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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Avoiding pitfalls in gene (co)expression meta-analysis.

Gabriel Ostlund1, Erik L L Sonnhammer2

  • 1Stockholm Bioinformatics Centre, Science for Life Laboratory, Box 1031, SE-17121 Solna, Sweden; Department of Biochemistry and Biophysics, Stockholm University, Sweden.

Genomics
|November 5, 2013
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Differential gene expression analysis reliability depends on microarray platform and metrics. Differential coexpression requires large sample sizes for consistent results in disease research.

Keywords:
Cancer gene expressionDifferential coexpressionDifferential expressionMicroarray data processing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Differential gene expression (DGE) analysis is crucial for understanding disease at a molecular level.
  • Numerous experimental and bioinformatics techniques exist, but their impact on DGE reliability is understudied.
  • Comparative analysis of clinical expression data is needed to assess the consistency of DGE findings.

Purpose of the Study:

  • To systematically evaluate the reliability and agreement of differential gene expression and coexpression analyses.
  • To compare the influence of different background corrections and differential expression metrics on study results.
  • To provide practical recommendations for gene (co)expression analysis in clinical studies.

Main Methods:

  • Performed a large-scale comparative analysis of clinical expression data.
  • Utilized several background correction methods and differential expression metrics.
  • Analyzed agreement between study pairs (same cancer type, different cancer types, cancer vs. non-cancer).
  • Replicated analyses using differential coexpression methods.

Main Results:

  • Agreement in differential expression is predominantly influenced by the microarray platform used.
  • Differential coexpression analysis requires substantial sample sizes to achieve reliable agreement.
  • Studies using different differential expression metrics may show poor agreement, even with the same metric.

Conclusions:

  • Microarray platform choice significantly impacts differential gene expression results.
  • Large sample sizes are essential for robust differential coexpression analysis.
  • Careful selection of metrics and platforms is recommended for reliable gene (co)expression studies.