The Global View on Expression Data

Heat maps and clustering

Heat maps are a well-known visualization method for expression data from DNA microarrays. Expression profiles are in the rows, gel images in the columns. The legend across the top shows the color code for spot intensities. Rows are labeled based on the spot labels from the gel images. By default, data is standardized to zero mean and unit variance before being shown in the heat map. Other options for normalization are available in the Analyze menu of the statistics table.

Open the Demonstration project in Delta2D. Open the quantitation table (Window > Quantitation Table), make sure the Statistics Table is selected. Hide the quantitative data for the fused image: Choose Column > Column Properties, uncheck the checkbox next to Fused Image, press OK. Press the Analyze button in the top left of the statistics table. A new analysis window is opened, containing the current expression profiles in a heat map display. If you want to see more rows at once, you can use Display > Set Element Size and select 20 by 5.

Clustering images: what image groups or classes are there?

Clustering methods can group expression profiles and gel images by similarity. This can be very useful for getting an overview of all expression profiles before proceeding with more detailed analyses. Clustering of gel images can also be used to detect outliers, and to identify structures in the experiment. Ideally, the cluster composition will reflect the structure of the experiment, e.g. replicates and images from the same sample should have similar expression levels and thus end up in the same cluster.

In this clustering you see an experiment with control (C1, C2, C4, C5) and treated (T1, T2, T3, T4) samples, made in triplicates. The clustering rediscovers the experimental setup, i.e. gel images with similar samples share a cluster. A sample forming a separate cluster would indicate an outlier for which closer inspection is advisable. Made using Pearson correlation as the similarity measure between images.

Let's make a hierarchical clustering to show more structure in the data: Press the HCL button in the toolbar. Accept the default settings and press OK. The hierarchical clustering groups both samples (gel images) and expression profiles. The cluster hierarchy is shown in a tree display. As you can see, replicates are clustered together, indicating higher similarity, as we would expect.

Clustering expression profiles: finding correlated proteins

Clustering of expression profiles is done to identify proteins with similar behavior, implying that they are co-regulated or at least correlated. The global nature of the cluster display allows for a broad overview and the forming of hypotheses that can then be tested.

Spots with similar expression profiles are clustered together. Support Tree clustering with Euclidean distance.

Discovering patterns in expression profiles

Cluster Viewers

One can regard the mean (or median) of a cluster as a kind of "typical" expression profile. The clustering displays allow you to split the set of expression profiles into separate subsets:

Cutting a tree by a distance threshold. Use the slider to adjust the threshold.

Right click anywhere in the cluster display and select Gene tree properties from the context menu. Use the slider to cut the tree at a certain distance from the root. Then check the Create Cluster Viewers checkbox and press OK. A new section called Gene Tree Cut is created in the Result Tree on the left hand side of the display.

Combined expression profiles in 12 clusters.