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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
  1. 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
  1. 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