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To check how well our list of differentially expressed genes distinguishes one treatment group from another, we can perform hierarchical clustering based on the gene list. Clustering can also be used to discover novel groups within your data set, identify gene expression signatures distinguishing groups of samples, and to identify genes with similar patterns of gene expression across samples.
- Select Click the Feature list data node
- Select the Visualizations section of Click Exploratory analysis in the task menuSelect Hierarchical clustering from
- the Visualizations section of the task menu Click Hierarchical clustering (Figure 1)
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The Hierarchical clustering menu will open (Figure 2).
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- Select Click Finish to run with default settings
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- Double-click the Hierarchical clustering task node to launch the heat map
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Samples are shown on rows and genes on columns. Clustering for samples and genes is shown through the dendrogram trees. More similar samples/genes are separated by fewer branch points of the dendrogram tree.
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The heat map can be customized to improve data visualization using the menu on the left-hand side of the heat map.
- Select Treatment from the Attributes drop5-AZA Dose from the Attributes drop-down menu
Samples are now labeled with their Treatment group (Figure 5).
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Samples cluster based on treatment group and the 5μM and 10μM groups are more similar to each other than to the 0μM group.
We can save the heat map as a publication-quality image.
- Select Click
- Choose size and resolution using the Save as SVG dialog (Figure 6)
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- Select Save
The heat map will be saved as a .svg file and downloaded in your web browser.
For more information about hierarchical clustering and the Dendrogram view, please see the Hierarchical Clustering (Old Version) user guide.
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