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 [Haghverdi et al., 2016, Wolf et al., 2019].

Reconstruct the progression of a biological process from snapshot data. Diffusion Pseudotime has been introduced by Haghverdi et al. [2016] and implemented within Scanpy [Wolf et al., 2018]. Here, we use a further developed version, which is able to deal with disconnected graphs [Wolf et al., 2019] and can be run in a hierarchical mode 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')[0]

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.

Added in version 1.1.

dpt() also requires to run diffmap() first. As previously, dpt() came with a default parameter of n_dcs=10 but diffmap() has a default parameter of n_comps=15, you need to pass n_comps=10 in diffmap() in order to exactly reproduce previous dpt() results.

Parameters:

adata AnnData

Annotated data matrix.

n_dcs int (default: 10)

The number of diffusion components to use.

n_branchings int (default: 0)

Number of branchings to detect.

min_group_size float (default: 0.01)

During recursive splitting of branches (‘dpt groups’) for n_branchings > 1, do not consider groups that contain less than min_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 (default: True)

If a very small branch is detected upon splitting, shift away from maximum correlation in Kendall tau criterion of Haghverdi et al. [2016] to stabilize the splitting.

neighbors_key str | None (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 (default: False)

Copy instance before computation and return a copy. Otherwise, perform computation inplace and return None.

Return type:

AnnData | None

Returns:

Returns None if copy=False, else returns an AnnData object. Sets the following fields (If n_branchings==0, no field adata.obs['dpt_groups'] will be written):

adata.obs['dpt_pseudotime']pandas.Series (dtype float)

Array of dim (number of samples) that stores the pseudotime of each cell, that is, the DPT distance with respect to the root cell.

adata.obs['dpt_groups']pandas.Series (dtype category)

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 Angerer et al. [2015].