AttributeError: 'tuple' object has no attribute 'dim'

[pensive-bubble]

Hello again,

I have run into a further error while trying to run disease critical network demo. Tuple object causing further issues.

If you know how to fix this, it would be greatly appreciated!
Natasha

[kexinhuang12345]

Hi! are you using exactly the same code as in the demo? same model/gwas sumstats as well?

[pensive-bubble]

Hi! are you using exactly the same code as in the demo? same model/gwas sumstats as well?

Hello! I wasn't, I was using my own GWAS, but I've just tried it with the same model/gwas sumstats and have run into the same error.

[skoc]

Hi @pensive-bubble , can you make this small change and try again:

KGWAS/kgwas/utils.py

Line 461 in 68d6f60

x_dict = {i: j[0] for i,j in x_dict.items()}

That line will be:
x_dict = {i: j[0][0] for i,j in x_dict.items()}

[pensive-bubble]

Hi @pensive-bubble , can you make this small change and try again:

KGWAS/kgwas/utils.py

Line 461 in 68d6f60

x_dict = {i: j[0] for i,j in x_dict.items()}

That line will be:
x_dict = {i: j[0][0] for i,j in x_dict.items()}

Hello, I tried both on my GWAS and on the test one. I ended up with a fun new error.

[Himanshu-at-bayer]

Hi team, I am also having the same error while running the sample code :(

[skoc]

Hi @pensive-bubble , can you make this small change and try again:
KGWAS/kgwas/utils.py
Line 461 in 68d6f60
x_dict = {i: j[0] for i,j in x_dict.items()}
That line will be:
x_dict = {i: j[0][0] for i,j in x_dict.items()}

Hello, I tried both on my GWAS and on the test one. I ended up with a fun new error.

Thank you @pensive-bubble for the follow-up! Can make this pd concat change in your local version and try again:

# Instead of:
df_val_all = df_val_all.append(df_val)

# Use:
import pandas as pd
df_val_all = pd.concat([df_val_all, df_val], ignore_index=True)

Hey @Himanshu-at-bayer, you can also try x_dict = {i: j[0][0] for i,j in x_dict.items()} and pd concat change after that.

utils has multiple df_x.append(df_y) cases, changes has to be applied all

[Himanshu-at-bayer]

Thanks for the response and help. The first got removed after all the amendments you suggested but now got a new error

Retrieving weights...
Aggregating across node types...
Start generating disease critical network...
No filters... Using all genes and gene programs...

local/lib/python3.10/site-packages/kgwas/utils.py:590: FutureWarning: The provided callable <function std at 0x7f6dac644f70> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
rel2std = snp2genes_hit.groupby('rel_type').weight.agg(np.std).reset_index().rename(columns = {'weight': 'rel_type_std'})

ValueError Traceback (most recent call last)
Cell In[6], line 1
----> 1 df_network_weight, df_variant_interpretation, disease_critical_network = run.get_disease_critical_network(variant_threshold = 5e-8,
2 magma_path = None,
3 magma_threshold = 0.05,
4 program_threshold = 0.05,K_neighbors = 3,
5 num_cpus = 10)

File ~/.local/lib/python3.10/site-packages/kgwas/kgwas.py:272, in KGWAS.get_disease_critical_network(self, variant_threshold, magma_path, magma_threshold, program_threshold, K_neighbors, num_cpus)
268 def get_disease_critical_network(self, variant_threshold = 5e-8,
269 magma_path = None, magma_threshold = 0.05, program_threshold = 0.05,
270 K_neighbors = 3, num_cpus = 1):
271 df_network_weight = get_network_weight(self, self.data)
--> 272 df_variant_interpretation, disease_critical_network = generate_viz(self, df_network_weight, self.data_path, variant_threshold, magma_path, magma_threshold, program_threshold, K_neighbors, num_cpus)
273 return df_network_weight, df_variant_interpretation, disease_critical_network

