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Bioinformatics. 2007 Nov 15;23(22):3032-8. Epub 2007 Sep 24.

The high-level similarity of some disparate gene expression measures.

Author information

1
Nonclinical Biostatistics, Johnson & Johnson Pharmaceutical Research & Development LLC, Raritan, NJ 08869, USA.

Abstract

Probe-level data from Affymetrix GeneChips can be summarized in many ways to produce probe-set level gene expression measures (GEMs). Disturbingly, the different approaches not only generate quite different measures but they could also yield very different analysis results. Here, we explore the question of how much the analysis results really do differ, first at the gene level, then at the biological process level. We demonstrate that, even though the gene level results may not necessarily match each other particularly well, as long as there is reasonably strong differentiation between the groups in the data, the various GEMs do in fact produce results that are similar to one another at the biological process level. Not only that the results are biologically relevant. As the extent of differentiation drops, the degree of concurrence weakens, although the biological relevance of findings at the biological process level may yet remain.

PMID:
17893087
DOI:
10.1093/bioinformatics/btm448
[Indexed for MEDLINE]

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