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Statistical Methods for Analysis of Protein Microarray Data Using R.

Yunro Chung1

  • 1College of Health Solutions & Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ, USA. yunro.chung@asu.edu.

Methods in Molecular Biology (Clifton, N.J.)
|June 11, 2021
PubMed
Summary
This summary is machine-generated.

This chapter details statistical methods for analyzing protein microarray data using R. It covers essential techniques like hypothesis testing and false discovery rate for practical applications.

Keywords:
BiostatisticsCorrelationFalse discovery rateMann-Whitney U testNonparametric testSample size calculationSignificance levelp-Valueq-Valuet-Test

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Area of Science:

  • Biostatistics
  • Bioinformatics
  • Proteomics

Background:

  • Protein microarrays are crucial for high-throughput proteomic analysis.
  • Analyzing complex protein array data requires robust statistical methodologies.
  • Standardized statistical approaches are needed for reproducible research.

Purpose of the Study:

  • To present a comprehensive guide to statistical methods for protein microarray data analysis.
  • To demonstrate practical applications of statistical techniques using R.
  • To provide accessible R code for implementing these analyses.

Main Methods:

  • Application of descriptive statistics for data summarization.
  • Implementation of hypothesis testing for group comparisons.
  • Utilizing false discovery rate control for multiple testing.
  • Employing receiver operating characteristic (ROC) curve analysis for diagnostic performance.
  • Correlation analysis to identify relationships between variables.
  • Data visualization techniques for intuitive data interpretation.
  • Power analysis for experimental design optimization.

Main Results:

  • Demonstration of various statistical methods on a public protein array dataset.
  • Provision of executable R code for all presented statistical analyses.
  • Illustrative examples of applying statistical concepts to real-world proteomic data.

Conclusions:

  • The chapter provides a practical framework for statistical analysis of protein microarray data.
  • R is a versatile tool for implementing advanced statistical methods in proteomics.
  • The provided methods and code facilitate reproducible and robust analysis of proteomic data.