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Sign up| method: | |
| id: paga | |
| name: PAGA | |
| tool_id: paga | |
| source: tool | |
| platform: Python | |
| url: https://github.com/theislab/graph_abstraction | |
| authors: | |
| - given: Alexander | |
| family: Wolf | |
| email: alex.wolf@helmholtz-muenchen.de | |
| github: falexwolf | |
| ORCID: 0000-0002-8760-7838 | |
| - given: Fabian | |
| family: Theis | |
| email: fabian.theis@helmholtz-muenchen.de | |
| github: theislab | |
| wrapper: | |
| type: branch_trajectory | |
| topology_inference: free | |
| trajectory_types: | |
| - cycle | |
| - linear | |
| - bifurcation | |
| - convergence | |
| - multifurcation | |
| - tree | |
| - acyclic_graph | |
| - graph | |
| - disconnected_graph | |
| input_required: | |
| - counts | |
| - start_id | |
| input_optional: | |
| - groups_id | |
| container: | |
| docker: dynverse/ti_paga | |
| url: https://github.com/dynverse/ti_paga | |
| manuscript: | |
| doi: 10.1186/s13059-019-1663-x | |
| google_scholar_cluster_id: '10470081259069082868' | |
| preprint_date: '2017-10-27' | |
| publication_date: '2019-03-19' | |
| parameters: | |
| - id: filter_features | |
| description: Whether to do feature filtering | |
| type: logical | |
| default: yes | |
| - id: n_neighbors | |
| description: Number of neighbours for knn | |
| type: integer | |
| default: 15 | |
| distribution: | |
| type: uniform | |
| lower: 1 | |
| upper: 100 | |
| - id: n_comps | |
| description: Number of principal components | |
| type: integer | |
| default: 50 | |
| distribution: | |
| type: uniform | |
| lower: 0 | |
| upper: 100 | |
| - id: n_dcs | |
| description: Number of diffusion components for denoising graph, 0 means no denoising. | |
| type: integer | |
| default: 15 | |
| distribution: | |
| type: uniform | |
| lower: 0 | |
| upper: 40 | |
| - id: resolution | |
| description: Resolution of louvain clustering, which determines the granularity | |
| of the clustering. Higher values will result in more clusters. | |
| type: numeric | |
| default: 1 | |
| distribution: | |
| type: uniform | |
| lower: 0.1 | |
| upper: 10 | |
| - id: embedding_type | |
| description: Either 'umap' (scales very well, recommended for very large datasets) | |
| or 'fa' (ForceAtlas2, often a bit more intuitive for small datasets). | |
| type: character | |
| default: fa | |
| values: | |
| - umap | |
| - fa | |
| - id: connectivity_cutoff | |
| description: Cutoff for the connectivity matrix | |
| type: numeric | |
| default: 0.05 | |
| distribution: | |
| type: uniform | |
| lower: 0 | |
| upper: 1 |