Bug in TensorQTL - pandas & numpy conflict

[m-mews]

I tried to run a cis-eQTL analysis using TensorQTL, but received the below error. Upon investigating the dockerfile from which I built my singularity sif image, it appears that this pandas and numpy version conflict originate FROM gaow/base-notebook. Thank you in advance for your help!

Warning: 'rfunc' cannot be imported. R and the 'rpy2' Python package are needed. Mapping files: 100% | 3/3 [00:07<00:00, 2.45s/it] cis-QTL mapping: nominal associations for all variant-phenotype pairs

• 221 samples
• 1130 phenotypes
• 44 covariates
• 4817011 variants
• checking phenotypes: 1130/1130
• Computing associations Mapping chromosome 3 processing phenotype 1130/1130 time elapsed: 3.57 min
  • writing output Traceback (most recent call last): File "/home/mxm1368/.sos/19d2b6ee7290e278/singularity_run_25148.py", line 41, in cis.map_nominal(genotype_df, variant_df, File "/opt/conda/lib/python3.8/site-packages/tensorqtl/cis.py", line 475, in map_nominal chr_res_df.to_parquet(os.path.join(output_dir, f'{prefix}.cis_qtl_pairs.{chrom}.parquet')) File "/opt/conda/lib/python3.8/site-packages/pandas/util/_decorators.py", line 211, in wrapper return func(*args, **kwargs) File "/opt/conda/lib/python3.8/site-packages/pandas/core/frame.py", line 2976, in to_parquet return to_parquet( File "/opt/conda/lib/python3.8/site-packages/pandas/io/parquet.py", line 430, in to_parquet impl.write( File "/opt/conda/lib/python3.8/site-packages/pandas/io/parquet.py", line 174, in write table = self.api.Table.from_pandas(df, **from_pandas_kwargs) File "pyarrow/table.pxi", line 1393, in pyarrow.lib.Table.from_pandas File "/opt/conda/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 1027, in 'floating': np.float, File "/opt/conda/lib/python3.8/site-packages/numpy/init.py", line 305, in getattr raise AttributeError(former_attrs[attr]) AttributeError: module 'numpy' has no attribute 'float'. np.float was a deprecated alias for the builtin float. To avoid this error in existing code, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations