The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

[22219098]

Hi,
we met the problem when we ran code with our own adata:
AnnData object with n_obs × n_vars = 28374 × 25086
obs: 'cell_subtype', 'batch', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt'
var: 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
uns: 'cellchat_output', 'log1p', 'pyliger_info', 'flowsig_network', 'flowsig_network_orig'
obsm: 'X_gem', 'X_flow', 'X_flow_orig'
layers: 'counts'

fs.tl.apply_biological_flow(adata,
                            flowsig_network_key = 'flowsig_network',
                            adjacency_key = 'adjacency',
                            validated_key = 'validated')

This is the error report:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_83050/680968092.py in ?()
----> 1 fs.tl.apply_biological_flow(adata,
      2                             flowsig_network_key = 'flowsig_network',
      3                             adjacency_key = 'adjacency',
      4                             validated_key = 'validated')

~/.conda/envs/flowsigenv/lib/python3.8/site-packages/flowsig/tools/_validate_network.py in ?(adata, flowsig_network_key, adjacency_key, validated_key)
    183         # Define the edge because we may need to reverse it
    184         edge = (node_1, node_2)
    185 
    186         # If there's a link from received morphogen to a TF
--> 187         if ( (node_1_type == 'inflow')&(node_2_type == 'module') ):
    188 
    189             add_edge = True
    190 

~/.conda/envs/flowsigenv/lib/python3.8/site-packages/pandas/core/generic.py in ?(self)
   1464     @final
   1465     def __nonzero__(self) -> NoReturn:
-> 1466         raise ValueError(
   1467             f"The truth value of a {type(self).__name__} is ambiguous. "
   1468             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
   1469         )

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

But when we tried to run tutorial on example burkhardt21_merged.h5ad, this error did not occur.
AnnData object with n_obs × n_vars = 5305 × 18027
obs: 'sample_labels', 'Donor', 'Condition', 'library_size', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'doublet_score', 'predicted_doublet', 'n_counts', 'log_counts', 'n_genes', 'leiden', 'Type'
var: 'gene_symbols', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection'
uns: 'Condition', 'Condition_colors', 'Donor_colors', 'NMF_10', 'NMF_CV', 'Type', 'base_networks', 'base_networks_leiden', 'causal_networks', 'causal_networks_leiden', 'cellchat_output', 'cpdb_Type', 'flowsig_network', 'flowsig_network_cpdb', 'flowsig_network_cpdb_orig', 'flowsig_network_orig', 'hvg', 'learned_networks', 'leiden', 'leiden_colors', 'log1p', 'neighbors', 'pca', 'pyliger', 'pyliger_10', 'pyliger_11', 'pyliger_12', 'pyliger_15', 'pyliger_20', 'pyliger_3', 'pyliger_5', 'pyliger_8', 'pyliger_9', 'pyliger_info', 'pyliger_vars', 'sample_labels_colors', 'scrublet', 'umap'
obsm: 'X_SC', 'X_celltype_ligand', 'X_celltype_ligand_leiden', 'X_flow', 'X_flow_cpdb', 'X_flow_cpdb_orig', 'X_flow_orig', 'X_gem', 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts', 'normalized'
obsp: 'connectivities', 'distances'

