PicassoBatchProcess

Automatic analysis of SMLM data using Picasso software

This workflow is suitable if you have stable and robust parameters that you are using for your DNA-PAINT analysis. Using the workflow, you can enter your parameters for all the steps and a given folder will be processed.

Workflow

Copy all HDF5 and YAML files into input_folder → copy all 6 scripts and config.yaml file into one folder → adjust config.yaml file to set your parameters → run main.py script

Instalation

The required Python environment can be installed using conda and the provided environment file (environment.yaml).

1. Clone the repository:

git clone https://github.com/HeilemannLab/PicassoBatchProcess.git
cd path/to/PicassoBatchProcess

2. Create conda enviroment and install required packages:
   • It will create an environment with the name PicassoBatchProcess. To change the name, make changes in environment.yaml file

conda env create -f environment.yaml

3. Activate the environment:

conda activate PicassoBatchProcess

• The environment was tested with Python 3.8.

Pipeline steps:

1. Filtering (Sx/Sy, ellipticity, etc.)

• ‘bg_filter.py’: Background filtering. Parameters can be specified/adjusted in ‘config.yaml’ file.
• Saves HDF5 and YAML files in an output_folder which can be specified in ‘config.yaml’ file.

2. NeNA value calculation

• ‘nena.py’: Calculates NeNA value from filtered files (from ‘bg_filter.py’)
• based on the version==7.3 of Picasso software (before the NeNA correction)

3. Linking

• ‘linking.py’: Extracts calculated NeNA value from ‘nena.py’ function, performs linking
• Saves HDF5 and YAML files in an output_folder.
• Dark times and min sample parameters can be adjusted in ‘config.yaml’ file.
• User can specify nena_multiplier which will be used for dark time value calculation using extracted NeNA value.

4. DBSCAN clustering

• ‘dbscan.py’: Uses previously calculated NeNA and performs DBSCAN clustering on linked files
• Saves HDF5 and YAML files in an output_folder (both, clustered and centers files).
• Radius and num of samples parameters can be adjusted in ‘config.yaml’ file.

5. Postfiltering for mean and std frame

• ‘mean_std_frame_filter.py’: Performs postfiltering on clustered files
• Saves HDF5 and YAML files in an output_folder

Citation

If you use this pipeline, please cite:

Alexandra Kaminer, Yunqing Li, Hans-Dieter Barth, Marina S. Dietz, Mike Heilemann.
Quantitative mapping of nanoscale EGFR–Grb2 assemblies by DNA-PAINT
bioRxiv 2026.02.16.706070; doi: https://doi.org/10.64898/2026.02.16.706070