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Getting Started

This guide walks you through your first PartiNet analysis using the three-stage pipeline. We'll process cryo-EM micrographs from start to finish.

Prerequisites​

Before starting, ensure you have:

  • PartiNet installed (see Installation)
  • Motion-corrected micrographs in a source directory
  • A project directory where outputs will be saved
  • GPU access for optimal performance

Directory Structure​

PartiNet expects and creates the following directory structure:

project_directory/
├── motion_corrected/          # 📁 Your input micrographs
│   ├── micrograph1.mrc
│   ├── micrograph2.mrc
│   └── ...
├── denoised/                  # 🧹 Created by denoise stage
│   ├── micrograph1.mrc
│   ├── micrograph2.mrc
│   └── ...
├── exp/                       # 🎯 Created by detect stage
│   ├── labels/               # 📋 Detection coordinates
│   │   ├── micrograph1.txt
│   │   ├── micrograph2.txt
│   │   └── ...
│   ├── micrograph1.png    # 🖼️ Micrographs with detections drawn
│   ├── micrograph2.
│   └── ...
└── partinet_particles.star   # ⭐ Final STAR file (created by star stage)

Pipeline Flow:

  1. Input → motion_corrected/ (your micrographs)
  2. Stage 1 → denoised/ (cleaned micrographs)
  3. Stage 2 → exp*/ (detections + visualizations)
  4. Stage 3 → *.star (final particle coordinates)

Stage 1: Denoise​

The first stage removes noise from your micrographs and improves signal-to-noise ratios:

Local Installation
partinet denoise \
    --source /data/my_project/motion_corrected \
    --project /data/my_project

What this does:

  • Reads micrographs from motion_corrected/ directory
  • Applies denoising algorithms
  • Saves cleaned micrographs to denoised/ directory in your project folder

Stage 2: Detect​

The detection stage identifies particles in your denoised micrographs:

Local Installation
partinet detect \
    --weight /path/to/downloaded/model_weights.pt \
    --source /data/partinet_picking/denoised \
    --device 0,1,2,3 \
    --project /data/partinet_picking

What this creates:

  • exp/ directory in your project folder
  • exp/labels/ directory containing detection coordinates for each micrograph
  • Micrographs with detection boxes drawn on top (saved in exp/)

Key parameters:

  • --backbone-detector: Neural network architecture to use
  • --weight: Path to trained model weights
  • --conf-thres: Confidence threshold for detections (0.0 = accept all)
  • --iou-thres: Intersection over Union threshold for filtering overlapping detections
  • --device: GPU devices to use (0,1,2,3 = use 4 GPUs)

Stage 3: Star​

The final stage converts detections to STAR format and applies confidence filtering:

Local Installation
partinet star \
    --labels /data/my_project/exp/labels \
    --images /data/my_project/denoised \
    --output /data/my_project/partinet_particles.star \
    --conf 0.1

What this does:

  • Reads detection labels from exp/labels/
  • Filters particles based on confidence threshold (0.1 in this example)
  • Creates a STAR file ready for further processing in RELION or other software

Output Files​

After running all three stages, you'll have:

  1. Denoised micrographs (denoised/) - Cleaned input for particle detection
  2. Detection visualizations (exp/*.mrc) - Micrographs with particle boxes drawn
  3. Detection coordinates (exp/labels/*.txt) - Raw detection data
  4. STAR file (*.star) - Final particle coordinates ready for downstream processing

Next Steps​

Troubleshooting​

If you encounter issues:

  • Ensure all paths exist and are accessible
  • Check GPU availability with nvidia-smi
  • Verify container mounting with -B flags includes all necessary paths