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Related Experiment Videos

Effects of filtering by Present call on analysis of microarray experiments.

Jeanette N McClintick1, Howard J Edenberg

  • 1Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana, USA. jnmcclin@iupui.edu

BMC Bioinformatics
|February 2, 2006
PubMed
Summary

Filtering Affymetrix GeneChip data using detection calls significantly reduces false positives. This method improves the reliability of gene expression profiling by removing unreliable probe sets while retaining significant findings.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Affymetrix GeneChips are extensively used for gene expression profiling.
  • High-throughput analysis can lead to an increased number of false positives.
  • Quantitative evaluation of methods to reduce false positives is limited.

Purpose of the Study:

  • To evaluate a simple method using Affymetrix microarray suite version 5 (MAS5) detection calls to reduce false positives in gene expression data.
  • To compare this detection call filtering method with filtering by expression level.
  • To assess the impact of different thresholds and experiment sizes on data analysis.

Main Methods:

  • A threshold was applied to the fraction of arrays with a 'Present' detection call within treatment groups.
  • Probe sets with 'Absent' calls were removed prior to statistical comparisons.

Related Experiment Videos

  • The method was evaluated using permutations to estimate false positives and compared with the Robust Multichip Average (RMA) algorithm.

    • Main Results:

    • Filtering by 'Present' calls effectively removed unreliable probe sets while retaining significant ones (p <= 0.001).
    • This approach improved the false discovery rate and preferentially retained biologically relevant probe sets.
    • Smaller experiments (3-5 samples) benefited more from stricter filtering thresholds (>=50% Present).

    Conclusions:

    • Using a threshold fraction of 'Present' calls is a simple and effective method to enhance the reliability of gene expression analysis.
    • This approach successfully eliminated unreliable probe sets, increasing the ratio of true to false positives.
    • The method preserves significant findings and probe sets with differential expression, making it valuable for genomic studies.