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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. 

We will use t-SNE to visualize the results of Graph-based clustering.

t-SNE

t-Distributed Stochastic Neighbor Embedding (t-SNE) is a dimensional reduction technique that prioritizes local relationships to build a low-dimensional representation of the high-dimensional data that places objects that are similar in high-dimensional space close together in the low-dimensional representation. This makes t-SNE well suited for analyzing high-dimensional data when the goal is to identify groups of similar objects, such as types of cells in single cell RNA-Seq data. 

• Click the Cluster result node 
• Click Exploratory analysis in the task menu
• Click t-SNE

If you have multiple samples, you can choose to run t-SNE for each sample individually or for all samples together using the Split cells by sample option. Please note that this option will not be present if you are running t-SNE on a clustering result. For clarity, clustering results run with all samples together must be viewed together and clustering results run individually must be viewed individually.