to use %>% operator

SummariseTwoMarkersCellSegmentationDataInForm<- function( file.path.cell.seg.data.file="E:/Lab_MarkS/lunC_work/Immunohistochemistry_images/data_output/AP_Exp108.1_PeterMac-lungCancer-CD8-PD1/analysis-results/AP_Exp108.1_PeterMac-lungCancer-CD8-PD1_cell-seg-data-merged_CD8_PD1.tsv"
                                                          ,column.position.grouping.variable=1
                                                          ,column.position.confidence=2
                                                          # Measurement for marker 1 should be at column 3
                                                          ,column.position.marker.1=3
                                                          # Measurement for marker 2 should be at column 4
                                                          ,column.position.marker.2=4
                                                          ,marker.1.info.list=list(c("CD8", 1.8, 98))
                                                          ,marker.2.info.list=list(c("CD226", 3, 98)) # Name marker, threshold, confidence threshold
                                                          ,output.folder.path="E:/Lab_MarkS/lunC_work/Immunohistochemistry_images/data_output/AP_Exp109.1_PeterMac-lungCancer-CD8-CD226/analysis-results"){
  # Check file existence
  if(file.exists(file.path.cell.seg.data.file)!=TRUE){
    cat("Input file for cell segmentation data could not be found")
  } else {
    
    # Add wanted column positions to a vector
    column.positions <- as.numeric(c( column.position.grouping.variable
                                     ,column.position.confidence
                                     ,column.position.marker.1
                                     ,column.position.marker.2
                                     )
                                   )
    
    # Import cell segmentation data file
    cat("Reading these columns by position from the input data file into R","\n"
        ,column.positions,"\n")
    
    # Import data
    varname.marker.1 <- marker.1.info.list[[1]][1]
    varname.marker.2 <- marker.2.info.list[[1]][1]
    
    data <- read.delim( file=file.path.cell.seg.data.file
                        ,header = TRUE
                        ,sep = "\t"
                        ,stringsAsFactors = F
                        #,nrows = 20000
                        ) %>%
      dplyr::select(column.positions) %>%
      # Rename columns by positions
      dplyr::rename( grouping.variable = 1
                     ,!!varname.marker.1 := 3
                    ,!!varname.marker.2 := 4 ) %>%
      dplyr::mutate(
      # Create numeric confidence from string variable confidence
      confidence_percent= as.numeric(stringr::str_replace_all(string=.[,2]
                                                               ,pattern="%"
                                                               ,replacement=""))) %>%
      # Remove string column confidence
      dplyr::select(-2) %>%
      # Reorder columns
      dplyr::select(grouping.variable, confidence_percent, everything()) # dim(data) 2136791 4
    
    #----------------------------------------------------
    # Specify filtration conditions as expression objects
    #----------------------------------------------------
    measurement.threshold.marker.1 <- as.numeric(marker.1.info.list[[1]][2]) # marker.1.info.list[[1]][2]
    confidence.threshold.marker.1 <-  as.numeric(marker.1.info.list[[1]][3])

    measurement.threshold.marker.2 <- as.numeric(marker.2.info.list[[1]][2])
    confidence.threshold.marker.2 <- as.numeric(marker.2.info.list[[1]][3])
    
    # Set thresholds for marker 1 (column 3) and confidence (column 2)
    filtration.conditions.single.positive.marker.1 <- expression(.[,3] > measurement.threshold.marker.1 & 
                                                                 .[,2] > confidence.threshold.marker.1) # class(filtration.conditions.single.positive.marker.1) [1] "expression"
    
    # Set thresholds for marker 2 (column 5)and confidence (column 6)
    filtration.conditions.single.positive.marker.2 <- expression(.[,4] > measurement.threshold.marker.2 & 
                                                                   .[,2] > confidence.threshold.marker.2)
    # Set thresholds for double positive cells
    filtration.conditions.double.positive <- expression(.[,3] > measurement.threshold.marker.1 & 
                                                          .[,2] > confidence.threshold.marker.1 & 
                                                          .[,4] > measurement.threshold.marker.2 & 
                                                          .[,2] > confidence.threshold.marker.2)
    
