scirpy.tl.define_clonotypes¶
-
scirpy.tl.define_clonotypes(adata, *, key_added='clonotype', **kwargs)¶ Define clonotypes based on CDR3 nucleic acid sequence identity.
As opposed to
define_clonotype_clusters()which employs a more flexible definition of clonotype clusters, this function stringently defines clonotypes based on nucleic acid sequence identity. Technically, this function is an alias todefine_clonotype_clusters()with different default parameters.Requires running
scirpy.pp.tcr_neighbors()withsequence='nt'andmetric='identityfirst (which are the default parameters).- Parameters
- adata
Annotated data matrix
- key_added
Name of the columns which will be added to
adata.obsif inplace isTrue. Will create the columns{key_added}and{key_added}_size.- same_v_gene
Enforces clonotypes to have the same V-genes. This is useful as the CDR1 and CDR2 regions are fully encoded in this gene. See CDR for more details.
Possible values are
False- Ignore V-gene during clonotype definition"primary_only"- Only the V-genes of the primary pair of alpha and beta chains needs to match"all"- All V-genes of all sequences need to match.
Chains with no detected V-gene will be treated like a separate “gene” with the name “None”.
- partitions
How to find graph partitions that define a clonotype. Possible values are
leiden, for using the “Leiden” algorithm andconnectedto find fully connected sub-graphs.The difference is that the Leiden algorithm further divides fully connected subgraphs into highly-connected modules.
- resolution
resolutionparameter for the leiden algorithm.- n_iterations
n_iterationsparameter for the leiden algorithm.- neighbors_key
Key under which the neighboorhood graph is stored in
adata.uns. By default, tries to read fromtcr_neighbors_{sequence}_{metric}, e.g.tcr_neighbors_nt_identity.- inplace
If
True, adds the results to anndata, otherwise returns them.
- Return type
Tuple[ndarray,ndarray],NoneOptional[Tuple[ndarray,ndarray]]- Returns