Genes: Groups/clusters of genes can be created using a number of different biological characteristics (proposed gene function, genome location, co-expression).
Experiments: Details of different experiments can be viewed. If required the experiment data can be transformed, constrained or filtered. Alternatively a covariance (or co-occurance) analysis can be performed.
Models: Mixtures of models can be used to categorize experiments. These models can be to compare results against a 'reference' set of experiments.
Graphs and Hierarchies: Graphs and hierarchies of genes can be constructed and edited. These can be used to generate clusters of genes based on function, location or expression.
Gene Functions: Shows details about gene functions (defined using an ontology). The function information can be used in conjunction with other information (e.g. location or expression data) to construct graphs, hierarchies or refine clusters.
Gene Locations: Genome feature locations on chromosomes/contigs can be used to identify co-regulated genes. This information can be visualised and used to construct both graphs and models.
Gene Views: Gene Table gives information about genes, their individual experiment expression value, associated annotations and any descriptions; and Gene List Editor allows for the editing of sets of genes
Gene Expression Visualisations: Scatter plots are provided for examining covariance (bivariance) between the experimental data sets; Histograms are provided for looking at the distribution of the data; and Parallel projections to show all the the expression profiles.
Gene Variance Visualisations: Gene Spectrums which show the variance that occurs in a single expression experiment; and Gene Clouds which can be used to compare the variance that occurs within two experiments
Cluster Analysis Visualisations: Cluster comparison tool is provided to compare the results of two different cluster analyses or the results of a cluster analysis against a biological relevant categorization (e.g. cell cycle, disease state, regulatory networks information); and Cluster viewers are provided to examine differences/similarities between the clusters resulting from an individual analysis/algorithm.
Gene Ontology Visualisations: TreeMap visualisations for examination of scores associated with concepts (e.g. number of assigned instances); Ontology Graph visualisations for the analysis of interrelationships within the ontology; and Table Visualisations for the viewing and editing of the specific concept details
Model and Hierarchy Visualisations: Probability Model visualisations are provided for exploring the results of a mixture model analysis by showing the probabilities of each gene belonging to each model; and Dendograms are provided for viewing the hierarchical relationships within the data which have been established using one of the hierarchical cluster techniques.
Using Heuristics : Specialised workflows are available to identify specific clusters (e.g. SAGE data)
Using Distance Measure: Clusters can be derived using a variety of distance based partitioning cluster algorithms.
Refining and Validating Clusters: Once clusters have been identified they can be refined using functional enrichment or their size and shape can be altered using probababilistic models. Clusters can also be validated by calculating their c-index.
Finding Concepts: co-occurance and co-variance projections can be calculated.
Transforming Data: a variety of options are available for filtering, constraining and transforming the data.