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Microarray Analysis for Saccharomyces cerevisiae
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Genes selection comparative study in microarray data analysis.

Ouafae Kaissi1, Eric Nimpaye2, Tiratha Raj Singh3

  • 1LTI Laboratory, ENSA, Adbelmalek Essaadi University, Tangier, Morocco.

Bioinformation
|February 6, 2014
PubMed
Summary
This summary is machine-generated.

This study compared gene selection methods for DNA Microarray analysis. R/BioConductor with Significance Analysis of Microarrays (SAM) showed superior performance over Fold Change and T-test, unlike results from the Bioinformatics Matlab Toolbox.

Keywords:
Bioinformatics Matlab ToolboxComparative StudyGene selectionMicroarray dataR/BioConductor

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • DNA Microarray Technologies have rapidly advanced, leading to numerous gene selection algorithms.
  • Comparative studies exist, but clarity on method performance across different software tools is lacking.

Purpose of the Study:

  • To compare the performance of three common gene selection methods: Fold Change, T-test, and Significance Analysis of Microarrays (SAM).
  • To evaluate these methods using two distinct Affymetrix datasets and two software environments: Bioinformatics Matlab Toolbox and R/BioConductor.

Main Methods:

  • Utilized Fold Change, T-test, and SAM algorithms for differentially expressed gene selection.
  • Applied these methods to two Affymetrix datasets.
  • Compared results using R/BioConductor and Bioinformatics Matlab Toolbox.

Main Results:

  • R/BioConductor demonstrated superior performance for SAM compared to Fold Change and T-test, based on sensitivity and specificity.
  • No significant performance differences were observed among the three methods when using the Bioinformatics Matlab Toolbox.
  • Receiver Operating Characteristic (ROC) curve analysis favored R/BioConductor with SAM for microarray selection.

Conclusions:

  • R/BioConductor combined with SAM is recommended for gene selection in microarray analysis for improved sensitivity and specificity.
  • The choice of software significantly impacts the comparative performance of gene selection algorithms.