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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Systematic order-dependent effect in expression values, variance, detection calls and differential expression in

Kathe E Bjork¹, Karen Kafadar

¹Department of Mathematical Sciences, University of Colorado at Denver and Health Sciences Center, Denver, CO 80217, USA. Kathe.bjork@cudenver.edu

Bioinformatics (Oxford, England)

|September 28, 2007

Summary

Gene expression data from Affymetrix GeneChips show an unexpected order dependence. This bias affects analysis results, impacting differential expression and clustering evaluations in human and mouse studies.

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Area of Science:

Genomics
Bioinformatics
Gene Expression Analysis

Background:

Affymetrix GeneChips are widely used for gene expression quantification.
Analyzing gene expression data requires robust normalization and comparison methods.
Understanding data characteristics is crucial for accurate biological interpretation.

Purpose of the Study:

To characterize features of Affymetrix GeneChip data.
To evaluate analyses for differential expression, regulation, and clustering.
To identify potential biases in gene expression profiling.

Main Methods:

Utilized publicly available human and mouse gene expression datasets.
Analyzed data processed and normalized with Affymetrix MAS5.0 and Robust Multi-array Average (RMA) methods.
Investigated order dependence across various GeneChip platforms.

Main Results:

Discovered an unexpected order dependence in GeneChip expression data.
This order dependence affected relative expression measures and detection calls.
The effect was observed across multiple human and mouse datasets and chip types.

Conclusions:

Order dependence in GeneChip data can significantly bias experimental results.
Standard normalization methods do not fully correct for this observed bias.
Researchers should be aware of this phenomenon when analyzing GeneChip expression data.