Summary
Objectives:
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 micro-array 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.
Keywords
Confounding - gene expression profiling - biometry - research design - selection bias
- microarray studies