File ~/.local/lib/python3.10/site-packages/kgwas/utils.py:600, in generate_viz(run, df_network, data_path, variant_threshold, magma_path, magma_threshold, program_threshold, K_neighbors, num_cpus)
598 v2g_hit = v2g_hit_with_rel_type[['h_idx', 't_idx', 'importance', 'h_type', 't_type', 'rel_type']]
599 v2g_hit.loc[:,'rel_type'] = v2g_hit.rel_type.apply(lambda x: x[4:])
--> 600 v2g_hit.loc[:,'Category'] = 'V2G'
602 v2g_hit.loc[:,'h_id'] = v2g_hit['h_idx'].apply(lambda x: idx2id['Gene'][x])
603 v2g_hit.loc[:,'t_id'] = v2g_hit['t_idx'].apply(lambda x: idx2id['SNP'][x])

File ~/.local/lib/python3.10/site-packages/pandas/core/indexing.py:911, in _LocationIndexer.setitem(self, key, value)
908 self._has_valid_setitem_indexer(key)
910 iloc = self if self.name == "iloc" else self.obj.iloc
--> 911 iloc._setitem_with_indexer(indexer, value, self.name)

File ~/.local/lib/python3.10/site-packages/pandas/core/indexing.py:1847, in _iLocIndexer._setitem_with_indexer(self, indexer, value, name)
1845 if not len(self.obj):
1846 if not is_list_like_indexer(value):
-> 1847 raise ValueError(
1848 "cannot set a frame with no "
1849 "defined index and a scalar"
1850 )
1851 self.obj[key] = value
1852 return

ValueError: cannot set a frame with no defined index and a scalar

[Himanshu-at-bayer]

This is line 590 in my case:

rel2std = snp2genes_hit.groupby('rel_type').weight.agg(np.std).reset_index().rename(columns = {'weight': 'rel_type_std'})

[jhchung]

I am also encountering this problem when there are no significant SNPs in the output. Some changes that seem to work (in addition to those already shared):

In the utils.py file, update lines 596, 613, and 631 to use assign instead of directly creating a new column.

For example, for line 596

# Old
v2g_hit.loc[:,'Category'] = 'V2G'

# Updated
v2g_hit = v2g_hit.assign(Category='V2G')

There is also a bug where the variant_threshold option in `get_disease_critical_network is not correctly used.

The GWAS P threshold is hardcoded in utils.py line 546

# Old 
hit_snps = gwas[gwas.P < 5e-8].SNP.values

# Updated
hit_snps = gwas[gwas.P < variant_threshold].SNP.values

[kexinhuang12345]

Nice! @jhchung would you like to make a PR for this? no worries if not, thanks!

[jhchung]

I need to do some additional testing to make sure these changes resolve the issue. but happy to make a PR when ready.

[kexinhuang12345]

Great - thank you!!

[Himanshu-at-bayer]

Thanks for the fix! Now at least I can run the codes.
However, I am not able to grasp the output of run.get_disease_crtiical_network :(
a. I can't reproduce the demo analysis even though I am running the same line of codes
b. df_variant_interpretation and disease_critical_network are returning empty even with low threshold cutoff for variant_threshold paramters.
c. How did you plotted the figures of variants connected with diseases/GO/other variants in the publication?

[jhchung]

I am also getting the same problem of no output using the test dataset.

I do see a warning that pops up which may be related to the pandas version. I have pandas v2.2.3 installed.

lib/python3.12/site-packages/kgwas/utils.py:591: FutureWarning: The provided callable <function std at 0x2aba9b549a80> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.

[jhchung]

I believe the problem is in the get_network_weight function in utils.py. I added a bunch of print statements for debugging purposes and ran this step from the tutorial. I think the problem is that h_idx and t_idx and weight are not correctly extracted (maybe?)

df_network_weight, df_variant_interpretation, disease_critical_network = run.get_disease_critical_network(
    variant_threshold = 5e-8, 
    magma_path = None,
    magma_threshold = 0.05,
    program_threshold = 0.05,
    K_neighbors = 3,
    num_cpus = 1
)

