MS Peak Detection & Processing

This Python script processes mass spectrometry (MS) data by applying smoothing, baseline correction, and peak detection. The results are exported as Excel files for further analysis.

Features

• Loads MS data from an Excel file.
• Applies Savitzky-Golay smoothing to reduce noise.
• Performs baseline correction using wavelet decomposition.
• Detects peaks based on intensity prominence.
• Saves processed data and detected peaks to separate Excel files.
• Visualizes the raw, smoothed, and baseline-corrected spectra with detected peaks.

Installation

Ensure you have Python installed (>=3.7). Install the required dependencies using:

pip install -r requirements.txt

Usage

1. Place your MS data file (Excel format) in the same directory as the script.
2. Modify the file_path variable in main() to point to your data file.
3. Run the script:

python ms_peak_processing.py

Output

• Processed_MS_Data.xlsx: Contains raw, smoothed, and baseline-corrected intensity values.
• Detected_Peaks.xlsx: Contains detected peak m/z values and their corresponding intensities.

Example Data

An example dataset (example_data.xlsx) is provided for testing purposes.

Dependencies

• pandas
• numpy
• matplotlib
• seaborn
• pywavelets
• scipy

License

This project is open-source under the MIT License.

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Feel free to modify the script to suit your specific needs!