Closed zelhar closed 12 months ago
Sichao25
commented
1 year ago Hi, thanks for reporting the issue. Could you provide the complete traceback output to help us better identify the problem? Also, it will be helpful to provide your Dynamo version and Numpy version. I can't reproduce the error on my end so I suspect it's related to a specific version.
zelhar
commented
1 year ago Hi. So here are the details: python version: 3.11.4 numpy: 1.24.4 pandas:2.0.3 dynamo: 1.3.2
And this is the output I get from dyn.tl.dynamics(adata, model='deterministic', tkey='time', assumption_mRNA='ss'):
In [49]: dyn.tl.dynamics(adata, model='deterministic', tkey='time', assumption_mRNA='ss')
|-----> dynamics_del_2nd_moments_key is None. Using default value from DynamoAdataConfig: dynamics_del_2nd_moments_key=False
|-----? Looks like you are using minutes as the time unit. For the purpose of numeric stability, we recommend using hour as the time unit.
|-----------> removing existing M layers:[]...
|-----------> making adata smooth...
|-----> calculating first/second moments...
|-----> [moments calculation] completed [22.1245s]
|-----? Your adata only has labeling data, but `NTR_vel` is set to be `False`. Dynamo will reset it to `True` to enable this analysis.
|-----> experiment type: kin, method: direct, model: deterministic
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[49], line 1
----> 1 dyn.tl.dynamics(adata, model='deterministic', tkey='time', assumption_mRNA='ss')
File ~/micromamba/envs/mdc/lib/python3.11/site-packages/dynamo/tools/dynamics.py:725, 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)
722 est_method = "direct"
723 data_type = "smoothed" if use_smoothed else "sfs"
--> 725 (params, half_life, cost, logLL, param_ranges, cur_X_data, cur_X_fit_data,) = kinetic_model(
726 subset_adata,
727 tkey,
728 model,
729 est_method,
730 experiment_type,
731 has_splicing,
732 splicing_labeling,
733 has_switch=True,
734 param_rngs={},
735 data_type=data_type,
736 return_ntr=return_ntr,
737 **est_kwargs,
738 )
740 if type(params) == dict:
741 alpha = params.pop("alpha")
File ~/micromamba/envs/mdc/lib/python3.11/site-packages/dynamo/tools/dynamics.py:1323, in kinetic_model(subset_adata, tkey, model, est_method, experiment_type, has_splicing, splicing_labeling, has_switch, param_rngs, data_type, return_ntr, **est_kwargs)
1321 if model.lower() in ["deterministic", "stochastic"]:
1322 layer = "M_n" if ("M_n" in subset_adata.layers.keys() and data_type == "smoothed") else "X_new"
-> 1323 X, X_raw = prepare_data_no_splicing(
1324 subset_adata,
1325 subset_adata.var.index,
1326 time,
1327 layer=layer,
1328 total_layer=total_layer,
1329 )
1330 elif model.lower().startswith("mixture"):
1331 layers = (
1332 ["M_n", "M_t"]
1333 if ("M_n" in subset_adata.layers.keys() and data_type == "smoothed")
1334 else ["X_new", "X_total"]
1335 )
File ~/micromamba/envs/mdc/lib/python3.11/site-packages/dynamo/tools/moments.py:721, in prepare_data_no_splicing(adata, genes, time, layer, use_total_layers, total_layer, return_old, return_ntr)
719 else:
720 sfs = adata.obs.total_Size_Factor
--> 721 sfs, tot_sfs = sfs[:, None], sfs[:, None]
722 else:
723 sfs, _ = sz_util(
724 adata,
725 layer,
(...)
730 CM=layer_data,
731 )
File ~/micromamba/envs/mdc/lib/python3.11/site-packages/pandas/core/series.py:1033, in Series.__getitem__(self, key)
1030 key = np.asarray(key, dtype=bool)
1031 return self._get_values(key)
-> 1033 return self._get_with(key)
File ~/micromamba/envs/mdc/lib/python3.11/site-packages/pandas/core/series.py:1048, in Series._get_with(self, key)
1043 raise TypeError(
1044 "Indexing a Series with DataFrame is not "
1045 "supported, use the appropriate DataFrame column"
1046 )
1047 elif isinstance(key, tuple):
-> 1048 return self._get_values_tuple(key)
1050 elif not is_list_like(key):
1051 # e.g. scalars that aren't recognized by lib.is_scalar, GH#32684
1052 return self.loc[key]
File ~/micromamba/envs/mdc/lib/python3.11/site-packages/pandas/core/series.py:1082, in Series._get_values_tuple(self, key)
1077 if com.any_none(*key):
1078 # mpl compat if we look up e.g. ser[:, np.newaxis];
1079 # see tests.series.timeseries.test_mpl_compat_hack
1080 # the asarray is needed to avoid returning a 2D DatetimeArray
1081 result = np.asarray(self._values[key])
-> 1082 disallow_ndim_indexing(result)
1083 return result
1085 if not isinstance(self.index, MultiIndex):
File ~/micromamba/envs/mdc/lib/python3.11/site-packages/pandas/core/indexers/utils.py:343, in disallow_ndim_indexing(result)
335 """
336 Helper function to disallow multi-dimensional indexing on 1D Series/Index.
337
(...)
340 in GH#30588.
341 """
342 if np.ndim(result) > 1:
--> 343 raise ValueError(
344 "Multi-dimensional indexing (e.g. `obj[:, None]`) is no longer "
345 "supported. Convert to a numpy array before indexing instead."
346 )
ValueError: Multi-dimensional indexing (e.g. `obj[:, None]`) is no longer supported. Convert to a numpy array before indexing instead.
Interesting. Could you take a look at adata.uns["pp"] after preprocessing? When I run the tutorial in Colab, the experiment type is one-shot because the "time" observation is all the same. Just from your output, your program is using kinetics experiments instead. It will also be helpful to output the np.unique(adata.obs.time) to help understand the reason.
The error itself seems to be caused by Pandas. Multi-dimensional indexing has been deprecated in the latest version. If you downgrade it to an earlier version, the error may be replaced by a warning.
github-actions[bot]
commented
1 year ago This issue is stale because it has been open 90 days with no activity. Remove stale label or comment or this will be closed in 14 days
I tried to follow exactly the example from: https://dynamo-release.readthedocs.io/en/latest/notebooks/scEU_seq_organoid_analysis_kinetic.html
calling
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