scirpy.pl.clonal_expansion
- scirpy.pl.clonal_expansion(adata, groupby, *, target_col='clone_id', clip_at=3, expanded_in=None, summarize_by='cell', normalize=True, show_nonexpanded=True, viztype='bar', **kwargs)
Visualize clonal expansion.
`Visualize clonal expansion.
Plots the fraction of cells that belong to an expanded Clonotype by a categorical variable.
If
summarize_byis set to “clone_id” it plots the fraction of clonotypes instead of the fraction of cells.Removes all entries with
NaNintarget_colprior to plotting.- Parameters
- adata :
AnnData AnnData object to work on.
- groupby :
str Group by this categorical variable in
adata.obs.- target_col :
str(default:'clone_id') Column in
adata.obscontaining the clonotype information.- clip_at :
int(default:3) All entries in
target_colwith more copies thanclip_atwill be summarized into a single group.- expanded_in :
str|NoneOptional[str] (default:None) Calculate clonal expansion within groups. To calculate expansion within patients, set this to the column containing patient annotation. If set to None, a clonotype counts as expanded if there’s any cell of the same clonotype across the entire dataset. See also Public clonotype.
- summarize_by : {‘cell’, ‘clone_id’}
Literal[‘cell’, ‘clone_id’] (default:'cell') Can be either
cellto count cells belonging to a clonotype (the default), orclone_idto count clonotypes. The former leads to a over-representation of expanded clonotypes but better represents the fraction of expanded cells.- normalize :
bool(default:True) If True, compute fractions rather than reporting abosolute numbers.
- show_nonexpanded :
bool(default:True) Whether or not to show the fraction of non-expanded cells/clonotypes
- viztype : {‘bar’, ‘barh’}
Literal[‘bar’, ‘barh’] (default:'bar') barfor bars,barhfor horizontal bars.- **kwargs
Additional arguments passed to
scirpy.pl.base.bar()
- adata :