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In this case, we see a few marker genes are highly correlated with PC1. 

Graph-based clustering

Graph-based clustering identifies groups of similar cells using PC scores as the input. By including only the most informative PCs, noise in the data set is excluded, improving the results of clustering. 

• Click the Normalized counts data node
• Click Exploratory analysis in the task menu
• Click Graph-based clustering 

Clustering can be performed on each sample individually or on all samples together. Here, we are working with a single sample. 

• Click Configure to access the advanced options
• Set Number of principal components to 10 

The Number of principal components should be set based on the your examination of the Scree plot and component loadings table. The default value of 100 is likely exhaustive for most data sets, but may introduce noise that reduces the resolution of clustering (fewer clusters identified). 

• Click Apply
  
• Click Finish to run

A new Graph-based clustering task node and a Clustering result data node will be generated. 

• Double-click the Graph-based clustering task node to open the task report

The Graph-based clustering task report lists the number of clusters and what proportion of cells fall into each cluster. It also includes a cluster biomarkers table. This lists the top-10 genes that distinguish each cluster from the others.