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Readme.txt
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This readme file shows how to properly run the msiPL code (Abdelmoula et al,):
Walid Abdelmoula et al, msiPL: Non-linear Manifold and Peak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks, bioRxiv, 2020
License: The Peak Learning software (msiPL) will be shared using the 3D Slicer Software License agreement.
---------------------- Installations: Software and Libraries --------------
We have implemented our machine learning model using the following software items:
1- Python(3.6.4)
2- Keras (2.1.5-tf) with a Tensorflow(1.8.0) backend.
3- Packages: numpy(1.14.2), sklearn(0.19.1), scipy(1.0.0), and h5py(2.7.1)
4- We implemented this model on Windows 10 PC workstation(Intel Xenon 3.3GHz, 512 GB RAM, 64-bit Windows, 2 GPUs NVIDIA TITAN Xp).
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---------------------------------- Demo --------------------------------------
How to run the code?
1- "msiPL_Run.py" is the main file that you should run first. The file should be running in a sequential manner, and we have
provided required comments for instructions and guidance. In this file you will be able to:
1.1. Load a dataset.
1.2. Load the computational neural network architecture (VAE_BN).
1.3. Train the model.
1.3. Non-linear manifold learning and data visualization (non-linear dimensionality reduction)
1.4. Evaluate the learning quality by estimation and reconstruction of the original data
1.5. Peak Learning learning (Equation#4): to get a smaller list of informative peaks.
1.6. Perform data clustering (GMM).
1.7. Identify localized peaks within each cluster.
2- "Computational_Model.py": implementation of the fully connected variational autoencoder, and regularized
with batch normalization.
3- "LearnPeaks.py": implementation of a function that identifies peaks of interest.
It should be called after training the model, as instructed in "msiPL_Run.py".
4. "msiPL_ForTesting.py": ultra-fast analysis on test data without any prior peak picking.
You will need first to load the trained model from step#1 ("msiPL_Run.py").
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We provide a sample of a publicly available MSI data to train and test the model and to ensure reproducibility.