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Review of spectral clustering algorithms used in proteomics

Author

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  • Shraddha Kumar
  • Anuradha Purohit
  • Sunita Varma

Abstract

Tandem mass spectrometry (MS/MS) generates a large number of spectra showing the signal intensity of detected ions as a function of mass-to-charge ratio. Spectral clustering in proteomics is a powerful but under-utilised technique. Based on the similarity of spectra, the spectral clustering algorithms systematically and unerringly classify large numbers of spectra, such that all spectra in a given cluster belong to the same peptide. The data points in the spectral clustering approach are connected and do not require having convex boundaries. Spectral clustering therefore reduces the running time and computation requirements of spectral library and database searches. It enhances peptide identification process and has fuelled the development of many new proteomics algorithms recently. The goal of this review is to provide a clear overview of the most popular spectral clustering algorithms used in proteomics. It describes a systematic analysis of these spectral clustering algorithms, evaluating the benefits and limitations of each approach.

Suggested Citation

  • Shraddha Kumar & Anuradha Purohit & Sunita Varma, 2023. "Review of spectral clustering algorithms used in proteomics," International Journal of Data Science, Inderscience Enterprises Ltd, vol. 8(1), pages 16-38.
    • Handle: RePEc:ids:ijdsci:v:8:y:2023:i:1:p:16-38
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