Evaluation of nine strategies for analyzing a cDNA toxicology microarray data set.
Microarray technology with two-color-based cDNA is commonly used for drug development, as well as for a much broader range of biomedical research. Among all the applications, two-group design is probably most commonly used for comparing, e.g., normal and abnormal tissue samples, tissues treated and untreated, or individuals responded and not responded to a drug. Despite the apparent simplicity, there are numerous methods for analyzing such data in a statistically rigorous manner. Here, we discuss nine different analytical strategies, each of which is derived under a set of "reasonable" assumptions. Some of them resemble methods developed for different contexts. In the absence of the truth, investigators should consider underlying assumptions before taking one or more of these strategies for analyzing data from a particular experiment. The issue here is what are the similarities and differences between these analytical strategies. We present these strategies in the context of an actual microarray experiment performed at the U.S. Food and Drug Administration.
Zhang
Juan Joanne
JJ
Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Rockville, Maryland 20857, USA. zhangjua@cder.fda.gov
Yi
Tsong
T
Zhao
Lue Ping
LP
D000465:Algorithms; D000818:Animals; D018076:DNA, Complementary / Q000187:drug effects* / Q000235:genetics; D003627:Data Interpretation, Statistical; D005786:Gene Expression Regulation / Q000187:drug effects / Q000235:genetics; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D051381:Rats; D012107:Research Design; D014116:Toxicology / Q000706:statistics & numerical data*; D014481:United States; D014486:United States Food and Drug Administration
D018076:DNA, Complementary