Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity.

@article{citeulike:1405956, abstract = {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.}, address = {Department of Statistics, The Ohio State University, 1958 Neil Avenue Columbus, Ohio 43210, USA. Hsu.1@osu.edu}, author = {Hsu, J. C. and Chang, J. and Wang, T. and Steingr\'{i}msson, E. and Magn\'{u}sson, M. K. and Bergsteinsdottir, K. }, citeulike-article-id = {1405956}, doi = {10.1093/bib/bbl023}, issn = {1467-5463}, journal = {Brief Bioinform}, keywords = {experiment\_design, review}, month = {January}, number = {1}, pages = {22--31}, posted-at = {2007-07-19 16:10:05}, priority = {2}, title = {Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity.}, url = {http://dx.doi.org/10.1093/bib/bbl023}, volume = {8}, year = {2007} }

TY - JOUR ID - citeulike:1405956 L3 - citeulike-article-id:1405956 TI - Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity. JF - Brief Bioinform VL - 8 IS - 1 SP - 22 EP - 31 AD - Department of Statistics, The Ohio State University, 1958 Neil Avenue Columbus, Ohio 43210, USA. Hsu.1@osu.edu SN - 1467-5463 N2 - 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. KW - experiment_design KW - review AU - Hsu, JC AU - Chang, J AU - Wang, T AU - Steingrímsson, E AU - Magnússon, MK AU - Bergsteinsdottir, K PY - 2007/01// UR - http://dx.doi.org/10.1093/bib/bbl023 ER -