Characterization of variability in large-scale gene expression data: implications for study design.
Large-scale gene expression measurement techniques provide a unique opportunity to gain insight into biological processes under normal and pathological conditions. To interpret the changes in expression profiles for thousands of genes, we face the nontrivial problem of understanding the significance of these changes. In practice, the sources of background variability in expression data can be divided into three categories: technical, physiological, and sampling. To assess the relative importance of these sources of background variation, we generated replicate gene expression profiles on high-density Affymetrix GeneChip oligonucleotide arrays, using either identical RNA samples or RNA samples obtained under similar biological states. We derived a novel measure of dispersion in two-way comparisons, using a linear characteristic function. When comparing expression profiles from replicate tests using the same RNA sample (a test for technical variability), we observed a level of dispersion similar to the pattern obtained with RNA samples from replicate cultures of the same cell line (a test for physiological variability). On the other hand, a higher level of dispersion was observed when tissue samples of different animals were compared (an example of sampling variability). This implies that, in experiments in which samples from different subjects are used, the variation induced by the stimulus may be masked by non-stimuli-related differences in the subjects' biological state. These analyses underscore the need for replica experiments to reliably interpret large-scale expression data sets, even with simple microarray experiments.
Novak
Jaroslav P
JP
Montreal Genome Centre, McGill University Health Centre, 1650 Cedar Avenue, Montréal, Québec, Canada H3G 1A4.
Sladek
Robert
R
Hudson
Thomas J
TJ
D000818:Animals; D020869:Gene Expression Profiling; D014644:Genetic Variation; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012313:RNA; D015203:Reproducibility of Results; D012107:Research Design / Q000639:trends*; D014407:Tumor Cells, Cultured
D012313:RNA