In this paper, we propose a clustering algorithm called M-CLUBS (for Microarray data CLustering Using Binary Splitting) exhibiting higher accuracy than the hierarchical ones proposed so far while allowing a faster computation with respect to partition based approaches.
For the microarray data, clustering and statistical analysis were performed using SUMO and Genego (metacore) software packages.
However, more appropriate distance measures accounting for the discreet, Poisson-distributed structure of SAGE data have been shown to produce better clustering results than those achieved with conventional Euclidian or Pearson similarity measures routinely used in microarray data clustering [12].
The dendrogram is one of the traditional approaches to perform microarray data clustering.
Most of the applications discussed in the last section mainly applied tools currently available in microarray data clustering tools such as TIGR MeV [ 35] and GeneSpring [ 42].
Although widely used for the analysis of gene expression microarray data, cluster analysis may not be the most appropriate statistical technique for some study aims.
With Biolisp, among others, one can perform simple biological natural language processing on PubMed, work on sequences, represent and search graphs, produce trees and perform microarray data clustering.
PCA confirmed that the microarray data clustered by sex and by treatment type suggesting that there are significant differences at the level of gene expression between i) male and female pups and ii) control, hyper, hypo and hypo+ pups.
In contrast, as revealed by macroarray and microarray data, clustering of plastid genes of WT plants exposed to various stress and exogenic conditions, which also affect the chloroplast, did not identify two major transcriptionally determined gene clusters, which behave oppositely (Fig. 5A and Supplementary Table S9).
In a first step, the microarray data is clustered by hierarchical agglomerative clustering using standard settings.
