scanpy.tl.dpt¶

scanpy.tl.dpt(adata, n_dcs=10, n_branchings=0, min_group_size=0.01, allow_kendall_tau_shift=True, copy=False)

Infer progression of cells through geodesic distance along the graph [Haghverdi16] [Wolf17i].

Reconstruct the progression of a biological process from snapshot data. Diffusion Pseudotime has been introduced by [Haghverdi16] and implemented within Scanpy [Wolf17]. Here, we use a further developed version, which is able to deal with disconnected graphs [Wolf17i] 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.

New 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, optional (default: 10) The number of diffusion components to use. n_branchings : int, optional (default: 0) Number of branchings to detect. min_group_size : [0, 1] or float, optional (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, optional (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. copy : bool, optional (default: False) Copy instance before computation and return a copy. Otherwise, perform computation inplace and return None. Depending on copy, returns or updates adata with the following fields. If n_branchings==0, no field dpt_groups will be written. dpt_pseudotime (pd.Series (adata.obs, 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. dpt_groups (pd.Series (adata.obs, 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 [Angerer16].