An efficient method to identify differentially expressed genes in microarray experiments.

@article{citeulike:2764374, abstract = {MOTIVATION: Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss. RESULTS: We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes. AVAILABILITY: The C++ code to implement the proposed method is available upon request for academic use. CONTACT: shuzhang@mtu.edu.}, address = {Department of Mathematical Sciences and Biotechnology Research Center, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA and Department of Mathematics, Heilongjiang University, Harbin 150080, China.}, author = {Qin, Huaizhen and Feng, Tao and Harding, Scott A A. and Tsai, Chung-Jui J. and Zhang, Shuanglin }, citeulike-article-id = {2764374}, doi = {10.1093/bioinformatics/btn215}, issn = {1460-2059}, journal = {Bioinformatics (Oxford, England)}, keywords = {differentially, expressed, genes, microarray}, month = {May}, posted-at = {2008-07-07 08:46:20}, priority = {4}, title = {An efficient method to identify differentially expressed genes in microarray experiments.}, url = {http://dx.doi.org/10.1093/bioinformatics/btn215}, year = {2008} }

TY - JOUR ID - citeulike:2764374 L3 - citeulike-article-id:2764374 TI - An efficient method to identify differentially expressed genes in microarray experiments. JF - Bioinformatics (Oxford, England) AD - Department of Mathematical Sciences and Biotechnology Research Center, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA and Department of Mathematics, Heilongjiang University, Harbin 150080, China. SN - 1460-2059 N2 - MOTIVATION: Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss. RESULTS: We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes. AVAILABILITY: The C++ code to implement the proposed method is available upon request for academic use. CONTACT: shuzhang@mtu.edu. KW - differentially KW - expressed KW - genes KW - microarray AU - Qin, Huaizhen AU - Feng, Tao AU - Harding, Scott A AU - Tsai, Chung-Jui AU - Zhang, Shuanglin PY - 2008/05/03/ UR - http://dx.doi.org/10.1093/bioinformatics/btn215 ER -