期刊
BIOINFORMATICS
卷 24, 期 14, 页码 1583-1589出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btn215
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资金
- NHGRI NIH HHS [R01 HG003054, R03 HG003613] Funding Source: Medline
- NIGMS NIH HHS [R01 GM069940] Funding Source: Medline
- Division Of Integrative Organismal Sys
- Direct For Biological Sciences [0836433] Funding Source: National Science Foundation
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.
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