Department of Biological Sciences, Sookmyung Women's University, Seoul, Republic of Korea.
Bioinformatics. 2010 Jul 15;26(14):1723-30. doi: 10.1093/bioinformatics/btq279. Epub 2010 May 28.
It is expected that individual genes have intrinsically different variability in the global expressional trend among them. Thus, the consideration of gene-specific expressional properties will help us to distinguish target-selective gene expression over non-selective over-expression.
The re-standardization and integration of heterogeneous microarray datasets, available from public databases, have enabled us to determine the global expression properties of individual genes across a wide variety of experimental conditions and samples. The global averages and SDs of expression for each gene in the integrated microarray datasets were found to be intrinsic properties, which were consistent among independent collections of datasets using different microarray platforms. Using the gene-specific intrinsic parameters to rescale the microarray data, we were able to distinguish novel selective gene expression [cartilage oligomeric matrix protein (COMP) and Collagen X] in breast cancer tissues from non-selective over-expression, a difference that has not been detectable by conventional methods.
The web-based tool for GS-LAGE is available at http://lage.sookmyung.ac.kr
预计个体基因在它们之间的全局表达趋势中具有内在的不同可变性。因此，考虑基因特异性表达特性将有助于我们区分选择性表达的靶基因与非选择性过表达。
对来自公共数据库的异质微阵列数据集进行重新标准化和整合，使我们能够确定个体基因在各种实验条件和样本中的全局表达特性。在整合的微阵列数据集中，每个基因的全局平均值和标准差被发现是内在特性，在使用不同微阵列平台的独立数据集集合中是一致的。使用基因特异性内在参数来重新调整微阵列数据，我们能够区分乳腺癌组织中的新型选择性基因表达（软骨寡聚基质蛋白（COMP）和胶原 X）与非选择性过表达，这是常规方法无法检测到的差异。
GS-LAGE 的基于网络的工具可在 http://lage.sookmyung.ac.kr 上获得。
