Identifying differentially expressed genes in dye-swapped microarray experiments of small sample size

I. B. Lian, C. J. Chang, Y. J. Liang, M. J. Yang, C. S.J. Fann

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

When using microarray analysis to determine gene dependence, one of the goals is to identify differentially expressed genes. However, the inherent variations make analysis challenging. We propose a statistical method (SRA, swapped and regression analysis) especially for dye-swapped design and small sample size. Under general assumptions about the structure of the channels, scanner, and target effects from the experiment, we prove that SRA removes bias caused by these effects. We compare our method with ANOVA, using both simulated and real data. The results show that SRA has consistent sensitivity for the identification of differentially expressed genes in dye-swapped microarrays, particularly when the sample size is small. The program for the proposed method is available at http://www.ibms.sinica.edu.tw/∼csjfann/firstflow/program.htm.

Original languageEnglish
Pages (from-to)2602-2620
Number of pages19
JournalComputational Statistics and Data Analysis
Volume51
Issue number5
DOIs
Publication statusPublished - 2007 Feb 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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