mzrt2h5 0.1.1

A package to convert mzML files to HDF5 for deep learning.

mzrt2h5

A Python package to convert mzML files to HDF5 format for deep learning applications.

Installation

After installation, a new command mzrt2h5 will be available in your terminal.

pip install -r requirements.txt
pip install .

CLI Usage

This is the most straightforward way to use the package. After installation, you can call the mzrt2h5 command from your terminal.

Example:

mzrt2h5 process \
    /path/to/your/mzml_folder/ \
    /path/to/your/output.h5 \
    --metadata-csv-path /path/to/your/metadata.csv \
    --rt-precision 0.1 \
    --mz-precision 0.01

Options:

Use mzrt2h5 --help to see all available options.

Python Usage

from mzrt2h5.processing import save_dataset_as_sparse_h5
from mzrt2h5.dataset import DynamicSparseH5Dataset
from mzrt2h5.visualization import plot_sample_image

# Process mzML files and save to HDF5
save_dataset_as_sparse_h5(
    folder="path/to/your/mzML_files",
    save_path="output.h5",
    rt_precision=0.1,
    mz_precision=0.01,
    metadata_csv_path="path/to/your/metadata.csv",
)

# Create a PyTorch dataset
dataset = DynamicSparseH5Dataset(
    h5_path="output.h5",
    target_rt_precision=0.5,
    target_mz_precision=0.05,
)

# Create a dataset with on-the-fly augmentation for training
# with a random retention time shift of +/- 30 seconds
# and a random m/z shift of +/- 5 ppm.
train_dataset = DynamicSparseH5Dataset(
    h5_path="output.h5",
    target_rt_precision=0.5,
    target_mz_precision=0.05,
    augment=True,
    aug_rt_shift_s=30,
    aug_mz_shift_ppm=5
)

# Plot a sample image from the HDF5 file
plot_sample_image(
    h5_path="output.h5",
    sample_id="Sample_A", # Or an integer index like 0
    target_rt_precision=0.5,
    target_mz_precision=0.05,
    output_path="sample_A_plot.png" # Saves to file, remove to display interactively
)

Visualization

To visualize a mass spectrometry image from your HDF5 file, use the mzrt2h5 plot command:

mzrt2h5 plot \
    /path/to/your/output.h5 \
    "Sample_A" \
    --rt-precision 0.5 \
    --mz-precision 0.05 \
    --output-path sample_A_plot.png

Options:

Use mzrt2h5 plot --help to see all available options for plotting.

Web Interface

This package also includes a simple web interface to perform the conversion.

1. Run the Flask app:

   python app/app.py
   

2. Access the web interface: Open your web browser and go to http://127.0.0.1:5000.

3. Use the form: Upload your metadata file, select your mzML files directory, set the parameters, and click "Process and Download H5".