Views

Using the visualisations.

There are a number of visualisers available within SeqExpress. All the views are linked in SeqExpress, so that if a gene is selected in one visualisation then the same gene will also be selected in all open visualisations (wheither they be views on genes, models, clusters, expression profiles, genome locations or an ontology). Any items that have been selected can also be saved as new selections in the gene tab by using the Edit->Copy Selection menu. From within the gene tab it is possible to rename, delete, merge or edit these selections.

Any selection is propagated to all other selections. This means that if you select an item in a parallel plot then this selection will be echoed in all other open visualisations, additionally, any items in the gene tab that contain one of these genes will be flagged in the 'contains' column. Examples of using such selections are given below:

A cluster analysis has been preformed, and all the genes in one of the clusters has been selected in the gene tab. The corresponding genes are then selected in the parallel plot and the histogram.

The binding term has been selected from the results of an ontology term search. The binding term is then automatically selected in the Function tab, as well as the open Tree Map visualisation. All genes that have been annotated with the binding term are also selected in the open parallel plot.

A heuristic location cluster analysis has been performed and the results viewed. The parallel plot shows the similiar expression profiles, whilst the two genome views show the locale of the genes. The genome view in the middle is set to auto-zoom, and so shows the locale in detail.

A series of models have been generated, and the genes with a high probability of belonging to one of the models has been selected in the model viewer. The corresponding location of the genes and their expression profiles are then shown.

Types of visualisation

All the views are linked in SeqExpress, so that if a gene is selected in one visualisation then the same gene will also be selected in all open visualisations (wheither they be views on genes, their expression profiles, genome location or an ontology). Any items that have been selected can also be saved a new selections in the gene tab. From within the gene tab it is possible to rename, delete, merge or edit these selections. A number of visualisation are provided, these each show different aspects of the data and perform different functions. Seven types of visualisation are provided:

Gene Views

Gene Table gives information about genes, their individual experiment expression value, associated annotations and any descriptions

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

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

Gene Clouds which can be used to comapre 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)

Cluster viewers are provided to examine differences/similarities between the clusters resulting from an individual analysis/algorithm.

Chromosome Location Visualisations

Genome viewer for the visualisation of the position of genetic elements on a chromosome

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;

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

Dendograms are provided for viewing the hierarchical relationships within the data which have been established using one of the hierarchical cluster techniques.