def cell_fate(data, groupby='clusters', disconnected_groups=None, self_transitions=False, n_neighbors=None, copy=False):
    adata = data.copy() if copy else data
    logg.info('computing cell fates', r=True)
    n_neighbors = 10 if n_neighbors is None else n_neighbors
    _adata = adata.copy()
    vgraph = VelocityGraph(_adata, n_neighbors=n_neighbors, approx=True, n_recurse_neighbors=1)
    vgraph.compute_cosines()
    _adata.uns['velocity_graph'] = vgraph.graph
    _adata.uns['velocity_graph_neg'] = vgraph.graph_neg
    T = transition_matrix(_adata, self_transitions=self_transitions)
    I = np.eye(_adata.n_obs)
    fate = np.linalg.inv(I - T)
    if issparse(T): fate = fate.A
    cell_fates = np.array(_adata.obs[groupby][fate.argmax(1)])
    if disconnected_groups is not None:
        idx = _adata.obs[groupby].isin(disconnected_groups)
        cell_fates[idx] = _adata.obs[groupby][idx]
    adata.obs['cell_fate'] = cell_fates
    adata.obs['cell_fate_confidence'] = fate.max(1) / fate.sum(1)
    strings_to_categoricals(adata)
    logg.info('    finished', time=True, end=' ' if settings.verbosity > 2 else '\n')
    logg.hint(
        'added\n'
        '    \'cell_fate\', most likely cell fate (adata.obs)\n'
        '    \'cell_fate_confidence\', confidence of transitioning to the assigned fate (adata.obs)')
    return adata if copy else None