fvfmPy: Fluorescence Imaging Processing Pipeline

Semi-automated analysis of leaf disc fluorescence images from a Walz ImagingPAM fluorometer.

This program automatically identifies regions of interest in leaf disc arrays, computes Fv/Fm = (Fm − Fo) / Fm for each leaf disc, and exports results to CSV for further processing.

Overview

This program is a semi-automated data analysis pipeline that processes fluorescence images generated from a Walz ImagingPAM fluorometer. The user specifies a folder containing .PIM or .TIF images, and for each image:

1. The program attempts to automatically identify regions of interest (ROIs)
2. The program estimates row and column numbers
3. The program estimates Fv/Fm within each ROI and logs each observation
4. Finally, observations are collated and output in a "long-format" CSV

Optionally, the user has the opportunity to:

5. Rotate or crop the image
6. Manually specify row and column numbers
7. Adjust image segmentation parameters
8. Manually adjust ROI placement
9. Re-analyze previous images

Requirements

• Python 3.10 or later
• Windows 10/11 or macOS 10.15+

Python dependencies:

matplotlib
numpy
opencv-python
pandas
PySide6
scipy
scikit-image

Quick start

We recommend the following steps to install and launch fvfmPy in a Python Virtual Environment.

Windows PowerShell:

# 1. Create a new virtual environment: 
python3 -m venv .venv 

# 2. Install fvfmPy within the virtual environment: 
.\.venv\Scripts\python.exe -m pip install fvfmPy

# 3. Run fvfmPy: 
.\.venv\Scripts\fvfmPy.exe

MacOS Terminal:

# 1. Create a new virtual environment: 
python3 -m venv .venv 

# 2. Install fvfmPy within the virtual environment: 
.venv/bin/python -m pip install fvfmPy

# 3. Activate virtual environment and run fvfmPy: 
source .venv/bin/activate
fvfmPy

Files

Output

Output is written to a "long-format" .CSV file in a location specified by the user. A complete description of the output file format can be found in the User's Guide.

R package

For users who prefer R and RStudio, an R wrapper package is available from this link. It exposes the same pipeline via three simple R functions, with the Python backend running transparently via reticulate.

Documentation

See the User's Guide for detailed walkthrough of the data analysis pipeline, description of optional processing steps, output file descriptions, and troubleshooting tips.

Authors

Josef Garen, Pieter Arnold, and Kristine Crous