umap
Visualize and reduce high-dimensional data into lower dimensions using uniform manifold approximation and projection (UMAP) for enhanced analysis, tailored for single-cell RNA sequencing workflows.
Instructions
Uniform Manifold Approximation and Projection (UMAP) for visualization
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| a | No | Parameter controlling the embedding. | |
| alpha | No | Initial learning rate for the embedding optimization. | |
| b | No | Parameter controlling the embedding. | |
| gamma | No | Weighting applied to negative samples. | |
| init_pos | No | How to initialize the low dimensional embedding. | spectral |
| maxiter | No | Number of iterations (epochs) of the optimization. | |
| method | No | Implementation to use ('umap' or 'rapids'). | umap |
| min_dist | No | Minimum distance between embedded points. | |
| n_components | No | Number of dimensions of the embedding. | |
| negative_sample_rate | No | Number of negative samples per positive sample. | |
| neighbors_key | No | Key for neighbors settings in .uns. | |
| random_state | No | Random seed for reproducibility. | |
| spread | No | Scale of embedded points. |