DNA-Chip Analyzer (dChip) (http://www.hsph.harvard.edu/cli/complab/dchip/) program was used to obtain model based gene expression value (measures the fluorescence intensity of that gene), then the class neighbors analysis standardizes the expression values for each gene by linearly adjusting their values across all samples to a mean of zero with a standard deviation of one.
The pathway based gene expression similarity measuring method outperforms commonly used similarity measuring methods.
For this purpose, we devised a pathway based gene expression similarity measuring method.
To evaluate pathway based gene expression similarity measuring method, we conducted cell type classification test.
The pathway based gene expression similarity measuring methods, PEPC, we suggested here, compute pathway level expression by averaging expression of genes mapped to pathway.
Considering the fact that public microarray database is consist of gene expression profiles of various experiments with various type of platform, pathway based gene expression similarity measuring method could be successfully applied for searching large public microarray databases.
First, we calculated the scaled gene expression score by summing up the log2 based gene expression ratios of the 87 cortex transcripts well measured in the 177 cRCCs while taking into account the direction of the gene expression.
As previously developed pathway based gene expression analysis methods were successfully improve intact gene expression based analysis methods [ 20- 23], pathway based similarity measuring method outperformed conventional method.
To make cross-platform gene expression profiles search possible, they transformed all gene expression profiles to Unigene ID based gene expression profiles, averaging expression values of genes for corresponding UniGene ID if multiple genes are mapped to a single UniGene ID, then measured similarity between expression profiles using Spearman rank correlation coefficient.
Owing to sequence incompatibilities in cross-species hybridization, the lists of genes selected based on gene expression measures may be biased against rapidly evolving genes and therefore functional categories attributable to such genes may appear as underrepresented in our analysis.
