Performing hierarchical clustering
Last updated
Last updated
The gene list in spreadsheet Down_Syndrome_vs_Normal (A) can be used for hierarchical clustering to visualize patterns in the data.
Under the Visualization section in the Gene Expression workflow, select Cluster Based on Significant Genes
The Cluster Significant Genes dialog asks you to specify the type of clustering you want to perform.
Choose Hierarchical Clustering and select OK
Choose the Down_Syndrome_vs_Normal (A) spreadsheet under the Spreadsheet with differentially expressed genes
Choose the Standardize – shift genes to mean of zero and scale to standard deviation of one under the Expression normalization panel (Figure 1)
This option will adjust all the gene intensities such that the mean is zero and the standard deviation is 1.
Figure 1. Configuring Hierarchical Clustering
Select OK to generate a Hierarchical Clustering tab (Figure 2)
Figure 2. Hierarchical Clustering of Down_Syndrome_vs_Normal (A)
The graph (Figure 2) illustrates the standardized gene expression level of each gene in each sample. Each gene is represented in one column, and each sample is represented in one row. Genes with no difference in expression have a value of zero and are colored black. Genes with increased expression in Down syndrome samples have positive values and are colored red. Genes with reduced expression in Down syndrome samples have negative values and are colored green. Down syndrome samples are colored red and normal samples are colored orange. On the left-hand side of the graph, we can see that the Down syndrome samples cluster together.
For more information on the methods used for clustering, you can refer to Chapter 8: Hierarchical & Partitioning Clustering in Help > User’s Manual. For a tutorial on configuring the clustering plot, please refer to Hierarchical Clustering Analysis
If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.
