SVD (“eigen-clustering”) of microarray data

We have worked on clustering methods for microarray analysis which allow multiple membership of genes in clusters. In particular, we used spectral analysis methods, such as Singular Value Decomposition (SVD), to uncover global patterns of gene expression. We also looked at other methods such as association rule mining, fuzzy clustering, and the general systems problem solver. We wrote a “manual” for how to use SVD and the related principal component analysis, for microarray data, which included a overview of the method and some insights about its relationship to Fourrier analysis [Wall, Rechtsteiner, and Rocha ,2003]. We also applied this method to uncover novel expression patterns in human cells subjected to Human Cytomegalovirus (Herpes) infection [Challacombe, et al,2004] in a collaboration with the life sciences division at Los Alamos and the Shenk lab at Princeton University.

Singular value decomposition of microarray data: example data from human cells subjected to Human Cytomegalovirus (Herpes) infection.Challacombe, et al,(2004).

Two relevant clusters of co-expressed genes are identified. Above: Two first eigengenes shown. Below: Correlation plot of all genes on the space of the two first eigenassays shown.




Project Members

Luis Rocha

Andreas Rechtsteiner




Selected Project Publications