Retrieving weights...
Aggregating across node types...
get_network_weights - aggregate node_type: SNP
get_network_weights - aggregate node_type: CellularComponent
get_network_weights - aggregate node_type: BiologicalProcess
get_network_weights - aggregate node_type: MolecularFunction
get_network_weights - aggregate node_type: Gene
get_network_weights - rel:
('Gene', 'rev_ABC', 'SNP')
get_network_weights - value:
[[-2.0716644e-03 -3.1249187e-04  3.9275470e-03 -1.5760920e-03
   1.6780517e-03 -3.4461175e-03  2.5265825e-03 -1.0672063e-03
   4.0031904e-03 -1.0308591e-03  3.7549750e-04  3.1278137e-04
   1.5559015e-06 -8.3525910e-04  1.1605183e-03  7.4588216e-04
   3.3852728e-03  3.1510936e-05 -5.1043357e-04 -5.4528105e-06
  -2.9207388e-04 -9.1397684e-05  3.9466590e-04 -2.0442675e-03
  -2.3451431e-03  1.5296302e-03  3.6036740e-03 -1.8908285e-03
  -2.9544737e-03  2.9688796e-03  4.0026626e-04 -1.3758009e-03
   2.5131714e-03 -2.0040853e-03 -2.6214693e-04  1.1852352e-03
   4.8373509e-03  2.0383879e-04  2.8918989e-03  1.8276627e-03
   1.8066184e-03 -1.4352469e-03 -8.9567597e-04 -1.6585212e-04
  -1.2759508e-03  3.2460161e-03  8.7355809e-05 -3.0406982e-05
  -4.9260369e-04 -8.8233524e-04 -9.6033362e-04 -1.2771680e-03
   5.0848688e-04  7.9946208e-04 -4.3930097e-05 -8.5693604e-04
   5.7231671e-05  3.9964896e-03 -3.4840038e-04 -2.4563840e-04
  -1.2383851e-03  2.5187898e-04 -1.4556643e-03  8.0300146e-04
   8.7260461e-04 -3.7705237e-03 -1.6676611e-04 -8.5003514e-05
   3.8802988e-04 -1.0413895e-03  3.2610237e-03  8.2029984e-04
  -2.5317625e-03  2.9502739e-03  2.5283487e-03 -6.2756013e-04
  -1.3397745e-04  2.1540313e-03  1.3146071e-03  4.8091803e-03
  -1.3393254e-03  1.4644704e-04  2.4677929e-03  7.8216493e-07
   1.5113153e-05  1.2555042e-04 -1.4540972e-04 -5.3928798e-04
   3.5365629e-03 -8.3208119e-04 -8.0154219e-04  2.6310943e-05
   2.1220173e-04  2.0721429e-03  4.8096078e-03  3.2412109e-03
   2.2343721e-03  1.2657263e-03  1.0212656e-03 -2.2337919e-03
   2.5083928e-03 -9.0389340e-06 -1.7807685e-03  5.8993761e-04
  -1.2250998e-05 -8.5323356e-04 -2.1024502e-03  9.0994313e-04
   1.9092846e-03 -3.1042792e-04  5.3971214e-03  8.7288453e-04
   2.5811803e-03 -1.1200345e-03  3.7443175e-03 -2.9956179e-03
   4.0307203e-03 -1.1125518e-03 -5.4673717e-04 -4.7517638e-05
  -6.8724749e-04 -1.8124997e-03  5.1351654e-04 -1.1841567e-03
  -1.2070135e-03  4.3265740e-03 -6.8042194e-04  4.2584125e-04]
 [-2.0716644e-03 -3.1249187e-04  3.9275470e-03 -1.5760920e-03
   1.6780517e-03 -3.4461175e-03  2.5265825e-03 -1.0672063e-03
   4.0031904e-03 -1.0308591e-03  3.7549750e-04  3.1278137e-04
   1.5559015e-06 -8.3525910e-04  1.1605183e-03  7.4588216e-04
   3.3852728e-03  3.1510936e-05 -5.1043357e-04 -5.4528105e-06
  -2.9207388e-04 -9.1397684e-05  3.9466590e-04 -2.0442675e-03
  -2.3451431e-03  1.5296302e-03  3.6036740e-03 -1.8908285e-03
  -2.9544737e-03  2.9688796e-03  4.0026626e-04 -1.3758009e-03
   2.5131714e-03 -2.0040853e-03 -2.6214693e-04  1.1852352e-03
   4.8373509e-03  2.0383879e-04  2.8918989e-03  1.8276627e-03
