Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity.
Gene expression signatures from microarray experiments promise to provide important prognostic tools for predicting disease outcome or response to treatment. A number of microarray studies in various cancers have reported such gene signatures. However, the overlap of gene signatures in the same disease has been limited so far, and some reported signatures have not been reproduced in other populations. Clearly, the methods used for verifying novel gene signatures need improvement. In this article, we describe an experiment in which microarrays and sample hybridization are designed according to the statistical principles of randomization, replication and blocking. Our results show that such designs provide unbiased estimation of differential expression levels as well as powerful tests for them.
Hsu
Jason C
JC
Department of Statistics, The Ohio State University, 1958 Neil Avenue Columbus, Ohio 43210, USA. Hsu.1@osu.edu
Chang
Jane
J
Wang
Tao
T
Steingrímsson
Eiríkur
E
Magnússon
Magnús Karl
MK
Bergsteinsdottir
Kristin
K
D001943:Breast Neoplasms / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data*; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards; D011897:Random Allocation; D015203:Reproducibility of Results; D012107:Research Design / Q000592:standards; D012680:Sensitivity and Specificity