dynamo.pd.andecestor

dynamo.pd.andecestor(adata, init_cells, init_states=None, cores=1, t_end=50, basis='umap', n_neighbors=5, direction='forward', interpolation_num=250, last_point_only=False, metric='euclidean', metric_kwds=None, seed=19491001, **kwargs)[source]

Predict the ancestors or descendants of a group of initial cells (states) with the given vector field function.

Parameters:

  • adata (AnnData) – AnnData object that contains the reconstructed vector field function in the uns attribute.

  • init_cells (List) – Cell name or indices of the initial cell states for the historical or future cell state prediction with numerical integration. If the names in init_cells not found in the adata.obs_name, it will be treated as cell indices and must be integers.

  • init_states (Optional[ndarray]) – Initial cell states for the historical or future cell state prediction with numerical integration.

  • basis (str) – The key in adata.obsm that points to the embedding data to use for predicting cell fate.

  • cores (int) – Number of cores to calculate nearest neighbor graph.

  • t_end (int) – The length of the time period from which to predict cell state forward or backward over time. This is used by the odeint function.

  • n_neighbors (int) – Number of nearest neighbors.

  • direction (str) – The direction to predict the cell fate. One of the forward, backward or both string.

  • interpolation_num (int) – The number of uniformly interpolated time points.

  • metric (str) – The distance metric to use for the tree. The default metric is ‘euclidean’, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of DistanceMetric for a list of available metrics. If metric is “precomputed”, X is assumed to be a distance matrix and must be square during fit. X may be a sparse graph, in which case only “nonzero” elements may be considered neighbors.

  • metric_kwds (Optional[dict]) – Additional keyword arguments for the metric function.

  • seed (int) – Random seed to ensure the reproducibility of each run.

  • kwargs – Additional arguments that will be passed to each nearest neighbor search algorithm.

Return type:

None

Returns:

Nothing but update the adata object with a new column in .obs that stores predicted ancestors or descendants.