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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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A first principles approach to differential expression in microarray data analysis.

Robert A Rubin1

  • 1Mathematics Department, Whittier College, 13406 E. Philadelphia St., Whittier, CA 90608, USA. brubin698@earthlink.net

BMC Bioinformatics
|September 18, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a simple median analysis of variance (ANOVA) method for Affymetrix microarray analysis. This approach offers comparable performance to existing methods and identifies novel "unanticipated positives" and "unanticipated negatives" in gene expression data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Analysis

Background:

  • Affymetrix microarray experiments often yield disparate results due to varied probe set and probe level models.
  • Existing methods rely on specific models, introducing potential biases and inconsistencies.
  • A need exists for a robust differential expression analysis method with minimal assumptions.

Purpose of the Study:

  • To develop a novel, assumption-light methodology for determining differential gene expression in Affymetrix microarray data.
  • To provide a gene-level measure of differential expression using a median of analysis of variance (ANOVA) results.
  • To compare the performance of the new method against existing techniques.

Main Methods:

  • The proposed method employs analysis of variance (ANOVA) across conditions for each probe position.
  • It requires only that probe amplitudes are independent and identically distributed under the null hypothesis.
  • Log-amplitudes are standardized within-chip, and the median of (1-p) values serves as the gene-level differential expression measure.

Main Results:

  • The median ANOVA (1-p) approach was applied to HGU-133A, HG-U95A, and "Golden Spike" spike-in datasets.
  • Receiver operating characteristic (ROC) curves demonstrated favorable comparison with published results.
  • The method revealed "unanticipated positives" – probe sets strongly suggesting differential expression.

Conclusions:

  • The median ANOVA (1-p) method is a simple, model-independent approach for microarray data analysis.
  • Its performance is comparable to existing methods on standard spike-in datasets.
  • The study identified "unanticipated positives" and "unanticipated negatives," highlighting their importance in interpreting "truthed" test beds.