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Uniform Manifold Approximation and Projection (UMAP) is a dimensional reduction technique [1]. UMAP aims to preserve the essential high-dimensional structure and present it in a low-dimensional representation. UMAP is particularly useful for visually identifying groups of similar samples in large high-dimensional data sets such as single cell RNA-Seq.
Running UMAP
We recommend normalizing your data prior to running UMAP, but the task will run on any counts data node.
- Click the counts data node
- Click the Exploratory analysis section of the toolbox
- Click UMAP
- Click Finish to run
UMAP produces a UMAP task node. Opening the task report launches a scatter plot showing the UMAP results.
UMAP vs. t-SNE
Both t-SNE and UMAP are dimensional reduction techniques that are useful for identifying groups of similar samples in large high-dimensional data sets. A comparison of the techniques for visualizing single cell RNA-Seq data by the authors of UMAP suggests that UMAP runs faster, is more reproducible, gives a more meaningful organization of clusters, and preserves more information about the global structure of the data than t-SNE [2].
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Running UMAP
We recommend normalizing your data prior to running UMAP, but the task will run on any counts data node.
- Click the counts data node
- Click the Exploratory analysis section of the toolbox
- Click UMAP
- Click Finish to run
UMAP produces a UMAP task node. Opening the task report launches a scatter plot showing the UMAP results.
Basic UMAP parameters
Initialize output values (--init)
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Overview
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