Scripts to segment roots and calculate intensities per cell.

For information about the methods see the methodology.

This image analysis project has been setup to take advantage of a technology known as Docker.

This means that you will need to:

1. Download and install the Docker Toolbox
2. Build a docker image

Before you can run the image analysis in a docker container.

Before you can run your analysis you need to build your docker image. Once you have built the docker image you should not need to do this step again.

A docker image is basically a binary blob that contains all the dependencies required for the analysis scripts. In other words the docker image has got no relation to the types of images that we want to analyse, it is simply a technology that we use to make it easier to run the analysis scripts.

$ cd docker
$ bash build_docker_image.sh
$ cd ..

The image analysis will be run in a Docker container. The script run_docker_container.sh will drop you into an interactive Docker session.

$ bash run_docker_container.sh
[root@048bd4bd961c /]#

Now you can run the image analysis on a series within a microscopy file. The below analyses series 0 in data/raw.lif and writes the output to output.

[root@048bd4bd961c /]# python scripts/analyse_series.py data/raw.lif 0 output/

We need to create a bash script for mass processing. If running outside of a docker container using a virtual environment setup we need to add a line sourcing it.

echo "source env/bin/activate" > mass_process.sh

We can then append all the jobs to the newly created mass_process.sh script.

$ python scripts/mass_process.py input_dir ouput_dir bash-script >> mass_process.sh

Finally, we can run the mass processing bash script.

$ bash mass_process.sh

Create master csv file.

$ python scripts/cat_csv_files.py output_dir > data.csv

Create faceted histogram.

$ Rscript scritps/all_histograms.R data.csv histograms.png