<Emphasis Type="Italic">Post hoc</Emphasis> pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data |
| |
Authors: | Randall Hulshizer Eric M Blalock |
| |
Institution: | (1) Department of Molecular and Biomedical Pharmacology, University of Kentucky College of Medicine, Lexington, Kentucky, USA |
| |
Abstract: | Background Researchers using RNA expression microarrays in experimental designs with more than two treatment groups often identify statistically
significant genes with ANOVA approaches. However, the ANOVA test does not discriminate which of the multiple treatment groups
differ from one another. Thus, post hoc tests, such as linear contrasts, template correlations, and pairwise comparisons are used. Linear contrasts and template
correlations work extremely well, especially when the researcher has a priori information pointing to a particular pattern/template among the different treatment groups. Further, all pairwise comparisons
can be used to identify particular, treatment group-dependent patterns of gene expression. However, these approaches are biased
by the researcher's assumptions, and some treatment-based patterns may fail to be detected using these approaches. Finally,
different patterns may have different probabilities of occurring by chance, importantly influencing researchers' conclusions
about a pattern and its constituent genes. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|