cellrank.tl.estimators.CFLARE.compute_terminal_states¶
- CFLARE.compute_terminal_states(use=None, percentile=98, method='kmeans', cluster_key=None, n_clusters_kmeans=None, n_neighbors=20, resolution=0.1, n_matches_min=0, n_neighbors_filtering=15, basis=None, n_comps=5, scale=False, en_cutoff=0.7, p_thresh=1e-15)[source]¶
Find approximate recurrent classes of the Markov chain.
Filter to obtain recurrent states in left eigenvectors. Cluster to obtain approximate recurrent classes in right eigenvectors.
- Parameters
use (
Union
[int
,Tuple
[int
],List
[int
],range
,None
]) – Which or how many first eigenvectors to use as features for clustering/filtering. If None, use the eigengap statistic.percentile (
Optional
[int
]) – Threshold used for filtering out cells which are most likely transient states. Cells which are in the lowerpercentile
percent of each eigenvector will be removed from the data matrix.method (
str
) – Method to be used for clustering. Must be one of ‘louvain’, ‘leiden’ or ‘kmeans’.cluster_key (
Optional
[str
]) – If a key to cluster labels is given,terminal_states
will get associated with these for naming and colors.n_clusters_kmeans (
Optional
[int
]) – If None, this is set touse + 1
.n_neighbors (
int
) – If we use ‘louvain’ or ‘leiden’ for clustering cells, we need to build a KNN graph. This is the \(K\) parameter for that, the number of neighbors for each cell.resolution (
float
) – Resolution parameter for ‘louvain’ or ‘leiden’ clustering. Should be chosen relatively small.n_matches_min (
Optional
[int
]) – Filters out cells which don’t have at least n_matches_min neighbors from the same class. This filters out some cells which are transient but have been misassigned.n_neighbors_filtering (
int
) – Parameter for filtering cells. Cells are filtered out if they don’t have at leastn_matches_min
neighbors among theirn_neighbors_filtering
nearest cells.basis (
Optional
[str
]) – Key from :paramref`adata`.obsm
to be used as additional features for the clustering.n_comps (
int
) – Number of embedding components to be use whenbasis
is not None.scale (
bool
) – Scale to z-scores. Consider using this if appending embedding to features.en_cutoff (
Optional
[float
]) – Ifcluster_key
is given, this parameter determines when an approximate recurrent class will be labeled as ‘Unknown’, based on the entropy of the distribution of cells over transcriptomic clusters.p_thresh (
float
) – If cell cycle scores were provided, a Wilcoxon rank-sum test is conducted to identify cell-cycle states. If the test returns a positive statistic and a p-value smaller thanp_thresh
, a warning will be issued.
- Returns
Nothing, but updates the following fields:
- Return type