    # Subset single positive cells for marker 1
    data.subset.single.positive.marker.1 <- data %>% 
      dplyr::filter(eval(filtration.conditions.single.positive.marker.1)) %>%
      dplyr::mutate(cell.status=paste0(marker.1.info.list[[1]][1],"+")) # dim(data.subset.single.positive.marker.1) 29226 5
    
    cat("Subsetting single positive cells based on"
        ,marker.1.info.list[[1]][1]
        ,"threshold of"
        , measurement.threshold.marker.1
        , "and confidence threshold of"
        , confidence.threshold.marker.1,"\n"
        ,(nrow(data)-nrow(data.subset.single.positive.marker.1))/nrow(data)*100
        ,"% data are filtered out","\n")
    
    # Subset single positive cells for marker 2
    data.subset.single.positive.marker.2 <- data %>% 
      dplyr::filter(eval(filtration.conditions.single.positive.marker.2))%>% 
      dplyr::mutate(cell.status=paste0(marker.2.info.list[[1]][1],"+")) # dim(data.subset.single.positive.marker.2) 40777 7
    
    cat("Subsetting single positive cells based on"
        ,marker.2.info.list[[1]][1]
        ,"threshold of"
        , measurement.threshold.marker.2
        , "and confidence threshold of"
        , confidence.threshold.marker.2,"\n"
        ,(nrow(data)-nrow(data.subset.single.positive.marker.2))/nrow(data)*100
        ,"% data are filtered out","\n")
    
    # Subset double positive cells
    data.subset.double.positive <- data %>%
      dplyr::filter(eval(filtration.conditions.double.positive))%>% 
      dplyr::mutate(cell.status=paste0( marker.1.info.list[[1]][1],"+"
                                       ,marker.2.info.list[[1]][1],"+"))# dim(data.subset.double.positive) 9501 7
    
    cat("Subsetting double positive cells based on"
        ,marker.1.info.list[[1]][1]
        ,"threshold of"
        , measurement.threshold.marker.1
        ,marker.2.info.list[[1]][1]
        ,"threshold of"
        , measurement.threshold.marker.1
        , "confidence threshold of"
        , confidence.threshold.marker.1
        ,"&" , confidence.threshold.marker.2,"\n"
        ,(nrow(data)-nrow(data.subset.double.positive))/nrow(data)*100
        ,"% data are filtered out","\n")  
    
    #-----------------------------------------
    # Count single positive cells per by group
    #-----------------------------------------
    # Get a list of TMA.core.location
    summary.data <- data %>% 
      dplyr::group_by(grouping.variable) %>%
      dplyr::count(name = "number.cells") # dim(summary.data) 553 2
      
    summary.marker.1 <- data.subset.single.positive.marker.1 %>%
      dplyr::group_by(grouping.variable) %>%
      # Add suffix _p for single positive cells
      dplyr::count(name = paste0(marker.1.info.list[[1]][1],"_p")) # dim(summary.marker.1) 424 2
    
    summary.marker.2 <- data.subset.single.positive.marker.2 %>%
      dplyr::group_by(grouping.variable) %>%
      # Add suffix _p for single positive cells
      dplyr::count(name = paste0(marker.2.info.list[[1]][1],"_p")) # dim(summary.marker.2) 504 2
    
    summary.double.positive <- data.subset.double.positive %>%
      dplyr::group_by(grouping.variable) %>%
      # Add suffix _p for single positive cells
      dplyr::count(name = paste0(marker.1.info.list[[1]][1],"p"
                                 ,marker.2.info.list[[1]][1],"p")) # dim(summary.double.positive) 317 2
    # Combine all summary data above
    summary.all <- list( summary.data
                        ,summary.marker.1
                        ,summary.marker.2
                        ,summary.double.positive) %>%
      purrr::reduce(dplyr::full_join, by=c("grouping.variable")) # dim(summary.all) 533 5
    
    # Change attribute class to data.frame
    # class(summary.all) # [1] "grouped_df" "tbl_df"     "tbl"        "data.frame"
    attributes(summary.all)$class <- "data.frame"
    
