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Usage

PlexPipe supports command-line execution and interactive analysis via Jupyter notebooks. For scalable workflows, it can also be orchestrated through Nextflow (see plex-pipe-nextflow).

Interactive steps (1 - Core detection and 3 - Quality Control) are designed to be executed using Napari with designed widgets.

Command-Line Usage

To run the pipeline from the command line, use the provided scripts:

Prepare Cores

python scripts/02_cut_rois.py --exp-config ../examples/example_pipeline_config.yaml

or alternatively with remote sourcing of the image files:

python scripts/02_cut_rois.py --exp_config '../examples/example_pipeline_config_globus.yaml' -globus_config '../examples/example_globus_config.yaml' --from_collection 'r_collection_id' --to_collection 'cbi_collection_id'  --cleanup

Image Processing

python 04_segment.py --exp_config ../examples/example_pipeline_config.yaml --overwrite

Quantification

python 05_quantify.py --exp_config ../examples/example_pipeline_config.yaml --overwrite

Jupyter Notebook Usage

For interactive inspection, prototyping, or educational purposes, the following notebooks illustrate how to use the components directly:

  • core_selection_demo.ipynb: An interactive notebook that enables users to define cores as rectangles or polygons using the Napari viewer. It supports automatic core detection via Segment Anything v2, with the option to manually correct the detected shapes or draw new ones from scratch. This step is interactive and only available as a notebook. The result is a core_info.csv file containing core metadata for use in subsequent steps via either Jupyter or CLI.
  • core_cutting_demo.ipynb: Demonstrates how to load a single image and metadata entry and apply the core cutting logic.