def transition_matrix(adata, vkey='velocity', basis=None, backward=False, self_transitions=True, scale=10, perc=None,
                      use_negative_cosines=False, weight_diffusion=0, scale_diffusion=1, weight_indirect_neighbors=None,
                      n_neighbors=None, vgraph=None):
    if vkey+'_graph' not in adata.uns:
        raise ValueError('You need to run `tl.velocity_graph` first to compute cosine correlations.')
    graph = csr_matrix(adata.uns[vkey + '_graph']).copy() if vgraph is None else vgraph.copy()
    if self_transitions:
        confidence = graph.max(1).A.flatten()
        ub = np.percentile(confidence, 98)
        self_prob = np.clip(ub - confidence, 0, 1)
        graph.setdiag(self_prob)
    T = np.expm1(graph * scale)
    if vkey + '_graph_neg' in adata.uns.keys():
        graph_neg = adata.uns[vkey + '_graph_neg']
        if use_negative_cosines:
            T -= np.expm1(-graph_neg * scale)
        else:
            T += np.expm1(graph_neg * scale)
            T.data += 1
    if 'neighbors' in adata.uns.keys() and weight_indirect_neighbors is not None and weight_indirect_neighbors < 1:
        direct_neighbors = adata.uns['neighbors']['distances'] > 0
        direct_neighbors.setdiag(1)
        w = weight_indirect_neighbors
        T = w * T + (1-w) * direct_neighbors.multiply(T)
    if backward: T = T.T
    T = normalize(T)
    if n_neighbors is not None:
        T = T.multiply(get_connectivities(adata, mode='distances', n_neighbors=n_neighbors, recurse_neighbors=True))
    if perc is not None:
        threshold = np.percentile(T.data, perc)
        T.data[T.data < threshold] = 0
        T.eliminate_zeros()
    if 'X_' + str(basis) in adata.obsm.keys():
        dists_emb = (T > 0).multiply(squareform(pdist(adata.obsm['X_' + basis])))
        scale_diffusion *= dists_emb.data.mean()
        diffusion_kernel = dists_emb.copy()
        diffusion_kernel.data = np.exp(-.5 * dists_emb.data ** 2 / scale_diffusion ** 2)
        T = T.multiply(diffusion_kernel)
        if 0 < weight_diffusion < 1:
            diffusion_kernel.data = np.exp(-.5 * dists_emb.data ** 2 / (scale_diffusion/2) ** 2)
            T = (1-weight_diffusion) * T + weight_diffusion * diffusion_kernel
        T = normalize(T)
    return T