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Updated: Jun 21, 2026

Analysis of Histone Antibody Specificity with Peptide Microarrays
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Visualisation and pre-processing of peptide microarray data.

Marie Reilly1, Davide Valentini

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Methods in Molecular Biology (Clifton, N.J.)
|August 4, 2009
PubMed
Summary
This summary is machine-generated.

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This chapter details processing peptide microarray data in R. It covers data visualization, quality assessment, and normalization using linear mixed models for accurate downstream analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Peptide microarrays generate complex image data requiring specialized processing.
  • Accurate analysis relies on robust data pre-processing and quality control.

Purpose of the Study:

  • To describe methods for reading and pre-processing peptide microarray data using the R statistical package.
  • To illustrate data visualization techniques for quality assessment and outlier identification.
  • To present a normalization strategy using linear mixed models to correct for array-specific artifacts.

Main Methods:

  • Data import into R statistical package.
  • Visualization of spot features, response parameters, and quality metrics.
  • Calculation of log-ratio response index and use of controls for defining detectable and false-positive responses.

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  • Application of linear mixed models for artifact removal and data normalization.

    • Main Results:

    • Methods for assessing data quality and excluding invalid data points are demonstrated.
    • A robust normalization procedure is established, yielding reliable input data for further analysis.
    • The approach facilitates comparative and predictive analyses of peptide microarray data.

    Conclusions:

    • Effective pre-processing and normalization are crucial for reliable peptide microarray data analysis.
    • The described R-based workflow enhances data quality assessment and artifact removal.
    • This methodology supports advanced downstream analyses, including comparative and predictive modeling.