mcmicroprep 0.1.4

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🧪 mcmicroprep 🚀

A command-line tool for preparing multiplexed imaging datasets (🦠 Olympus, 🩸 RareCyte) for the MCMICRO Nextflow pipeline.

🛠️ Installation

1. Prerequisites

   • Conda or Miniconda installed 🐍
   • Python 3.10+ environment 🌟
   • SLURM & Nextflow (labsyspharm/mcmicro) on your $PATH

2. Create Conda env

   conda create -n mcmicroprep python=3.12
   conda activate mcmicroprep
   

3. Install package

   pip install mcmicroprep
   

📁 Expected Dataset Structure

Your dataset root should contain one subdirectory per slide. Structures vary by vendor:

🦠 Olympus

Each slide directory must contain at least one *_frames/ folder —-- this is the minimum required structure. Additional files or folders may be present and do not need to be removed.

.DATASET FOLDER
├── slide1/
│   ├── image1_frames/
│   ├── image2_frames/
├── slide2/
└── slideN/

After running for Olympus: each slide/image folder would be as follows

slide1/
├── raw/                   # image1_frames/, image2_frames/
├── misc_files/            # JSON, logs
├── batch_submission.sh    # pipeline wrapper
├── mcmicro_template.sh    # Nextflow template
├── base.config
├── markers.csv
└── params.yml            

🩸 RareCyte

Slide dirs may contain *.rcpnl at any depth: —-- this is the minimum required structure. Additional files or folders may be present and do not need to be removed.

/path/to/dataset/
├── slide1/
│   ├── img001.rcpnl
│   ├── subA/img002.rcpnl
│   └── other files
└── slideN/

After running for RareCyte:

slide1/
├── raw/                   # all .rcpnl files
│   ├── img001.rcpnl
│   └── img002.rcpnl
├── misc_files/            # CSV, text
├── batch_submission.sh
├── mcmicro_template.sh
├── base.config
├── markers.csv
└── params.yml             

🚀 Usage

Note: Configured for the HMS O2 cluster (SLURM). Generalize by editing SLURM directives in templates/common/.

🦠 Olympus

preparemcmicro \
  --microscope olympus \
  --image-root /path/to/dataset

🩸 RareCyte

preparemcmicro \
  --microscope rarecyte \
  --image-root /path/to/dataset

🛠️ Next Steps for Users

1. ✏️ **Edit **`` in each slide directory to include your experiment-specific cycle-to-marker mappings.
2. 📤 Upload the entire processed dataset folder to the O2 cluster if you ran this locally.
3. 🚀 Start the job on O2:

   cd /n/scratch/users/USERNAME/<DATASET FOLDER>
   bash batch_submission.sh --dataset_path /n/scratch/users/USERNAME/<DATASET FOLDER>
   

Happy processing! 🔬