scFates.tl.tree¶
- scFates.tl.tree(adata, Nodes=None, use_rep=None, ndims_rep=None, weight_rep=None, method='ppt', init=None, ppt_sigma=0.1, ppt_lambda=1, ppt_metric='euclidean', ppt_nsteps=50, ppt_err_cut=0.005, ppt_gpu_tpb=16, epg_lambda=0.01, epg_mu=0.1, epg_trimmingradius=inf, epg_initnodes=2, epg_extend_leaves=False, epg_verbose=False, device='cpu', plot=False, basis='umap', seed=None, copy=False, **kwargs)¶
Generate a principal tree.
Learn a simplified representation on any space, compsed of nodes, approximating the position of the cells on a given space such as gene expression, pca, diffusion maps, … If method==’ppt’, uses simpleppt implementation from [Soldatov19]. If method==’epg’, uses Elastic Principal Graph approach from [Albergante20].
- Parameters
- adata :
AnnData Annotated data matrix.
- Nodes :
int|NoneOptional[int] (default:None) Number of nodes composing the principial tree, use a range of 10 to 100 for ElPiGraph approach and 100 to 2000 for PPT approach.
- use_rep :
str|NoneOptional[str] (default:None) Choose the space to be learned by the principal tree.
- ndims_rep :
int|NoneOptional[int] (default:None) Number of dimensions to use for the inference.
- weight_rep :
str|NoneOptional[str] (default:None) If ppt, use a weight matrix for learning the tree.
- method : {‘ppt’, ‘epg’}
Literal[‘ppt’, ‘epg’] (default:'ppt') If ppt, uses simpleppt approach, ppt_lambda and ppt_sigma are the parameters controlling the algorithm. If epg, uses ComputeElasticPrincipalTree function from elpigraph python package, epg_lambda epg_mu and epg_trimmingradius are the parameters controlling the algorithm.
- init :
DataFrame|NoneOptional[DataFrame] (default:None) Initialise the point positions.
- ppt_sigma :
float|int|NoneUnion[float,int,None] (default:0.1) Regularization parameter for simpleppt [Mao15].
- ppt_lambda :
float|int|NoneUnion[float,int,None] (default:1) Parameter for simpleppt, penalty for the tree length [Mao15].
- ppt_metric :
str(default:'euclidean') The metric to use to compute distances in high dimensional space. For compatible metrics, check the documentation of sklearn.metrics.pairwise_distances if using cpu or cuml.metrics.pairwise_distances if using gpu.
- ppt_nsteps :
int(default:50) Number of steps for the optimisation process of simpleppt.
- ppt_err_cut :
float(default:0.005) Stop simpleppt algorithm if proximity of principal points between iterations less than defiend value.
- ppt_gpu_tpb :
int(default:16) Threads per block parameter for cuda computations.
- epg_lambda :
float|int|NoneUnion[float,int,None] (default:0.01) Parameter for ElPiGraph, coefficient of ‘stretching’ elasticity [Albergante20].
- epg_mu :
float|int|NoneUnion[float,int,None] (default:0.1) Parameter for ElPiGraph, coefficient of ‘bending’ elasticity [Albergante20].
- epg_trimmingradius :
Optional(default:inf) Parameter for ElPiGraph, trimming radius for MSE-based data approximation term [Albergante20].
- epg_initnodes :
int|NoneOptional[int] (default:2) numerical 2D matrix, the k-by-m matrix with k m-dimensional positions of the nodes in the initial step
- epg_extend_leaves :
bool(default:False) Parameter for ElPiGraph, calls
elpigraph.ExtendLeaves()after graph learning.- epg_verbose :
bool(default:False) show verbose output of epg algorithm
- device : {‘cpu’, ‘gpu’}
Literal[‘cpu’, ‘gpu’] (default:'cpu') Run either mehtod on cpu or on gpu
- plot :
bool(default:False) Plot the resulting tree.
- basis :
str|NoneOptional[str] (default:'umap') Basis onto which the resulting tree should be projected.
- seed :
int|NoneOptional[int] (default:None) A numpy random seed.
- copy :
bool(default:False) Return a copy instead of writing to adata.
- **kwargs
Arguments passsed to
elpigraph.computeElasticPrincipalTree()
- adata :
- Returns
adata – if copy=True it returns or else add fields to adata:
- .uns[‘ppt’]
dictionnary containing information from simpelppt tree if method=’ppt’
- .uns[‘epg’]
dictionnary containing information from elastic principal tree if method=’epg’
- .obsm[‘R’]
soft assignment of cells to principal points
- .uns[‘graph’][‘B’]
adjacency matrix of the principal points
- .uns[‘graph’][‘F’]
coordinates of principal points in representation space
- Return type