LightSaver is a powerful data analysis package designed for fluorescent C. elegans imaging. Developed by Samuel Freitas with contributions from Raul Castro-Portugez at the University of Arizona, Sutphin Lab in the Microbiology (MCB) and Biomedical Engineering (BME) departments.
Please note: We're actively working on both a Python version and a standalone application for enhanced accessibility.
- 'Image Processing Toolbox'
- 'Computer Vision Toolbox'
This directory structure is essential for the proper functioning of the
multiple_samples -> Lightsaver_batch.m
script. In this example, the overarching experiment is the "Example Experiment" folder under the data directory.
Important Notes:
- The script scans files recursively, sorting them by timepoint (following the nomenclature DN, Day N).
- Even if there's only a single timepoint, this directory format must still be followed, but with a single sub-experiment folder.
Image Naming Guidelines:
- Each image should have a descriptive name (e.g.,
skn-1-HT115-EV_D1_1.tiff
,skn-1-HT115-EV_D1_2.tiff
). The naming convention typically followsexp-name-and-sumbnames_dayN_replicateN.tiff
. - The
Data analysis and export
section of the code will check for a number at the end of each file name (replicateN), additionally the system groups by removing any and all items that are consistent between ALL of the image names. Therefore if an unexpected result pops up the first check should be the image names and MAKING SURE that they are consistent with each other
- Set up data as shown above.
- Open
Lightsaver_batch.m
orLightSaver_batch.py
under the respective python or matlab directories. - Run the script (press F5 or the run button in MATLAB or your choice of python IDE -- vscode tested).
- The parameters prompt will ask for experiment-specific details (press OK when completed).
- Choose the overarching experiment folder in the selection prompt.
- The script will display progress bars and export the data.
- Check the "Exported images" folder (usually in documents/github/LightSaver) for the output. Rerun with the "Use large blob fix" flag if needed.
Usage: Data Processing Single Sub-Experiments Individually (Not Recommended Unless Data Is Extremely Noisy and "Bad_images_fix.m" Must Be Used)
- Open
Ligthsaver_script.m
. - Set parameters.
- Run
lightsaver_script.m
. - Choose the directory containing the .tiff images.
- Check output data if necessary.
If there are problems:
- Large blobs? Use the
large_blob_fix
option inlightsaver_script.m
. - Major issues? Employ
bad_images_fix.m
.
Now, you should find a data.csv
file in the directory containing the *.tifs.
- Open and run
Data_analysis_and_export.m
. - Choose the overarching experiment folder from the dropdown menu.
- Verify that "Analyzed_data.csv" is correct and the
output_figures
directory is present.