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MS-feature

A deep learning-based feature detection tool for feature detection in Liquid Chromotography-Mass Spectrometry (LC-MS). Details available in Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics. The folder for Faster-RCNN framework is forked from here.

Dependencies

  • python==3.8
  • torch==1.11.0
  • torchvision==0.12.0
  • opencv-python==4.6.0.66
  • jupyter==1.0.0
  • numpy==1.22.4
  • tqdm==4.64.0

Run

  • Put source .mzml files into the data_prep/src folder
  • Run mzml_to_img.ipynb jupyter notebook
  • Adjust the height and width parameters in the first two lines generate_windows.ipynb for the sizes of the sliding windows. The default of 240x270 has been applied as stated in the paper.
  • Run the first 4 cells of generate_windows.ipynb. You should expect the windows in the folder named img_(height)_(width).
  • Execute predict.py, changing line 11 to the folder generated from the last step.