Robust cluster analysis of microarray gene expression data with the number of clusters determined biologically.
BACKGROUND
The success of each method of cluster analysis depends on how well its underlying model describes the patterns of expression. Outlier-resistant and distribution-insensitive clustering of genes are robust against violations of model assumptions.


RESULTS
A measure of dissimilarity that combines advantages of the Euclidean distance and the correlation coefficient is introduced. The measure can be made robust using a rank order correlation coefficient. A robust graphical method of summarizing the results of cluster analysis and a biological method of determining the number of clusters are also presented. These methods are applied to a public data set, showing that rank-based methods perform better than log-based methods.


BACKGROUND
Software is available from http://www.davidbickel.com.
Bickel
David R
DR
Medical College of Georgia, Office of Biostatistics and Bioinformatics, 1120 Fifteenth St, AE-3037 Augusta 30912-4900, USA. dbickel@mail.mcg.edu
D000465:Algorithms; D001483:Base Sequence; D016000:Cluster Analysis; D014450:Electron Transport Complex III / Q000235:genetics; D003576:Electron Transport Complex IV / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011786:Quality Control; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
D003576:Electron Transport Complex IV; D014450:Electron Transport Complex III