scanpy.tl.dpt¶

scanpy.tl.
dpt
(adata, n_dcs=10, n_branchings=0, min_group_size=0.01, allow_kendall_tau_shift=True, neighbors_key=None, copy=False)¶ Infer progression of cells through geodesic distance along the graph [Haghverdi16] [Wolf19].
Reconstruct the progression of a biological process from snapshot data.
Diffusion Pseudotime
has been introduced by [Haghverdi16] and implemented within Scanpy [Wolf18]. Here, we use a further developed version, which is able to deal with disconnected graphs [Wolf19] and can be run in ahierarchical
mode by setting the parametern_branchings>1
. We recommend, however, to only usedpt()
for computing pseudotime (n_branchings=0
) and to detect branchings viapaga()
. For pseudotime, you need to annotate your data with a root cell. For instance:adata.uns['iroot'] = np.flatnonzero(adata.obs['cell_types'] == 'Stem')[0]
This requires to run
neighbors()
, first. In order to reproduce the original implementation of DPT, usemethod=='gauss'
in this. Using the defaultmethod=='umap'
only leads to minor quantitative differences, though.New in version 1.1.
dpt()
also requires to rundiffmap()
first. As previously,dpt()
came with a default parameter ofn_dcs=10
butdiffmap()
has a default parameter ofn_comps=15
, you need to passn_comps=10
indiffmap()
in order to exactly reproduce previousdpt()
results. Parameters
 adata :
AnnData
AnnData
Annotated data matrix.
 n_dcs :
int
int
(default:10
) The number of diffusion components to use.
 n_branchings :
int
int
(default:0
) Number of branchings to detect.
 min_group_size :
float
float
(default:0.01
) During recursive splitting of branches (‘dpt groups’) for
n_branchings
> 1, do not consider groups that contain less thanmin_group_size
data points. If a float,min_group_size
refers to a fraction of the total number of data points. allow_kendall_tau_shift :
bool
bool
(default:True
) If a very small branch is detected upon splitting, shift away from maximum correlation in Kendall tau criterion of [Haghverdi16] to stabilize the splitting.
 neighbors_key :
str
,None
Optional
[str
] (default:None
) If not specified, dpt looks .uns[‘neighbors’] for neighbors settings and .obsp[‘connectivities’], .obsp[‘distances’] for connectivities and distances respectively (default storage places for pp.neighbors). If specified, dpt looks .uns[neighbors_key] for neighbors settings and .obsp[.uns[neighbors_key][‘connectivities_key’]], .obsp[.uns[neighbors_key][‘distances_key’]] for connectivities and distances respectively.
 copy :
bool
bool
(default:False
) Copy instance before computation and return a copy. Otherwise, perform computation inplace and return
None
.
 adata :
 Return type
 Returns
Depending on
copy
, returns or updatesadata
with the following fields.If
n_branchings==0
, no fielddpt_groups
will be written.dpt_pseudotime
pandas.Series
(adata.obs
, dtypefloat
)Array of dim (number of samples) that stores the pseudotime of each cell, that is, the DPT distance with respect to the root cell.
dpt_groups
pandas.Series
(adata.obs
, dtypecategory
)Array of dim (number of samples) that stores the subgroup id (‘0’, ‘1’, …) for each cell. The groups typically correspond to ‘progenitor cells’, ‘undecided cells’ or ‘branches’ of a process.
Notes
The tool is similar to the R package
destiny
of [Angerer16].