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The metric to use when computing distances in high-dimensional space. Default is Euclidean. 

Number of iterations (--n_epochs)

UMAP uses an iterative algorithm to optimize the low-dimensional representation. The value 0 corresponds to the default, which chooses the number of iterations based on the size of the input data. More iterations will result in a more accurate embedding, but will take longer to run. Default is 0.

Random generator seed (--random_state)

Several parts of UMAP utilize a random number generator to provide an initial value. The value 0 corresponds to the default of the underlying algorithm.  Default is 0.

Generate UMAP table

Output a UMAP table data node that can be downloaded. The 2D UMAP coordinates are labeled Feature 1 and Feature 2; the 3D UMAP coordinates are labeled Feature 3, 4, and 5.  Default is disabled.

PCA: Number of principal components 

UMAP uses principal components as its input. The number of principal components to use is set here. Default is 50.

We recommend using the PCA task to determine the optimal number of principal components for your data. 

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Options are equally or by variance. Feature values can be standardized prior to PCA so that the contribution of each feature does not depend on its variance. To standardize, choose equally. To take variance into account and focus on the most variable features, choose by variance.  Default is by variance.

Normalization: Log transform data

You can choose to log transform the data prior to running PCA as part of UMAP. Default is disabled.

Normalization: Log base

If you are normalizing the data, choose a log base. Default is 2 when Log transform data is enabled.

Normalization: Log offset

If you are normalizing the data, choose an offset. Default is 1 when Log transform data is enabled.

References

[1] McInnes L and Healy J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv, 2018, e-prints 1802.03426,

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