scirpy.tl.repertoire_overlap
- scirpy.tl.repertoire_overlap(adata, groupby, *, target_col='clone_id', overlap_measure='jaccard', overlap_threshold=None, fraction=None, inplace=True, added_key='repertoire_overlap')
Compute distance between cell groups based on clonotype overlap.
Adds parwise overlaps, distance matrix and linkage to
uns
.Warning
This function is experimental and is likely to change in the future.
- Parameters
- adata :
AnnData
AnnData object to work on.
- groupby :
str
Column with group labels (e.g. samples, tussue source, diagnosis, etc).
- target_col :
str
(default:'clone_id'
) Category that overlaps among groups (
clone_id
by default, but can in principle be any group or cluster)- overlap_measure :
str
(default:'jaccard'
) Any distance measure accepted by
scipy.spatial.distance
; by default it isjaccard
.- overlap_threshold :
float
|None
Optional
[float
] (default:None
) The minimum required weight to accept presence.
- fraction :
None
|str
|bool
Union
[None
,str
,bool
] (default:None
) If
True
, compute fractions of abundances relative to thegroupby
column rather than reporting abosolute numbers. Alternatively, a column name can be provided according to that the values will be normalized or an iterable providing cell weights directly. Setting it toFalse
orNone
assigns equal weight to all cells.- inplace :
bool
(default:True
) Whether results should be added to
uns
or returned directly.- added_key :
str
(default:'repertoire_overlap'
) Results will be added to
uns
under this key.
- adata :
- Return type
None
|Tuple
[DataFrame
,ndarray
,ndarray
]Optional
[Tuple
[DataFrame
,ndarray
,ndarray
]]- Returns
A DataFrame used by the pairwise scatterplot, distance matrix and linkage.