A two-step strategy for detecting differential gene expression in cDNA microarray data.
A mixed-model approach is proposed for identifying differential gene expression in cDNA microarray experiments. This approach is implemented by two interconnected steps. In the first step, we choose a subset of genes that are potentially expressed differentially among treatments with a loose criterion. In the second step, these potential genes are used for further analyses and data-mining with a stringent criterion, in which differentially expressed genes (DEGs) are confirmed and some quantities of interest (such as gene x treatment interaction) are estimated. By simulating datasets with DEGs, we compare our statistical method with a widely used method, the t-statistic, for single genes. Simulation results show that our approach produces a high power and a low false discovery rate for DEG identification. We also investigate the impacts of various source variations resulting from microarray experiments on the efficiency of DEG identification. Analysis of a published experiment studying unstable transcripts in Arabidopsis illustrates the utility of our method. Our method identifies more novel and biologically interesting unstable transcripts than those reported in the original literature.
Lu
Yan
Y
Institute of Bioinformatics, Zhejiang University, Hangzhou, 310029, Peoples Republic of China.
Zhu
Jun
J
Liu
Pengyuan
P
D017360:Arabidopsis / Q000235:genetics; D018076:DNA, Complementary / Q000235:genetics*; D015870:Gene Expression; D017343:Genes, Plant; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis
D018076:DNA, Complementary