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Microarray Analysis for Saccharomyces cerevisiae
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Statistical Analysis of Microarray Data.

Ricardo Gonzalo Sanz1, Alex Sánchez-Pla2,3

  • 1Statistics and Bioinformatics Unit (UEB), Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.

Methods in Molecular Biology (Clifton, N.J.)
|May 23, 2019
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Summary
This summary is machine-generated.

This chapter presents a typical workflow for analyzing microarray data using R and Bioconductor. It covers steps from raw data to biological significance analysis, aiding researchers in gene expression studies.

Keywords:
BioconductorDifferential expressionMicroarraysR

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Area of Science:

  • Bioinformatics
  • Biostatistics
  • Genomics

Background:

  • Microarray data analysis is crucial in modern bioinformatics and statistics.
  • Various tools and methods exist for analyzing gene expression data.
  • A standardized workflow enhances reproducibility and efficiency.

Purpose of the Study:

  • To present a comprehensive workflow for microarray data analysis.
  • To demonstrate the application of R and Bioconductor packages for this workflow.
  • To provide a practical guide from raw data to biological significance.

Main Methods:

  • Utilized R and Bioconductor packages for data analysis.
  • Implemented a step-by-step workflow: data reading, quality check, normalization, filtering, gene selection, list comparison, and biological significance analysis.
  • Developed a use case with provided data and code for practical implementation.

Main Results:

  • A complete workflow for microarray data analysis was successfully implemented.
  • Each step of the workflow was demonstrated using R and Bioconductor.
  • The analysis successfully progressed from raw hybridization data to biological significance.

Conclusions:

  • The presented R and Bioconductor workflow offers a robust method for microarray data analysis.
  • This approach facilitates the identification and interpretation of differentially expressed genes.
  • The availability of code and data promotes reproducible research in gene expression studies.