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=1e15)[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 zscores. 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 labelled 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 ranksum test is conducted to identify cellcycle states. If the test returns a positive statistic and a pvalue smaller thanp_thresh
, a warning will be issued.
 Returns
Nothing, but updates the following fields:
 Return type