   1.8066184e-03 -1.4352469e-03 -8.9567597e-04 -1.6585212e-04
  -1.2759508e-03  3.2460161e-03  8.7355809e-05 -3.0406982e-05
  -4.9260369e-04 -8.8233524e-04 -9.6033362e-04 -1.2771680e-03
   5.0848688e-04  7.9946208e-04 -4.3930097e-05 -8.5693604e-04
   5.7231671e-05  3.9964896e-03 -3.4840038e-04 -2.4563840e-04
  -1.2383851e-03  2.5187898e-04 -1.4556643e-03  8.0300146e-04
   8.7260461e-04 -3.7705237e-03 -1.6676611e-04 -8.5003514e-05
   3.8802988e-04 -1.0413895e-03  3.2610237e-03  8.2029984e-04
  -2.5317625e-03  2.9502739e-03  2.5283487e-03 -6.2756013e-04
  -1.3397745e-04  2.1540313e-03  1.3146071e-03  4.8091803e-03
  -1.3393254e-03  1.4644704e-04  2.4677929e-03  7.8216493e-07
   1.5113153e-05  1.2555042e-04 -1.4540972e-04 -5.3928798e-04
   3.5365629e-03 -8.3208119e-04 -8.0154219e-04  2.6310943e-05
   2.1220173e-04  2.0721429e-03  4.8096078e-03  3.2412109e-03
   2.2343721e-03  1.2657263e-03  1.0212656e-03 -2.2337919e-03
   2.5083928e-03 -9.0389340e-06 -1.7807685e-03  5.8993761e-04
  -1.2250998e-05 -8.5323356e-04 -2.1024502e-03  9.0994313e-04
   1.9092846e-03 -3.1042792e-04  5.3971214e-03  8.7288453e-04
   2.5811803e-03 -1.1200345e-03  3.7443175e-03 -2.9956179e-03
   4.0307203e-03 -1.1125518e-03 -5.4673717e-04 -4.7517638e-05
  -6.8724749e-04 -1.8124997e-03  5.1351654e-04 -1.1841567e-03
  -1.2070135e-03  4.3265740e-03 -6.8042194e-04  4.2584125e-04]]
      h_idx     t_idx h_type rel_type t_type layer  weight
0 -0.002072 -0.002072   Gene  rev_ABC    SNP    l1     NaN
1 -0.000312 -0.000312   Gene  rev_ABC    SNP    l1     NaN
2  0.003928  0.003928   Gene  rev_ABC    SNP    l1     NaN
3 -0.001576 -0.001576   Gene  rev_ABC    SNP    l1     NaN
4  0.001678  0.001678   Gene  rev_ABC    SNP    l1     NaN
Start generating disease critical network...
      h_idx     t_idx h_type rel_type t_type layer  weight
0 -0.002072 -0.002072   Gene  rev_ABC    SNP    l1     NaN
1 -0.000312 -0.000312   Gene  rev_ABC    SNP    l1     NaN
2  0.003928  0.003928   Gene  rev_ABC    SNP    l1     NaN
3 -0.001576 -0.001576   Gene  rev_ABC    SNP    l1     NaN
4  0.001678  0.001678   Gene  rev_ABC    SNP    l1     NaN
No filters... Using all genes and gene programs...
generate_viz: snp2genes
      h_idx     t_idx h_type rel_type t_type layer  weight
0 -0.002072 -0.002072   Gene  rev_ABC    SNP    l1     NaN
1 -0.000312 -0.000312   Gene  rev_ABC    SNP    l1     NaN
2  0.003928  0.003928   Gene  rev_ABC    SNP    l1     NaN
3 -0.001576 -0.001576   Gene  rev_ABC    SNP    l1     NaN
4  0.001678  0.001678   Gene  rev_ABC    SNP    l1     NaN
generate_viz: snp2genes_hit
Empty DataFrame
Columns: [h_idx, t_idx, h_type, rel_type, t_type, layer, weight]
Index: []
generate_viz: snp2genes_hit with z_rel
Empty DataFrame
Columns: [h_idx, t_idx, h_type, rel_type, t_type, layer, weight, rel_type_std, rel_type_mean, z_rel]
Index: []
Empty DataFrame
Columns: [h_idx, t_idx, h_type, rel_type, t_type, layer, weight, rel_type_std, rel_type_mean, importance]
Index: []

