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Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable

Jiebiao Wang1, Pei Wang2, Donald Hedeker1

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

Biostatistics (Oxford, England)
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Summary
This summary is machine-generated.

We developed multivariate models to analyze complex proteomics data, addressing batch effects and missing values. These models improve statistical efficiency and biological interpretation for breast cancer research.

Keywords:
Alternating direction method of multipliersExpectation-maximization algorithmGraphical lassoMissing not at randomMultivariate mixed-effects modelsProteomicsSelection model

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Area of Science:

  • Quantitative proteomics
  • Mass spectrometry
  • Biostatistics

Background:

  • Mass tag labeling enhances protein profiling efficiency in mass spectrometry.
  • Batch processing in proteomics introduces significant batch effects and missing data.
  • Existing methods struggle to jointly analyze correlated peptides/proteins while accounting for these challenges.

Purpose of the Study:

  • To develop novel statistical models for analyzing labeled proteomics data.
  • To address batch effects and non-ignorable missingness in high-throughput proteomic studies.
  • To improve statistical efficiency and biological interpretation by leveraging multivariate correlations.

Main Methods:

  • Developed two multivariate MIxed-effects SElection (mvMISE) models.
  • Employed a factor-analytic random effects structure for intra-protein peptide correlations.
  • Utilized a graphical lasso penalty on the error precision matrix for inter-protein pathway correlations.
  • Implemented an efficient algorithm using the alternating direction method of multipliers.

Main Results:

  • Simulations confirmed the advantages of the proposed mvMISE models.
  • Applied methods to breast cancer data, identifying key phosphoproteins and pathways.
  • Differentiated activity patterns in triple-negative breast tumors compared to others.

Conclusions:

  • The mvMISE models effectively handle batch effects and missing data in quantitative proteomics.
  • Multivariate analysis enhances statistical power and biological insights in complex datasets.
  • The methods are applicable to various high-dimensional multivariate analyses with clustered or missing data.