Merged consensus clustering to assess and improve class discovery with microarray data

Background: One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a large number of methods available to perform clustering, but it is often unclear which method is best suited to the data and how to quantify the quality of the classifications produced. Results: Here we describe an R package containing methods to analyse the consistency of clustering results from any number of different clustering methods using resampling statistics. These methods allow the identification of the the best supported clusters and additionally rank cluster members by their fidelity within the cluster. These metrics allow us to compare the performance of different clustering algorithms under different experimental conditions and to select those that produce the most reliable clustering structures. We show the application of this method to simulated data, canonical gene expression experiments and ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: T. Ian Simpson, J. Douglas Armstrong, Andrew P. Jarman
• Keywords: Averaged Clustering Robustness, BMCBI 2010, Business, Clustering Methods, Reliable Clustering Structures