BDehapiot/ETH-ScopeM_Gassler

Analysis of fungi spore germination

ETH-ScopeM_Gassler

Spore germination analysis tool

Index

• Installation
• Usage
• Main procedure
• Outputs
• Comments

Installation

Pease select your operating system

Windows

Step 1: Download this GitHub Repository

• Click on the green <> Code button and download ZIP
• Unzip the downloaded file to a desired location

Step 2: Install Miniforge (Minimal Conda installer)

• Download and install Miniforge for your operating system
• Run the downloaded .exe file
  • Select "Add Miniforge3 to PATH environment variable"

Step 3: Setup Conda

• Open the newly installed Miniforge Prompt
• Move to the downloaded GitHub repository
• Run one of the following command:

# TensorFlow with GPU support
mamba env create -f environment_tf_gpu.yml
# TensorFlow with no GPU support 
mamba env create -f environment_tf_nogpu.yml

• Activate Conda environment:

conda activate Gassler

Your prompt should now start with (Gassler) instead of (base)

MacOS

Step 1: Download this GitHub Repository

• Click on the green <> Code button and download ZIP
• Unzip the downloaded file to a desired location

Step 2: Install Miniforge (Minimal Conda installer)

• Download and install Miniforge for your operating system
• Open your terminal
• Move to the directory containing the Miniforge installer
• Run one of the following command:

# Intel-Series
bash Miniforge3-MacOSX-x86_64.sh
# M-Series
bash Miniforge3-MacOSX-arm64.sh

Step 3: Setup Conda

• Re-open your terminal
• Move to the downloaded GitHub repository
• Run one of the following command:

# TensorFlow with GPU support
mamba env create -f environment_tf_gpu.yml
# TensorFlow with no GPU support 
mamba env create -f environment_tf_nogpu.yml

• Activate Conda environment:

conda activate Gassler

Your prompt should now start with (Gassler) instead of (base)

Usage

preextract.py

Read data from remote_path, format and save to data_path

• Paths

- remote_path     # str, path to remote directory
- data_path       # str, path to saving directory
- exclude         # list[str], exclude path(s) containing any of the str

• Parameters

- pixSize_out     # float, output pixel size (µm) 

main.py

Read data from data_path and execute the main procedure

• Paths

- remote_path     # str, path to remote directory
- data_path       # str, path to data directory
- target          # str, "all" or "image_name"
- overwrite       # bool, overwrite outputs

• Parameters

- threshAll       # float, threshold for "all" predictions
- threshOut       # float, threshold for "outlines" predictions
- threshBod       # float, threshold for "bodies" predictions
- min_size        # int, min. size for tracked objects
- min_roundness   # float, min. roundness for tracked objects

Main procedure

Outputs

Images

- C1_proj.tif     # channel 1 (spores) std-projection
- C2_proj.tif     # channel 2 (bacteria) sum-projection
- display.tif     # tracked objects display
- labels.tif      # tracked objects labels
- composite.tif   # C1_proj + C2_proj + display

Data

- data.pkl        # PKL file containing all data 
- area.csv        # tracked objects areas
- intensity.csv   # tracked objects C2 intensities 
- length.csv      # tracked objects length  
- roundness.csv   # tracked objects roundness
- plot.jpg        # all data plot

Comments

• Features
  • Done
    • Background subtraction for channel #2
    • Measure object elongation
    • Remove border touching objects
    • Output CSV
    • Save plots as JPG
    • Object ID outputs & plots
  • To do
    • Remove early touching objects
    • Manually reject objects from analysis