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Multidimensional Normalization to Minimize Plate Effects of Suspension Bead Array Data.

Mun-Gwan Hong1, Woojoo Lee2, Peter Nilsson1

  • 1Affinity Proteomics, Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology , Solna 171 65, Sweden.

Journal of Proteome Research
|August 30, 2016
PubMed
Summary
This summary is machine-generated.

New normalization methods, multidimensional MA (multi-MA) and MA-loess, effectively reduce plate-to-plate variation in antibody suspension bead array assays. These methods improve biomarker discovery by minimizing batch effects without external reference samples.

Keywords:
affinity proteomicsmultiplexed immunoassaysnormalizationplate effect

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Area of Science:

  • Biotechnology
  • Proteomics
  • Bioinformatics

Background:

  • Biobanks enable large-scale biomarker studies.
  • Antibody-based proteomics using suspension bead arrays is a powerful tool for analyzing biological samples.
  • Inter-plate variation necessitates robust normalization methods for high-throughput assays.

Purpose of the Study:

  • To develop and evaluate novel normalization methods for antibody-based proteomic assays.
  • To minimize batch effects and improve data consistency across multiple assay plates.
  • To enhance the reliability of biomarker discovery in large-scale studies.

Main Methods:

  • Development of multidimensional MA (multi-MA) and MA-loess normalization approaches.
  • Application of normalization methods to analyze 384 serum and plasma samples.
  • Utilizing principal component analysis (PCA) to assess the impact of normalization on data clustering.
  • Evaluating correlation profiles between antibody pairs to detect plate effects.

Main Results:

  • Multi-MA normalization successfully eliminated plate-wise clusters identified by PCA.
  • Both MA normalization methods significantly reduced inflated correlations caused by plate effects.
  • Normalization approaches minimized batch effects in antibody suspension bead array assays.
  • Multi-MA restored biomarker associations obscured by plate effects in a simulated study.

Conclusions:

  • Multi-MA and MA-loess normalization effectively address batch effects in high-throughput proteomic assays.
  • These methods improve data quality and facilitate biomarker discovery.
  • The developed normalization approaches, available as an R package (MDimNormn), are applicable to various high-throughput assay data.