find_extreme_cells: Identify candidate start cells for the trajectory
In jw156605/SLICER: Selective Locally Linear Inference of Cellular Expression Relationships
Description
Usage
Arguments
Value
Examples
Description
Plots the embedding generated by LLE and highlights
potential starting cells for the trajectory. The candidates
are chosen based on the longest shortest path through the
nearest neighbor graph.
Usage
1
find_extreme_cells(traj_graph, embedding)
Arguments
traj_graph
Nearest neighbor graph built from LLE embedding
embedding
Low-dimensional LLE embedding of cells
Value
Indices of potential starting cells
Examples
1
2
3
4
5
6
genes=1:200
cells=sample(1:500,30)
k=10
traj_lle = lle::lle(traj[cells,genes],m=2,k)$Y
traj_graph = conn_knn_graph(traj_lle,5)
find_extreme_cells(traj_graph,traj_lle)
jw156605/SLICER documentation built on May 20, 2019, 5:21 a.m.
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Description Usage Arguments Value Examples
Description
Plots the embedding generated by LLE and highlights potential starting cells for the trajectory. The candidates are chosen based on the longest shortest path through the nearest neighbor graph.
Usage
1 | find_extreme_cells(traj_graph, embedding)
|
Arguments
traj_graph |
Nearest neighbor graph built from LLE embedding |
embedding |
Low-dimensional LLE embedding of cells |
Value
Indices of potential starting cells
Examples
1 2 3 4 5 6 | genes=1:200
cells=sample(1:500,30)
k=10
traj_lle = lle::lle(traj[cells,genes],m=2,k)$Y
traj_graph = conn_knn_graph(traj_lle,5)
find_extreme_cells(traj_graph,traj_lle)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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