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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
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