Factorial and time course designs for cDNA microarray experiments.
Microarrays are powerful tools for surveying the expression levels of many thousands of genes simultaneously. They belong to the new genomics technologies which have important applications in the biological, agricultural and pharmaceutical sciences. There are myriad sources of uncertainty in microarray experiments, and rigorous experimental design is essential for fully realizing the potential of these valuable resources. Two questions frequently asked by biologists on the brink of conducting cDNA or two-colour, spotted microarray experiments are 'Which mRNA samples should be competitively hybridized together on the same slide?' and 'How many times should each slide be replicated?' Early experience has shown that whilst the field of classical experimental design has much to offer this emerging multi-disciplinary area, new approaches which accommodate features specific to the microarray context are needed. In this paper, we propose optimal designs for factorial and time course experiments, which are special designs arising quite frequently in microarray experimentation. Our criterion for optimality is statistical efficiency based on a new notion of admissible designs; our approach enables efficient designs to be selected subject to the information available on the effects of most interest to biologists, the number of arrays available for the experiment, and other resource or practical constraints, including limitations on the amount of mRNA probe. We show that our designs are superior to both the popular reference designs, which are highly inefficient, and to designs incorporating all possible direct pairwise comparisons. Moreover, our proposed designs represent a substantial practical improvement over classical experimental designs which work in terms of standard interactions and main effects. The latter do not provide a basis for meaningful inference on the effects of most interest to biologists, nor make the most efficient use of valuable and limited resources.
Glonek
G F V
GF
School of Applied Mathematics, The University of Adelaide, Adelaide, SA 5005, Australia.
Solomon
P J
PJ
D000818:Animals; D003627:Data Interpretation, Statistical; D007938:Leukemia / Q000235:genetics; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards; D016187:Receptors, Granulocyte-Macrophage Colony-Stimulating Factor / Q000235:genetics; D012107:Research Design / Q000592:standards
D016187:Receptors, Granulocyte-Macrophage Colony-Stimulating Factor