Luciferase-pipeline

An automated pipeline for analyzing arabidopsis luciferase imaging data.

How to Install

On Linux

• Download the install-luc-linux64.sh script
• run install-luc-linux64.sh in bash, e.g. by running bash install-luc-linux64.sh
• answer the prompts (one of the steps requires sudo/admin/root privledges)

On Windows

• Download the install-luc-win64.ps1 script
• Change the powershell execution policy to allow running scripts, e.g. by running Set-ExecutionPolicy Bypass in powershell
• Run 'install-luc-win64.ps1 in PowerShell, e.g. by opening PowerShell and typing .\install-luc-win64.ps1
• Answer the prompts (one of the steps requires administrator privledges)

How to Run

On Linux

Run the run.sh script in bash, e.g. by running bash run.sh

On Windows

Run the run.ps1 script in powershell

About

Capabilities

• Generate ROI objects to measure plants in the data from an image ( the first image is usually very bright relative to the rest of the sequence )
• measure the plant ROI brightness over the sequence of photographs (and therefore over time)
• process the data to (ideally) improve the signal to noise ratio
• correlate plant ROI objects with experimental group ROI objects by position, and use this to organize data by experimental group
• graph accumulated measurements

Assumptions

• The data is a timelapse/sequence of 16-bit grayscale images
• the images depict plates of (transgenic) arabidopsis plants that express luciferase production
  • the plants have access to luciferin substrate (in the water or the growth media)
  • the plants emit an amount of light that is detectable by the camera
  • the only light sources in the images are either the plants themselves or reflections of their light
• the images cotain a region that is recognizably not data
• the plates in the images form one or more distinct experimental groups
• the time between photographs is fixed for the duration of the timelapse/sequence

Dependencies

• Fiji (ImageJ variant)
• Python 3 (programming language)
• numpy (n-dimensional array mathematic/manipulation library)
• matplotlib (graphing library)
• pyjnius (java to python interoperation library)
• OpenJDK (open-source java development kit)
• venv (virtual environment tool for Python)

Input Parameters

• folder with sequence of images
• image (used for segmentation: division of image[s] into subject(data) and non-subject(background) zones)
• background (non-data area of image used for comparison, represented as ImageJ ROI object)
• groups (archive(zip file) containing list of areas (represented as ImageJ ROI objects) defining distinct experimental groups)
• whether to load or generate and save plant/data ROI objects
• filename of or for plant/data ROI objects
• whether to normalize the data (by dividing each subject ROI by its own maximum value)
• the time the first photograph was taken
• elapsed time between each photograph being taken

Output

• aggregate graph organized by experimental group over time (average brightness on y-axis, elapsed time on x-axis)
• graph of each experimental group over time (individual brightness on y-axis, elapsed time on x-axis)
• graph of all individuals over time (individual brightness on y-axis, elapsed time on x-axis)