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

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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 cell types of cells in single cell RNA-Seq data. 

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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 by sample must be viewed individuallyby sample. 

Like Graph-based clustering, t-SNE takes PC values as its input and further reduces the data down to two or three dimensions. For consistency, you should use the same number of PCs as the input for t-SNE that you used for Graph-based clustering. 

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

A new t-SNE task node will be produced.

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The t-SNE scatter plot is interactive and can be viewed for 2D or 3D (calculated separately). Using the t-SNE plot, cells can be classified based on clustering results or differences in gene and pathway expression. 

The t-SNE plot is in 3D by default. You can rotate the 3D plot by left-clicking and dragging your mouse. You can zoom in and out using your mouse wheel. You can pan by right-clicking and dragging your mouse. The 2D t-SNE is also calculated and you can switch between the 2D and 3D plots using the Plot style radio buttons. 

By default, the t-SNE plot will be colored by the clusters identified by Graph-based clustering.