Diametrical clustering is a software that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i) re-partitioning the genes and (ii) computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a ‘diametric’ cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method.
KegArray is a Java application that provides an environment for analyzing both transcriptome data (gene expression profiles) and metabolome data (compound profiles). Tightly integrated with the KEGG database, KegArray enables you to easily map those data to KEGG resources including PATHWAY, BRITE and genome maps.
VISDA (VIsual and Statistical Data Analyzer) is a software for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data.
Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines.
Genesis Server is an application server for computation of Hierarchical Clustering, Self Organizing Maps (SOM), k-means Clustering and Support Vector Machines (SVM).
GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, compact and easy to update; (b) it can be used naturally in conjunction with data driven internal validation methods.
ValWorkBench consists of a collection of measures for validation of clustering solutions and algorithms. It has external measures, as the Adjusted Rand index, and internal measures as Figure of Merit, Gap Statistics, Within Cluster Sum Square, Consensus Clustering and more.