umap
Reduce high-dimensional single-cell RNA sequencing data to 2D or 3D visualizations for pattern discovery and cluster analysis using UMAP dimensionality reduction.
Instructions
Uniform Manifold Approximation and Projection (UMAP) for visualization
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| min_dist | No | Minimum distance between embedded points. | |
| spread | No | Scale of embedded points. | |
| n_components | No | Number of dimensions of the embedding. | |
| maxiter | No | Number of iterations (epochs) of the optimization. | |
| alpha | No | Initial learning rate for the embedding optimization. | |
| gamma | No | Weighting applied to negative samples. | |
| negative_sample_rate | No | Number of negative samples per positive sample. | |
| init_pos | No | How to initialize the low dimensional embedding. | spectral |
| random_state | No | Random seed for reproducibility. | |
| a | No | Parameter controlling the embedding. | |
| b | No | Parameter controlling the embedding. | |
| method | No | Implementation to use ('umap' or 'rapids'). | umap |
| neighbors_key | No | Key for neighbors settings in .uns. |