3D-MSNet: A point cloud based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data

If you meet any problem in running or training 3D-MSNet, feel free to contact [email protected]

Highlights

  • Novelty: 3D-MSNet achieves lossless high-dimensional feature detection and quantification, by considering LC-MS feature detection problem as a 3D point cloud instance segmentation problem.
  • Accuracy: 3D-MSNet achieved the best accuracy in terms of feature detection and quantification on the test datasets.
  • Applicability: 3D-MSNet can be widely applied to various MS systems and acquisition methods.
  • Efficiency: 3D-MSNet spends reasonable analysis time by accelerating with GPU, which is similar to traditional methods and about 5 times faster than other deep learning methods.
  • Potentiality: 3D-MSNet can obtain better accuracy on bigger annotated training datasets.

Environment

Recommended

Intel(R)_Core(TM)_i9-10900K CPU, 128GB memory, GeForce RTX 3090 GPU

Ubuntu 16.04 + CUDA 11.1 + cuDNN 8.0.5

Anaconda 4.9.2 + Python 3.6.13 + PyTorch 1.9

Setup

  1. Prepare the deep-learning environment based on your system and hardware, including GPU driver, CUDA, cuDNN, Anaconda, Python, and PyTorch.

  2. Install the dependencies. Here we use ROOT_PATH to represent the root path of 3D-MSNet.

    cd ROOT_PATH

    pip install -r requirements.txt

  3. Compile CUDA code. This will take a few minutes.

    cd cuda

    python setup.py install

Datasets

The 3DMS dataset and all the benchmark datasets (mzML format) can be freely downloaded at Zenodo.

Raw MS files of the metabolomics datasets can be downloaded at Google Drive.

Raw MS files of the proteomics datasets can be downloaded at ProteomeXchange (dataset PXD001091).

Targeted annotation results, evaluation results and evaluation methods can be downloaded at Zenodo.

Run 3D-MSNet

Demos

Our demos can help you reproduce the evaluation results.

Place the benchmark datasets as follows.

3D-MSNet-master
├── dataset
│   ├── TripleTOF_6600
│   │   ├── mzml
│   │   │   ├── *.mzML

│   ├── QE_HF
│   │   ├── mzml
│   │   │   ├── *.mzML

│   ├── Orbitrap_XL
│   │   ├── mzml
│   │   │   ├── *.mzML

Then run scripts in folder DEMO. For example:

cd ROOT_PATH

Prepare point clouds: python DEMO/TripleTOF_6600_untarget/0_pc_extraction.py

Extract features: python DEMO/TripleTOF_6600_untarget/1_peak_detection.py

The result files are saved in the dataset folder.

Customized running

Refer to DEMO for parameter setting of different LC-MS platforms.

cd ROOT_PATH

Prepare point clouds:

python workflow/predict/point_cloud_extractor.py --data_dir=PATH_TO_MZML --output_dir=POINT_CLOUD_PATH --window_mz_width=0.8 --window_rt_width=6 --min_intensity=128 --from_mz=0 --to_mz=2000 --from_rt=0 --to_rt=300 --expansion_mz_width=0.1 --expansion_rt_width=1

Extract features:

python workflow/predict/main_eval.py --data_dir=POINT_CLOUD_PATH --mass_analyzer=orbitrap --mz_resolution=60000 --resolution_mz=400 --rt_fwhm=0.1 --target_id=None

Run python workflow/predict/point_cloud_extractor.py -h and python workflow/predict/main_eval.py -h to learn parameter details.

Train

We provided a pretrained model in experiment folder.

If you want to train the model on your self-annotated data, prepare your .csv files refer to the 3DMS dataset. Each MS signal should be annotated an instance label.

Place the training dataset as follows.

3D-MSNet-master
├── dataset
│   ├── your_training_dataset
│   │   ├── dataset_anno
│   │   │   ├── [id_mz_rt].csv

Then change the training parameters at config/msnet_default.yaml

cd ROOT_PATH

Split training set and validation set:

python workflow/train/dataset_generator.py

Start training:

python workflow/train/main_train.py

Trained models are saved in experiment folder.

Citation

Cite our paper at:

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License

3D-MSNet is an open-source tool, using Mulan Permissive Software License，Version 2 (Mulan PSL v2)

GitHub

View Github