View Batch Counting IHC.ijm
//NOTE: Before counting, please go to Analyze>>Set Scale and set the scale for image in pixels/um. Check "global" to maintain the same scale for all the images.
input = getDirectory("Input directory"); //Folder with images you want counted
output = getDirectory("Output directory"); //Folder where you want to save the new data (saves image of positive and total cells each)
keyword = "5HT"; //Keyword in the file names that you want (in case there are other photos in the folder)
suffix = ".tif"; //Works with tiff images
processFolder(input);
waitForUser("Batch Completed", "Batch Counting IHC has finished. Thank you for using!");
View VarScan2_format_converter_oncotator converter.py
__author__ = "Anand M, edited to be a oncotator input friendly version by ZK Tuong"
'''
Takes output file generated by VarScan2 somatic programme and converts the formats.
'''
import argparse, math, re
parser = argparse.ArgumentParser(
description="Converts VarScan2 somatic vcf to native format and vice-versa.\nInput is automatically detected")
View TumorVsNecroticTissueQuant.ijm
setTool("line");
waitForUser("Image selection", "Please zoom in (+) to where the scale bar is and then draw a line over the scale bar. Click 'OK' when ready.")
run("Original Scale");
run("Set Scale...");
run("Select None");
setTool("polygon");
waitForUser("Image selection", "Please draw to select area you want to keep for analysis\nIf you need to adjust, finish drawing first. Click 'OK' when ready.")
roiManager("Add");
run("Make Inverse");
setBackgroundColor(255, 255, 255);
View CountingBrownObjects.ijm
// To run this script, you need to first save screen grab of your tiff.
// This script is set up for 50-66% zoom in OliVia.
// Then click Run when you're ready and wait for prompts. =)
// splits images to other colors
run("Set Measurements...", "area limit redirect=None decimal=3");
run("Colour Deconvolution", "vectors=[H DAB]");
waitForUser("Image selection", "Please select the brown image as have it as the front image. Then Click 'OK'.")
// change brown image to b/w
View BubbleTreeInputFilePrep.R
#!/usr/bin/env Rscript
# written by ZKTUONG z.tuong@uq.edu.au
# purpose is to automate the file prep steps to generate the right files as input for BubbleTree prediction analysis
# to invoke the script, do:
# Rscript --vanilla BubbleTreeInputFilePrep.R -cna [called somatic CN file] -vcf [mutect output VCF file] -snvout [output SNV R dataset] -cnvout [output CNV R dataset] -name [sample ID/name]
library(optparse);
option_list = list(
make_option(c("-cna", "--cna"), type="character", default=NULL,