Error about group parameter in dyn. tl. dynamics

[mingjianPeng]

Dear Xiaojie,
My data is pulse-chase data from scNT-seq, three temperatures were used for processing, but an error occurs when running dyn. tl. dynamics(adata, group = 'temp'), here's my run code：
Integrate AnnData list and filter

def add_lable_filter(ad_dict,mito_perc):
    adata = dyn.pp.integrate(list(ad_dict.values()), batch_key='batch', fill_value=0)
    adata=ov.pp.qc(adata,
              tresh={'mito_perc': mito_perc, 'nUMIs': 500, 'detected_genes': 400},mt_startswith='mt-',)
    adata.layers['total'] = adata.X
    adata.layers['new'] = adata.layers['X_l_TC']
    adata.layers.pop('X_l_TC', None)
    adata.layers.pop('X_n_TC', None)
    adata.obs['label_time'] = 24 # this is the labeling time
    tkey = 'label_time'
    adata.obs['time'] = adata.obs['time']/60
    return(adata)
adata_merge = add_lable_filter(merge_dict,0.15)

log:

|-----> Running monocle preprocessing pipeline...
|-----------> filtered out 0 outlier cells
|-----------> filtered out 14130 outlier genes
|-----> PCA dimension reduction
|-----> <insert> X_pca to obsm in AnnData Object.
|-----> computing cell phase...
|-----? 
Dynamo is not able to perform cell cycle staging for you automatically. 
Since dyn.pl.phase_diagram in dynamo by default colors cells by its cell-cycle stage, 
you need to set color argument accordingly if confronting errors related to this.
|-----> [Preprocessor-monocle] completed [38.2824s]

run preproces and dynamics

adata_dyn = adata_merge.copy()
preprocessor = dyn.pp.Preprocessor(cell_cycle_score_enable=True)
preprocessor.config_monocle_recipe(adata_dyn)
preprocessor.filter_cells_by_outliers_kwargs["keep_filtered"] = True
preprocessor.preprocess_adata_monocle(adata_dyn, tkey='label_time', experiment_type='one-shot')
dyn.tl.dynamics(adata_dyn,group = 'temp')

An error occurred:

ValueError                                Traceback (most recent call last)
Cell In[155], line 1
----> 1 dyn.tl.dynamics(adata_dyn,group = 'temp')

File ~/miniconda3/envs/dynamo1.3/lib/python3.8/site-packages/dynamo/tools/dynamics.py:700, in dynamics(adata, filter_gene_mode, use_smoothed, assumption_mRNA, assumption_protein, model, est_method, NTR_vel, group, protein_names, concat_data, log_unnormalized, one_shot_method, fraction_for_deg, re_smooth, sanity_check, del_2nd_moments, cores, tkey, **est_kwargs)
    684     vel_P = vel.vel_p(S, P)
    686     adata = set_velocity(
    687         adata,
    688         vel_U,
   (...)
    697         ind_for_proteins,
    698     )
--> 700     adata = set_param_ss(
    701         adata,
    702         est,
    703         alpha,
    704         beta,
    705         gamma,
    706         eta,
    707         delta,
    708         experiment_type,
    709         _group,
    710         cur_grp,
    711         kin_param_pre,
    712         valid_bools_,
    713         ind_for_proteins,
    714     )
    716 elif assumption_mRNA.lower() == "kinetic":
    717     return_ntr = True if fraction_for_deg and experiment_type.lower() == "deg" else False

File ~/miniconda3/envs/dynamo1.3/lib/python3.8/site-packages/dynamo/tools/utils.py:1428, in set_param_ss(adata, est, alpha, beta, gamma, eta, delta, experiment_type, _group, cur_grp, kin_param_pre, valid_ind, ind_for_proteins)
   1422     if cur_grp == _group[0]:
   1423         adata.varm[kin_param_pre + "alpha"] = (
   1424             sp.csr_matrix(np.zeros(adata.shape[::-1]))
   1425             if sp.issparse(alpha)
   1426             else np.zeros(adata.shape[::-1])
   1427         )  #
-> 1428     adata.varm[kin_param_pre + "alpha"][valid_ind, :] = alpha  #
   1429     adata.var.loc[valid_ind, kin_param_pre + "alpha"] = alpha.mean(1)
   1430 elif len(alpha.shape) == 1:

File ~/.local/lib/python3.8/site-packages/scipy/sparse/_index.py:132, in IndexMixin.__setitem__(self, key, x)
    129 broadcast_col = x.shape[1] == 1 and i.shape[1] != 1
    130 if not ((broadcast_row or x.shape[0] == i.shape[0]) and
    131         (broadcast_col or x.shape[1] == i.shape[1])):
--> 132     raise ValueError('shape mismatch in assignment')
    133 if x.shape[0] == 0 or x.shape[1] == 0:
    134     return

ValueError: shape mismatch in assignment

I want to know whether it is feasible to analyze different samples simultaneously in an AnnData ? Is it where I got it wrong when I was analyzing ?
Best regard.

[mingjianPeng]

@Xiaojieqiu I'd appreciate it if you'd take the time to look at it for me.

[Sichao25]

Hi, thank you for raising this issue. This looks like a bug when setting the alpha back to the original data. I will take a look at it. But I do suggest you analyze those samples separately for now since they may have different velocity parameters.

[mingjianPeng]

Thanks so much for your reply.Then I'll try to analyze it separately first.

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