Novel and simple transformation algorithm for combining microarray data sets

Background: With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis. Results: Two microarray data sets based on a 17k cDNA microarray system were used, consisting of 82 normal colon mucosa and 72 colorectal cancer tissues. Each data set was prepared from either total RNA or amplified mRNA, and the difference of RNA source between these two data sets was detected by ANOVA (Analysis of variance) model. A simple integration method was introduced which was based on the distributions of gene expression ratios among different microarray da...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Ki-Yeol Kim, Dong Hyuk Ki, Ha Jin Jeong, Hei-Cheul Jeung, Hyun Cheol Chung, Sun Young Rha
• Keywords: BMCBI 2007, Data Sets, Microarray Data, Rna Source