Sample selection for microarray gene expression studies.
OBJECTIVE
The choice of biomedical samples for microarray gene expression studies is decisive for both validity and interpretability of results. We present a consistent, comprehensive framework to deal with the typical selection problems in microarray studies.


METHODS
Microarray studies are designed either as case-control studies or as comparisons of parallel groups from cohort studies, since high levels of random variation in the experimental approach thwart absolute measurements of gene expression levels. Validity and results of gene expression studies heavily rely on the appropriate choice of these study groups. Therefore, the so-called principles of comparability, which are well known from both clinical and epidemiological studies, need to be applied to microarray experiments.


RESULTS
The principles of comparability are the study-base principle, the principle of deconfounding and the principle of comparable accuracy in measurements. We explain each of these principles, show how they apply to microarray experiments, and illustrate them with examples. The examples are chosen as to represent typical stumbling blocks of microarray experimental design, and to exemplify the benefits of implementing the principles of comparability in the setting of microarray experiments.


CONCLUSIONS
Microarray studies are closely related to classical study designs and therefore have to obey the same principles of comparability as these. Their validity should not be compromised by selection, confounding or information bias. The so-called study-base principle, calling for comparability and thorough definition of the compared cell populations, is the key principle for the choice of biomedical samples and controls in microarray studies.
Repsilber
D
D
Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.
Fink
L
L
Jacobsen
M
M
Bläsing
O
O
Ziegler
A
A
D000465:Algorithms; D000818:Animals; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008432:Mathematical Computing; D008957:Models, Genetic; D009369:Neoplasms / Q000175:diagnosis / Q000453:epidemiology; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D015983:Selection Bias