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In this data set, a reasonable cut-off could be set anywhere between around 10 and 30 PCs. We will use 15 in downstream steps. 

Cluster

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by Gene Expression data

CITE-Seq data includes both gene and protein expression information. When the data types are combined, we can perform downstream analysis using both data types. We will begin with the mRNA data.

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The UMAP task report includes a scatter plot with the clustering results coloring the points (Figure 19).

 Plot controls are located in the control panel on the left. You can adjust plot options, perform selection and filtering, and manage cell type classifications using the control panel. Below the scatter plot is a biomarker table giving the top genes and proteins that are highly and specifically expressed in each cluster. Although the clusters were identified using only the gene expression data, the biomarkers calculation includes genes and proteins. The table is interactive and clicking a feature (gene or protein) in the table will color the plot by that feature (hold Ctrl on your keyboard and click to color by multiple genes in the table). 

The UMAP plot is 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 UMAP is also calculated and you can switch between the 2D and 3D plots using the Plot style radio buttons in the control panel.

• Click 2D in the Plot style options to switch from 3D to 2D UMAP

An advantage of UMAP over t-SNE is that is preserves more of the global structure of the data. This means that with UMAP, more similar clusters are closer together while dissimilar clusters are further apart. With t-SNE, the relative positions of clusters to each other are often uninformative.