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Related Experiment Video

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Protein Multiplexed Immunoassay Analysis with R.

Edmond J Breen1

  • 1Australian Proteome Analysis Facility (APAF), Macquarie University, Level 4, Building F7B, Research Park Drive, Sydney, NSW, 2109, Australia. ebreen@proteome.org.au.

Methods in Molecular Biology (Clifton, N.J.)
|July 5, 2017
PubMed
Summary
This summary is machine-generated.

This study presents R methods for analyzing multiplexed immunoassay data from type 2 diabetes patients. The bioinformatics approach ensures rigorous and reproducible analysis of fluorescence responses.

Keywords:
BioinformaticsMixed-effects modelMultiplex immunoassay analysisProteomicsQualityR programming languagexMap

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Immunology

Background:

  • Multiplexed immunoassays generate complex data requiring specialized analytical methods.
  • Analyzing fluorescence responses from technologies like Luminex Bio-Plex® xMap is crucial for biomarker discovery.
  • Standardized R methods are needed for reproducible analysis of immunoassay data.

Purpose of the Study:

  • To disclose R methods and source code for analyzing multiplexed immunoassay data.
  • To present techniques for data processing, statistical analysis, and visualization of fluorescence responses.
  • To provide a foundation for rigorous and reproducible bioinformatics analysis of immunoassay results.

Main Methods:

  • Utilized Luminex Bio-Plex® xMap multiplexed immunoassay technology.
  • Employed R methods and source code for analyzing analyte fluorescence response.
  • Applied multinomial regression for covariate balance and fixed/mixed-effect models for differential analysis.
  • Included methods for quality inspection, outlier detection, and plate layout design.

Main Results:

  • Developed and disclosed R methods and source code for comprehensive immunoassay data analysis.
  • Presented strategies for technical replicates, plate design, and covariate matching.
  • Demonstrated statistical approaches for differential analysis and outlier detection.
  • Established a reproducible bioinformatics workflow for fluorescence response data.

Conclusions:

  • The presented R methodology provides a robust framework for analyzing multiplexed immunoassay data.
  • This approach enhances the rigor and reproducibility of biomarker studies in clinical settings.
  • The disclosed methods facilitate accurate interpretation of fluorescence responses for research and diagnostics.