A bi-ordering approach to linking gene expression with clinical annotations in gastric cancer

Background: In the study of cancer genomics, gene expression microarrays, which measure thousands of genes in a single assay, provide abundant information for the investigation of interesting genes or biological pathways. However, in order to analyze the large number of noisy measurements in microarrays, effective and efficient bioinformatics techniques are needed to identify the associations between genes and relevant phenotypes. Moreover, systematic tests are needed to validate the statistical and biological significance of those discoveries. Results: In this paper, we develop a robust and efficient method for exploratory analysis of microarray data, which produces a number of different orderings (rankings) of both genes and samples (reflecting correlation among those genes and samples). The core algorithm is closely related to biclustering, and so we first compare its performance with several existing biclustering algorithms on two real datasets - gastric cancer and lymphoma datase...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Fan Shi, Christopher Leckie, Geoff MacIntyre, Izhak Haviv, Alex Boussioutas, Adam Kowalczyk
• Keywords: BMCBI 2010, Efficient Method, Gastric Cancer, Gene Expression Microarrays