An approach for clustering gene expression data with error information

Background: Clustering of gene expression patterns is a well-studied technique for elucidating trends across large numbers of transcripts and for identifying likely co-regulated genes. Even the best clustering methods, however, are unlikely to provide meaningful results if too much of the data is unreliable. With the maturation of microarray technology, a wealth of research on statistical analysis of gene expression data has encouraged researchers to consider error and uncertainty in their microarray experiments, so that experiments are being performed increasingly with repeat spots per gene per chip and with repeat experiments. One of the challenges is to incorporate the measurement error information into downstream analyses of gene expression data, such as traditional clustering techniques. Results: In this study, a clustering approach is presented which incorporates both gene expression values and error information about the expression measurements. Using repeat expression measurem...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Brian Tjaden
• Keywords: BMCBI 2006, Expression Data, Gene Expression, Gene Expression Data