My environment is :
Package Version

---

absl-py 2.1.0
adjustText 1.2.0
aiohappyeyeballs 2.4.0
aiohttp 3.10.5
aiosignal 1.3.1
anndata 0.9.2
annoy 1.17.3
asciitree 0.3.3
asttokens 2.0.5
astunparse 1.6.3
async-timeout 4.0.3
attrs 24.2.0
backcall 0.2.0
backports.zoneinfo 0.2.1
biothings-client 0.3.1
bleach 6.1.0
bokeh 3.1.1
cachetools 5.5.0
causaldag 0.1a163
certifi 2024.8.30
charset-normalizer 3.3.2
click 8.1.7
cloudpickle 3.0.0
colorcet 3.1.0
comm 0.2.1
conditional-independence 0.1a6
contourpy 1.1.1
cycler 0.12.1
dask 2023.5.0
dask-image 2023.3.0
dataclasses 0.6
datashader 0.15.2
datashape 0.5.2
debugpy 1.6.7
decorator 5.1.1
dill 0.3.8
dm-tree 0.1.8
docopt 0.6.2
docrep 0.3.2
einops 0.8.0
et-xmlfile 1.1.0
executing 0.8.3
fasteners 0.19
filelock 3.16.1
flatbuffers 24.3.25
flowsig 0.1.1
fonttools 4.54.0
frozendict 2.4.4
frozenlist 1.4.1
fsspec 2024.9.0
ftpretty 0.4.0
gast 0.4.0
get-annotations 0.1.2
goatools 1.4.12
google-ai-generativelanguage 0.1.0
google-api-core 2.20.0
google-auth 2.35.0
google-auth-oauthlib 1.0.0
google-generativeai 0.1.0rc1
google-pasta 0.2.0
googleapis-common-protos 1.65.0
graphical-model-learning 0.1a8
graphical-models 0.1a21
grpcio 1.66.1
grpcio-status 1.62.3
h5py 3.11.0
h5sparse 0.1.0
holoviews 1.17.1
idna 3.10
igraph 0.11.6
imageio 2.35.1
importlib-metadata 7.0.1
importlib_resources 6.4.5
inflect 7.0.0
ipdb 0.13.13
ipykernel 6.28.0
ipython 8.12.2
jedi 0.19.1
Jinja2 3.1.4
joblib 1.4.2
jupyter_client 8.6.0
jupyter_core 5.7.2
keras 2.13.1
kiwisolver 1.4.7
lazy_loader 0.4
leidenalg 0.10.2
libclang 18.1.1
linkify-it-py 2.0.3
llvmlite 0.41.1
locket 1.0.0
louvain 0.8.2
Markdown 3.7
markdown-it-py 3.0.0
MarkupSafe 2.1.5
matplotlib 3.7.5
matplotlib-inline 0.1.6
matplotlib-scalebar 0.8.1
mdit-py-plugins 0.4.2
mdurl 0.1.2
mizani 0.9.3
mpmath 1.3.0
multidict 6.1.0
multipledispatch 1.0.0
mygene 3.2.2
natsort 8.4.0
nest-asyncio 1.6.0
networkx 3.1
numba 0.58.1
numcodecs 0.12.1
numexpr 2.8.6
numpy 1.24.3
nvidia-cublas-cu12 12.1.3.1
nvidia-cuda-cupti-cu12 12.1.105
nvidia-cuda-nvrtc-cu12 12.1.105
nvidia-cuda-runtime-cu12 12.1.105
nvidia-cudnn-cu12 8.9.2.26
nvidia-cufft-cu12 11.0.2.54
nvidia-curand-cu12 10.3.2.106
nvidia-cusolver-cu12 11.4.5.107
nvidia-cusparse-cu12 12.1.0.106
nvidia-nccl-cu12 2.18.1
nvidia-nvjitlink-cu12 12.6.68
nvidia-nvtx-cu12 12.1.105
oauthlib 3.2.2
omnipath 1.0.8
openpyxl 3.1.5
opt-einsum 3.3.0
packaging 24.1
pandas 2.0.3
panel 1.2.3
param 2.1.1
parso 0.8.3
partd 1.4.1
patsy 0.5.6
pexpect 4.8.0
pgmpy 0.1.26
pickleshare 0.7.5
pillow 10.4.0
PIMS 0.7
pip 24.2
platformdirs 3.10.0
plotnine 0.12.4
progressbar2 4.5.0
prompt-toolkit 3.0.43
proto-plus 1.24.0
protobuf 4.25.5
psutil 5.9.0
ptyprocess 0.7.0
pure-eval 0.2.2
pyasn1 0.6.1
pyasn1_modules 0.4.1
pyct 0.5.0
pydantic 1.10.18
pydot 3.0.1
pygam 0.9.1
Pygments 2.15.1
pyliger 0.2.0
pynndescent 0.5.13
pyparsing 3.1.4
python-dateutil 2.9.0.post0
python-igraph 0.11.6
python-utils 3.8.2
pytz 2024.2
pyviz_comms 3.0.3
PyWavelets 1.4.1
PyYAML 6.0.2
pyzmq 25.1.2
requests 2.32.3
requests-oauthlib 2.0.0
rich 13.8.1
rsa 4.9
scanpy 1.9.8
scikit-image 0.21.0
scikit-learn 1.3.2
scipy 1.10.1
seaborn 0.13.2
session-info 1.0.0
setuptools 75.1.0
six 1.16.0
slicerator 1.1.0
spatial-factorization 0.0.1
squidpy 1.2.2
stack-data 0.2.0
statsmodels 0.14.1
stdlib-list 0.10.0
sympy 1.13.3
tensorboard 2.13.0
tensorboard-data-server 0.7.2
tensorflow 2.13.1
tensorflow-estimator 2.13.0
tensorflow-io-gcs-filesystem 0.34.0
tensorflow-probability 0.21.0
termcolor 2.4.0
texttable 1.7.0
threadpoolctl 3.5.0
tifffile 2023.7.10
tomli 2.0.1
toolz 0.12.1
torch 2.1.2
tornado 6.4.1
tqdm 4.66.5
traitlets 5.14.3
triton 2.1.0
typing 3.7.4.3
typing_extensions 4.5.0
tzdata 2024.2
uc-micro-py 1.0.3
umap-learn 0.5.6
urllib3 2.2.3
validators 0.34.0
wcwidth 0.2.5
webencodings 0.5.1
Werkzeug 3.0.4
wheel 0.44.0
wrapt 1.16.0
xarray 2023.1.0
xgboost 2.1.1
XlsxWriter 3.2.0
xyzservices 2024.9.0
yarl 1.12.1
zarr 2.16.1
zipp 3.17.0

