- 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)
Reconstruct the progression of a biological process from snapshot data.
Diffusion Pseudotimehas 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 a
hierarchicalmode by setting the parameter
n_branchings>1. We recommend, however, to only use
dpt()for computing pseudotime (
n_branchings=0) and to detect branchings via
paga(). For pseudotime, you need to annotate your data with a root cell. For instance:
adata.uns['iroot'] = np.flatnonzero(adata.obs['cell_types'] == 'Stem')
This requires to run
neighbors(), first. In order to reproduce the original implementation of DPT, use
method=='gauss'in this. Using the default
method=='umap'only leads to minor quantitative differences, though.
New in version 1.1.
dpt()also requires to run
diffmap()first. As previously,
dpt()came with a default parameter of
diffmap()has a default parameter of
n_comps=15, you need to pass
diffmap()in order to exactly reproduce previous
- adata :
Annotated data matrix.
- n_dcs :
The number of diffusion components to use.
- n_branchings :
Number of branchings to detect.
- min_group_size :
During recursive splitting of branches (‘dpt groups’) for
n_branchings> 1, do not consider groups that contain less than
min_group_sizedata points. If a float,
min_group_sizerefers to a fraction of the total number of data points.
- allow_kendall_tau_shift :
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 :
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 :
Copy instance before computation and return a copy. Otherwise, perform computation inplace and return
- adata :
- Return type
copy, returns or updates
adatawith the following fields.
n_branchings==0, no field
dpt_groupswill be written.
Array of dim (number of samples) that stores the pseudotime of each cell, that is, the DPT distance with respect to the root cell.
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.
The tool is similar to the R package