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A MIXED-EFFECTS MODEL FOR INCOMPLETE DATA FROM LABELING-BASED QUANTITATIVE PROTEOMICS EXPERIMENTS.

Lin S Chen1, Jiebiao Wang1, Xianlong Wang2

  • 1Department of Public Health Sciences, University of Chicago, 5841 S Maryland Ave, Chicago, Illinois, USA.

The Annals of Applied Statistics
|May 11, 2018
PubMed
Summary

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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This summary is machine-generated.

New mixEMM method accurately analyzes isobaric tag for relative and absolute quantitation (iTRAQ) and tandem mass tags (TMT) proteomics data, addressing batch effects and missing data. It improves parameter estimation and inference for reliable high-throughput protein profiling.

Area of Science:

  • Proteomics
  • Biostatistics
  • Computational Biology

Background:

  • Isobaric labeling techniques like iTRAQ and TMT are vital for high-throughput proteomics.
  • Batch effects and nonignorable missing data are significant challenges in iTRAQ/TMT data analysis.
  • Existing methods often fail to adequately address these issues, leading to inaccurate results.

Purpose of the Study:

  • To introduce mixEMM, a novel method for analyzing iTRAQ/TMT proteomics data.
  • To specifically address batch effects and batch-level abundance-dependent missing data (BADMM).
  • To improve the accuracy of parameter estimation and statistical inference in quantitative proteomics.

Main Methods:

  • Development of the mixEMM method utilizing a linear mixed-effects model.
Keywords:
Batch-level Abundance-Dependent Missing-data Mechanism (BADMM)Mixed-effects modelsthe expectation-conditional-maximization ( ECM) algorithm

Related Experiment Videos

  • Explicit modeling of batch effects and the BADMM.
  • Simulation studies to compare mixEMM with existing approaches.

    • Main Results:

    • mixEMM demonstrated more accurate parameter estimation and inference compared to existing methods.
    • The method effectively handles batch effects and nonignorable missing data.
    • Application to breast cancer iTRAQ data identified differentially expressed phosphopeptides.

    Conclusions:

    • mixEMM provides a robust solution for analyzing complex iTRAQ/TMT proteomics data.
    • The method enhances the reliability of high-throughput protein profiling.
    • mixEMM is applicable to general clustered data with cluster-level nonignorable missing data mechanisms.