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A Graph-based clustering task node will be added to the pipeline under the Analyses tab and a circular Graph-based clusters output data node will be produced (Figure ?10)

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• Click the circular Graph-based clusters data node
• Click Exploratory analysis in the toolbox
• Click UMAP
• Set the number of principal components to 15 (Figure ?11)
• Click Finish to run the task

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A UMAP task node will be added to the pipeline under the Analyses tab and a circular UMAP output data node will be produced (Figure ?12)

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Performing exploratory analysis with gene expression data is the same as for the merged counts. Because there are a large number of genes, you will need to reduce the dimensionality with PCA, choose an optimal number of PCs and perform downstream clustering and visualization (e.g. graph-based clustering and UMAP/t-SNE). Performing exploratory analysis with protein data is different. There is no need to reduce the dimensionality as there are only a handful of features (17 proteins in this case), so you can proceed straight to downstream clustering and visualization. Figure ? 13 shows an example of how the pipeline might look if the data is split and analyzed separately.

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You can then use the Data viewer to bring together multiple plots for comparison (Figure ?14).

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