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

  • Authors:

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

  • Affiliations:

  • Department of Mathematics, National Changhua University of Education, Changhua 50058, Taiwan and Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan;Graduate Institute of Medical Science, Chang Gung University, Taipei, Taiwan;Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan;Department of Mathematics, National Changhua University of Education, Changhua 50058, Taiwan;Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2007

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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.