Thanks and best wishes

[Zhaowado]

Yes, I also encountered this problem.Have you solved it?
This error is usually caused by the inconsistency of the data types used for comparison.I guess it may be because the input node_1_type or node_2_type are not all str types.
However, I still haven't solved it, hope to get a reply too.

[zhangpan-2018]

I also encountered a similar issue where the node_1_type/node_2_type was not a string, and it could be both an inflow and outflow at the same time, which caused the error. I later modified it to only take the first one, which solved the problem. Here is the specific modification:

I changed the part of the construct_intercellular_flow_network function where:
node_1_type = flow_var_info.loc[node_1]['Type']
node_2_type = flow_var_info.loc[node_2]['Type']

to:

Classify node types

temp_type1 = flow_var_info.loc[node_1]['Type']
node_1_type = temp_type1.values[0] if not isinstance(temp_type1, str) else temp_type1
temp_type2 = flow_var_info.loc[node_2]['Type']
node_2_type = temp_type2.values[0] if not isinstance(temp_type2, str) else temp_type2

[shisang7]

I seem to have solved this problem by the definition of apply_biological_flow

[fmulenge]

@22219098 I encountered similar error 'ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()'. To resolve this construct cellChatDB.use using 'Secreted Signaling'.
CellChatDB.use<- subsetDB(cellChatDB, search='Secreted Signaling').
Good luck 👍

[axelalmet]

Hi 22219098,

Thank you for the question and I'm very sorry for my extremely late reply. One reason I can think that this is the case is that you have two nodes of different types with the same name. The most likely case when this would happen is that you have an inflow node and outflow node that are the same. For example, if you have inferred cell-cell communication and inferred Cell-Cell Contact-based interactions, e.g., Cdh1-Cdh1 from the Cadherin CDH pathway.

This is why fmulenge's solution works.

Do you mind checking for me if that's the case? If not, I'll have to come back to the drawing board. Thank you!

Best wishes,
Axel.

[hajihajilala]

Hi Axel,
I encountered similar question like 22219098, and I check my data and find there are an inflow node and outflow node that are the same name. I think it's the reason of this error.

[hajihajilala]

Hi Axel,
Cell-Cell contact is important in my research, I don't want to lose these information. Are there other ways to solve this question? Thank you.