    # Calculate ratio as number of double positive divided by number of CD8+
    column.name.ratio <- paste0(names(summary.marker.2)[2],"_ratio")
    summary.all[[column.name.ratio]] <- summary.all[,5]/summary.all[,3]
    
    # Replace NA with 0
    summary.all[is.na(summary.all)] <- 0
    
    # Export summary data as a file
    write.table( x=summary.all
                 ,file = file.path(output.folder.path, "summary-of-cell-seg-data_single-positive_double-positive_ratio.tsv")
                 ,sep = "\t"
                 ,col.names = TRUE
                 ,quote = FALSE
                 ,row.names = FALSE)
    cat("Exported summary data file at","\n"
        ,file.path(output.folder.path, "summary-of-cell-seg-data_single-positive_double-positive_ratio.tsv"),"\n")
    
  } # End the else 
} # End the main function

---

Annotate tissue and detect DAB in QuPath

CD39

Create a new empty folder at
C:\Lab_MarkS\lunC_work\Immunohistochemistry_images\QuPath-projects\QuPath-project_Exp91-BRAFRES1-RBWHMM_CD39

Create a new QuPath project and select the folder above as the project folder
File> Project > create project

Add images to the project from
L:\Lab_MarkS\AshR\CD39 project images (all)\exp91_CD39 abcam M3R repeat

Create class
Annotations > right-click in the class panel > Add class > Name it as "tissue" > change color to "Red" The pixel classifier tissueClassifier adds red to tissue according the color specified by tissue=red
Annotations > right-click in the class panel > Add class > Name it as "DAB-positive" > change color to "Lime". The pixel classifier DAB-measurement adds green to DAB according the color specified by DAB-positive=green

Following the project description at L:\Lab_MarkS\AshR\5. QuPath Analysis\Analysis01.1 - MM_CD39 DAB Measure_AP210401\Description analysis01.1.txt, find out which the json file names used as the pixel classifiers. Here they are
findtissue vh H G 1 0.05 tissue ignore e.json
vh dab g 0.25 pos neg ash.json

Copy the source [pixel_classifiers folder](L:/Lab_MarkS/AshR/5. QuPath Analysis/Analysis01.1 - MM_CD39 DAB Measure_AP210401/classifiers/pixel_classifiers) to the project folder

Copy an existing scripts folder to the project scripts folder

Modify the script file

//Change the name of json files from
createAnnotationsFromPixelClassifier("findtissue_vh_H_G_1_0.05_tissue_ignore_e", 10000.0, 10000.0) //threshold 0.05
// to
createAnnotationsFromPixelClassifier("findtissue vh H G 1 0.05 tissue ignore e", 10000.0, 10000.0) //threshold 0.05

//from 
addPixelClassifierMeasurements("VH_DAB_G_0.5_POS_NEG_JASON2", "VH_DAB_G_0.5_POS_NEG_JASON2")
createDetectionsFromPixelClassifier("VH_DAB_G_0.5_POS_NEG_JASON2", 10.0, 10.0)
//to

Run the script
Script Editor> Run > Run for project > select all images

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Classifying pixels with 2 pixel classifiers (manually)

1. Create a project and add images
2. load a pixel classifier

3. Select tissueClassifier (/classifiers/pixel_classifiers/tissueClassifier.json)

4. Apply the classifier to full image

5. Specify hole size as 1000
   

   

6. Export renderred RGB
   

   

7. exported image
   

   

8. Delete annotations
   

   

9. load another pixel classifer

10. Apply the classifier to full image

11. Specify hole size as 1000

12. Export renderred RGB

13. Exported image as

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Classifying pixels with 2 pixel classifiers (Tam's script)

1. Run the script on 1 image

2. exported image

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Merge 3 channels in Fiji

Problem: THUNDER Imaging Systems generates images in grayscale. You would like to combine these grayscale images to single colored images

Data:
L:\Lab_MarkS\lunC\Immunohistochemistry_images\data_input\Exp59_HNSCC-CD226-ratio-201014-Rescanned\HNSCC_TMA_A_1core
HNSCC A Re-scan_1B_ICC Merged_RAW_ch00.tif
HNSCC A Re-scan_1B_ICC Merged_RAW_ch01.tif
HNSCC A Re-scan_1B_ICC Merged_RAW_ch02.tif

Solution:

Drag and drop a group of 3 images to Fiji

Select channels

Save your merged image

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Merge channels by a script

Problem: You have a large number of grayscale images to merge. You don't enjoy clicking your mouse multiple times.

