A robust measure of correlation between two genes on a microarray

Background: The underlying goal of microarray experiments is to identify gene expression patterns across different experimental conditions. Genes that are contained in a particular pathway or that respond similarly to experimental conditions could be co-expressed and show similar patterns of expression on a microarray. Using any of a variety of clustering methods or gene network analyses we can partition genes of interest into groups, clusters, or modules based on measures of similarity. Typically, Pearson correlation is used to measure distance (or similarity) before implementing a clustering algorithm. Pearson correlation is quite susceptible to outliers, however, an unfortunate characteristic when dealing with microarray data (well known to be typically quite noisy.) Results: We propose a resistant similarity metric based on Tukey's biweight estimate of multivariate scale and location. The resistant metric is simply the correlation obtained from a resistant covariance matrix o...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Johanna S. Hardin, Aya Mitani, Leanne Hicks, Brian VanKoten
• Keywords: BMCBI 2007, Gene Expression Patterns, Gene Network, Pearson Correlation