Tight clustering: a resampling-based approach for identifying stable and tight patterns in data

GC Tseng, WH Wong - Biometrics, 2005 - academic.oup.com
Biometrics, 2005academic.oup.com
In this article, we propose a method for clustering that produces tight and stable clusters
without forcing all points into clusters. The methodology is general but was initially motivated
from cluster analysis of microarray experiments. Most current algorithms aim to assign all
genes into clusters. For many biological studies, however, we are mainly interested in
identifying the most informative, tight, and stable clusters of sizes, say, 20–60 genes for
further investigation. We want to avoid the contamination of tightly regulated expression …
Summary
In this article, we propose a method for clustering that produces tight and stable clusters without forcing all points into clusters. The methodology is general but was initially motivated from cluster analysis of microarray experiments. Most current algorithms aim to assign all genes into clusters. For many biological studies, however, we are mainly interested in identifying the most informative, tight, and stable clusters of sizes, say, 20–60 genes for further investigation. We want to avoid the contamination of tightly regulated expression patterns of biologically relevant genes due to other genes whose expressions are only loosely compatible with these patterns. “Tight clustering” has been developed specifically to address this problem. It applies K-means clustering as an intermediate clustering engine. Early truncation of a hierarchical clustering tree is used to overcome the local minimum problem in K-means clustering. The tightest and most stable clusters are identified in a sequential manner through an analysis of the tendency of genes to be grouped together under repeated resampling. We validated this method in a simulated example and applied it to analyze a set of expression profiles in the study of embryonic stem cells.
Oxford University Press