Data:
L:\Lab_MarkS\lunC\Immunohistochemistry_images\data_input\Exp59_HNSCC-CD226-ratio-201014-Rescanned\HNSCC_TMA_A_1core
HNSCC A Re-scan_1B_ICC Merged_RAW_ch00.tif
HNSCC A Re-scan_1B_ICC Merged_RAW_ch01.tif
HNSCC A Re-scan_1B_ICC Merged_RAW_ch02.tif

Solution:
Move image files to a new folder if non-image
Run a imageJ macro script file (C:\Lab_MarkS\lunC\Immunohistochemistry_images\scripts\merge-red-green-blue-channels.ijm)

Plugins > Macro > Edit > Select the merge-red-green-blue-channels.ijm file

Run the script by clicking the Run tab or Ctrl+R

Enter file suffixes for blue, green and red channels

Select the source image folder

Select a folder to save merged images

This error occurs if the number of elements in the folder is not a multiple of 3

Channel merging successfully completed

//Clear the log window
print("//Clear");

//Create a popup window that takes input from users
Dialog.create("Enter file name suffixes that correspond to their channels");
  Dialog.addString("Blue Suffix:", "00");
  Dialog.addString("Green Suffix:", "01");
  Dialog.addString("Red Suffix:", "02");
 
  Dialog.show();
  blueSuffix = Dialog.getString() + ".";
  print("blueSuffix="+blueSuffix);
  greenSuffix = Dialog.getString() + ".";
  print("greenSuffix="+greenSuffix);
  redSuffix = Dialog.getString() + ".";
  print("redSuffix="+redSuffix);
  //Select the folder of source images  
  input_directory = getDirectory("Choose Source Directory");  
  list = getFileList(input_directory);
  //Select a folder to save images
  output_directory = getDirectory("Choose Save Directory");
  setBatchMode(true);
  n = list.length;
  print("The number of image files is "+n);
  if ((n%3)!=0)
    exit("The number of files must be a multiple of 3");
      stack = 0;
      first = 0;
      for (i=0; i<n/3; i++) {
      	  print("====================== Iternation "+i+"======================");
          showProgress(i+1, n/3);
          blue="?"; 
          print("blue="+blue);
          green="?"; 
          print("green="+green);
          red="?";
          print("red="+red);
          for (j=first; j<first+3; j++) {
              if (indexOf(list[j], blueSuffix)!=-1)
                  blue = list[j];
              if (indexOf(list[j], greenSuffix)!=-1)
                  green = list[j];    
              if (indexOf(list[j], redSuffix)!=-1)
                  red = list[j];
          }
		  open(input_directory+blue);
		  print("Opening blue channel image from "+input_directory+blue);
          open(input_directory+green);
          print("Opening green channel image from "+input_directory+green);
          open(input_directory+red);
          print("Opening red channel image from "+input_directory+red);
          
          run("Merge Channels...", "c1=["+red+"] c2=["+green+"] c3=["+blue+"] create");
          index = indexOf(blue, blueSuffix);
          print("index="+index);
          //Create output file names using input file name excluding the last 9 characters (e.g., _ch00.tif) and adding suffix "_channels-merged"
		  name = substring(blue, 0, lengthOf(blue)-9)+"_channels-merged";
		  print("The merged channels will be saved as ", name+".tiff"); 
          saveAs("tiff", output_directory+name);
          //Increment the first variable by 3 to work on the next group of 3 images
          first += 3;
          //Close all the opened images
          close();
          setBatchMode(false);
          }
showMessage("Channels merging completed");

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Check the MetaData folder

Problem: What if you don't know the file suffixes of channels

Solution?: Check *_Properties.xml file under the MetaData folder

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