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pseudoQC: A Regression-Based Simulation Software for Correction and Normalization of Complex Metabolomics and

Shisheng Wang1, Hao Yang1

  • 1West China-Washington Mitochondria and Metabolism Research Center, Key Lab of Transplant Engineering and Immunology, MOH, West China Hospital, Keyuan South Road, Hi-Tech Zone, Chengdu, 610041, China.

Proteomics
|September 2, 2019
PubMed
Summary

Simulate quality control (QC) data for mass spectrometry (MS) experiments using pseudoQC software. This tool enhances data accuracy in metabolomics and proteomics by using machine learning to correct and normalize datasets.

Keywords:
machine learningmetabolomicsproteomicspseudo-quality controlregression

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Area of Science:

  • Proteomics
  • Metabolomics
  • Bioinformatics
  • Computational Biology

Background:

  • Mass spectrometry (MS)-based metabolomics and proteomics experiments are susceptible to signal variations that compromise data accuracy.
  • Pooled quality control (QC) samples are crucial for data consistency but are challenging to manage due to experimental complexity and variability.
  • Many proteomics projects neglect QC sample integration during the initial experimental design phase.

Purpose of the Study:

  • To develop a user-friendly, web-based software tool (pseudoQC) for simulating quality control (QC) sample data.
  • To address the challenges of data consistency and QC sample stability in MS-based omics studies.
  • To provide a solution for researchers lacking bioinformatics expertise to improve their experimental data.

Main Methods:

  • Developed pseudoQC, an interactive web-based software utilizing four machine learning-based regression methods to simulate QC data.
  • Applied simulated QC data for the correction and normalization of two published metabolomics and proteomics datasets.
  • Evaluated the performance of linear versus nonlinear regression methods for QC data simulation.

Main Results:

  • Simulated QC data effectively corrected and normalized existing metabolomics and proteomics datasets.
  • Nonlinear regression methods demonstrated superior performance compared to linear methods in the simulation and correction processes.
  • The pseudoQC software provides a graphical user interface, making it accessible to scientists without specialized bioinformatics backgrounds.

Conclusions:

  • pseudoQC offers a powerful and accessible solution for generating simulated QC data in MS-based omics studies.
  • The tool enhances data quality and reliability, particularly for high-throughput and long-term projects.
  • pseudoQC is open-source and freely available, promoting wider adoption and advancement in the field.