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. 2005 Jun 21;102(25):8961-5.
doi: 10.1073/pnas.0502674102. Epub 2005 Jun 10.

Effects of threshold choice on biological conclusions reached during analysis of gene expression by DNA microarrays

Kuang-Hung Pan  1 , Chih-Jian Lih, Stanley N Cohen
Affiliations

Effects of threshold choice on biological conclusions reached during analysis of gene expression by DNA microarrays

Kuang-Hung Pan et al. Proc Natl Acad Sci U S A. .

Abstract

Global analysis of gene expression by using DNA microarrays is employed increasingly to search for differences in biological properties between normal and diseased tissue. In such studies, expression that deviates from defined thresholds commonly is used for creating genetic signatures that characterize disease vs. normality. Although it is axiomatic that the threshold parameters applied to microarray analysis will alter the contents of such genetic signatures, the extent to which threshold choice can affect the fundamental conclusions made from microarray-based studies has not been elucidated. We used GABRIEL (Genetic Analysis By Rules Incorporating Expert Logic), a platform of knowledge-based algorithms for the global analysis of gene expression, together with conventional statistical approaches, to examine the sensitivity of conclusions to threshold choice in recently published microarray-based studies. An analysis of the effects of threshold decisions in one of these studies [Ramaswamy, S., Ross, K. N., Lander, E. S. & Golub, T. R. (2003) Nat. Genet. 33, 49-54], which arrived at the important conclusion that the metastatic potential of primary tumors is encoded by the bulk of cells in the tumor, is the focus of this article. We discovered that support for this conclusion highly depends on the threshold used to create gene expression signatures. We also found that threshold choice dramatically affected the gene function categories represented nonrandomly in signatures. Our results suggest that the robustness of biological conclusions made by using microarray analysis should be routinely assessed by examining the validity of the conclusions by using a range of threshold parameters.

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Figures

Fig. 1.
Fig. 1.
Kaplan–Meier survival analysis of the clusters of lung adenocarcinoma individuals defined by the gene signature of different sizes. The dashed line represents the individuals without the metastasis signature delineated by hierarchical clustering, and the solid line represents the individuals with the metastasis signature. The plots are generated by the winstat software (R. Fitch Software, Staufen, Germany). Shown are the clusters for the following gene signatures: 128 (a), 256 (b), 512 (c), 1,024 (d), and 17 (e).
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