Effect of pooling samples on the efficiency of comparative studies using microarrays.
BACKGROUND
Many biomedical experiments are carried out by pooling individual biological samples. However, pooling samples can potentially hide biological variance and give false confidence concerning the data significance. In the context of microarray experiments for detecting differentially expressed genes, recent publications have addressed the problem of the efficiency of sample pooling, and some approximate formulas were provided for the power and sample size calculations. It is desirable to have exact formulas for these calculations and have the approximate results checked against the exact ones. We show that the difference between the approximate and the exact results can be large.


RESULTS
In this study, we have characterized quantitatively the effect of pooling samples on the efficiency of microarray experiments for the detection of differential gene expression between two classes. We present exact formulas for calculating the power of microarray experimental designs involving sample pooling and technical replications. The formulas can be used to determine the total number of arrays and biological subjects required in an experiment to achieve the desired power at a given significance level. The conditions under which pooled design becomes preferable to non-pooled design can then be derived given the unit cost associated with a microarray and that with a biological subject. This paper thus serves to provide guidance on sample pooling and cost-effectiveness. The formulation in this paper is outlined in the context of performing microarray comparative studies, but its applicability is not limited to microarray experiments. It is also applicable to a wide range of biomedical comparative studies where sample pooling may be involved.
Zhang
Shu-Dong
SD
MRC Toxicology Unit Hodgkin Building, Lancaster Road University of Leicester, Leicester, UK. sdz1@le.ac.uk
Gant
Timothy W
TW
D019295:Computational Biology; D003365:Costs and Cost Analysis; D020869:Gene Expression Profiling / Q000191:economics / Q000706:statistics & numerical data; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000191:economics / Q000706:statistics & numerical data*; D018401:Sample Size