Genomics Inform.  2006 Sep;4(3):110-117.

Significant Gene Selection Using Integrated Microarray Data Set with Batch Effect

  • 1Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, Korea.
  • 2Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
  • 3Brain Korea 21 Project for Medical Science, Yonsei University College of Medicine, Seoul, Korea.
  • 4Cancer Metastasis Research Center, Yonsei University College of Medicine, Seoul, Korea.
  • 5Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea.


In microarray technology, many diverse experimental features can cause biases including RNA sources, microarray production or different platforms, diverse sample processing and various experiment protocols. These systematic effects cause a substantial obstacle in the analysis of microarray data. When such data sets derived from different experimental processes were used, the analysis result was almost inconsistent and it is not reliable. Therefore, one of the most pressing challenges in the microarray field is how to combine data that comes from two different groups. As the novel trial to integrate two data sets with batch effect, we simply applied standardization to microarray data before the significant gene selection. In the gene selection step, we used new defined measure that considers the distance between a gene and an ideal gene as well as the between-slide and within-slide variations. Also we discussed the association of biological functions and different expression patterns in selected discriminative gene set. As a result, we could confirm that batch effect was minimized by standardization and the selected genes from the standardized data included various expression pattems and the significant biological functions.


genomic data; integration; batch effect; bioinformatics
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