[jhchung]

Unfortunately, I won't be able to do too much more. Things are getting a bit over my head with the ML portions.

[Himanshu-at-bayer]

@jhchung Did you manage to reproduce the results?
@kexinhuang12345 Would you be kind to share your thoughts on this and is there any codes for generating knowledge graph from scratch for the datasets you used?

[kexinhuang12345]

Thanks for trying it out! @jhchung
Sorry for being late in response - i was stranded with a few deadlines.
I will take a deep look into this by end of next week!
I can also share the notebook for generating the knowledge graph, and extension to larger variant set but it is a bit messy so needs lots of clean up.

[kexinhuang12345]

Hi - i re-run the code and there is no bug reported.
Could you share the environment versions? Especially the pytorch geometric related packages?

[Himanshu-at-bayer]

I actually used the .yml file what is provided and the python version 3.12.9

aiohappyeyeballs @ file:///home/conda/feedstock_root/build_artifacts/aiohappyeyeballs_1741285157081/work aiohttp @ file:///home/conda/feedstock_root/build_artifacts/aiohttp_1740481426959/work aiosignal @ file:///home/conda/feedstock_root/build_artifacts/aiosignal_1734342155601/work asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1733250440834/work attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1737819173731/work Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1725267488082/work certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1739515848642/work/certifi cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1725560558132/work charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1735929714516/work colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1733218098505/work comm @ file:///home/conda/feedstock_root/build_artifacts/comm_1733502965406/work debugpy @ file:///home/conda/feedstock_root/build_artifacts/debugpy_1741148399929/work decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1740384970518/work exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1733208806608/work executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1733569351617/work filelock @ file:///home/conda/feedstock_root/build_artifacts/filelock_1737517818712/work frozenlist @ file:///home/conda/feedstock_root/build_artifacts/frozenlist_1737645236190/work fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1741403990995/work gmpy2 @ file:///home/conda/feedstock_root/build_artifacts/gmpy2_1733462543063/work h2 @ file:///home/conda/feedstock_root/build_artifacts/h2_1738578511449/work hpack @ file:///home/conda/feedstock_root/build_artifacts/hpack_1737618293087/work hyperframe @ file:///home/conda/feedstock_root/build_artifacts/hyperframe_1737618333194/work idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1733211830134/work importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1737420181517/work ipykernel @ file:///home/conda/feedstock_root/build_artifacts/ipykernel_1719845459717/work ipython @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_ipython_1741457126/work ipython_pygments_lexers @ file:///home/conda/feedstock_root/build_artifacts/ipython_pygments_lexers_1737123620466/work jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1733300866624/work Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1741263328855/work joblib==1.4.2 jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1733440914442/work jupyter_core @ file:///home/conda/feedstock_root/build_artifacts/jupyter_core_1727163409502/work KGWAS==0.0.4 MarkupSafe @ 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file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_pycparser_1733195786/work Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1736243443484/work pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1735698276679/work PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1733217236728/work python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1733215673016/work pytz==2025.1 PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1737454647378/work pyzmq @ file:///home/conda/feedstock_root/build_artifacts/pyzmq_1738270962252/work requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1733217035951/work scikit-learn==1.6.1 scipy @ file:///home/conda/feedstock_root/build_artifacts/scipy-split_1739790643552/work/dist/scipy-1.15.2-cp312-cp312-linux_x86_64.whl#sha256=9c4c390e67a1320f6f3b418568855f0a6759c34ce8e5b9eb6cc90d02fd7ba711 setuptools==75.8.2 six @ 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urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1734859416348/work wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1733231326287/work wheel==0.45.1 yarl @ file:///home/conda/feedstock_root/build_artifacts/yarl_1737575777699/work zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1732827521216/work zstandard @ file:///work/perseverance-python-buildout/croot/zstandard_1698847073368/work

[Himanshu-at-bayer]

@kexinhuang12345 Also as you mentioned earlier that you could share the notebook for generating the KG :) Would be nice to have it and please don't need to polish the notebook ;)