Open JRZL123 opened 2 years ago
Install pySCENIC 0.12.0: https://pypi.org/project/pyscenic/
Install pySCENIC 0.12.0: https://pypi.org/project/pyscenic/<
Same problem, nothings change (ㄒoㄒ)
Install pySCENIC 0.12.0: https://pypi.org/project/pyscenic/
pretty sure"mm9.feather" it's the problem. I used "mm10_10kbp_up_10kbp_down_full_tx_clustered.genes_vs_motifs.rankings.feather" & "mm10_500bp_up_100bp_down_full_tx_clustered.genes_vs_motifs.rankings.feather" run perfectly find! Now the only question is, if I use the "mm10.feather" to analyze on the count matrix obtained by "mm9", will there be serious consequences?
Install pySCENIC 0.12.0: https://pypi.org/project/pyscenic/
pretty sure"mm9.feather" it's the problem. I used "mm10_10kbp_up_10kbp_down_full_tx_clustered.genes_vs_motifs.rankings.feather" & "mm10_500bp_up_100bp_down_full_tx_clustered.genes_vs_motifs.rankings.feather" run perfectly find! Now the only question is, if I use the "mm10.feather" to analyze on the count matrix obtained by "mm9", will there be serious consequences?
Normally not a lot. It might even be better as the mm9 gene annotation used in the old database was quite old (so you might recover more genes from your count matrix).
Hi, I'm having the same issue with the mm10 files, running pySCENIC 0.12.0.
ValueError: "mm10_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather" is not a cisTarget Feather database in Feather v1 or v2 format.
Any more ideas?
Can you redownload the file? The file didn't exist before (different name).
Trying again, but the checksum file doesn't exist. sha256sum.txt
Dear pyscenic development team, I encountered some problems when using “pyscenic ctx”
Describe the bug
Reproduce the behavior
Command run when the error occurred:
Error encountered:
2022-08-29 11:11:22,613 - pyscenic.cli.pyscenic - INFO - Loading expression matrix.
2022-08-29 11:11:22,867 - pyscenic.utils - INFO - Calculating Pearson correlations.
2022-08-29 11:11:22,987 - pyscenic.utils - WARNING - Note on correlation calculation: the default behaviour for calculating the correlations has changed after pySCENIC verion 0.9.16. Previously, the default was to calculate the correlation between a TF and target gene using only cells with non-zero expression values (mask_dropouts=True). The current default is now to use all cells to match the behavior of the R verision of SCENIC. The original settings can be retained by setting 'rho_mask_dropouts=True' in the modules_from_adjacencies function, or '--mask_dropouts' from the CLI. Dropout masking is currently set to [True].
2022-08-29 11:11:25,281 - pyscenic.utils - INFO - Creating modules.
2022-08-29 11:11:50,503 - pyscenic.cli.pyscenic - INFO - Loading databases. Traceback (most recent call last): File "/home/lhz197104/miniconda3/bin/pyscenic", line 8, in
sys.exit(main())
File "/home/lhz197104/miniconda3/lib/python3.8/site-packages/pyscenic/cli/pyscenic.py", line 677, in main
args.func(args)
File "/home/lhz197104/miniconda3/lib/python3.8/site-packages/pyscenic/cli/pyscenic.py", line 215, in prune_targets_command
dbs = _load_dbs(args.database_fname)
File "/home/lhz197104/miniconda3/lib/python3.8/site-packages/pyscenic/cli/pyscenic.py", line 176, in _load_dbs
return [opendb(fname=fname.name, name=get_name(fname.name)) for fname in fnames]
File "/home/lhz197104/miniconda3/lib/python3.8/site-packages/pyscenic/cli/pyscenic.py", line 176, in
return [opendb(fname=fname.name, name=get_name(fname.name)) for fname in fnames]
File "/home/lhz197104/miniconda3/lib/python3.8/site-packages/ctxcore/rnkdb.py", line 180, in opendb
return FeatherRankingDatabase(fname, name=name)
File "/home/lhz197104/miniconda3/lib/python3.8/site-packages/ctxcore/rnkdb.py", line 109, in init
self.ct_db = CisTargetDatabase.init_ct_db(
File "/home/lhz197104/miniconda3/lib/python3.8/site-packages/ctxcore/ctdb.py", line 170, in init_ct_db
raise ValueError(
ValueError: "mm9-500bp-upstream-10species.mc9nr.genes_vs_motifs.rankings.feather" is not a cisTarget Feather database in Feather v1 or v2 format.
aiohttp==3.8.1 aiosignal==1.2.0 arboreto==0.1.6 async-timeout==4.0.2 attrs==22.1.0 bokeh==2.4.3 boltons==21.0.0 Bottleneck @ file:///tmp/build/80754af9/bottleneck_1648028895253/work brotlipy==0.7.0 certifi @ file:///opt/conda/conda-bld/certifi_1655968806487/work/certifi cffi @ file:///opt/conda/conda-bld/cffi_1642701102775/work charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work click @ file:///tmp/build/80754af9/click_1646038465422/work cloudpickle==2.1.0 colorama @ file:///tmp/build/80754af9/colorama_1607707115595/work conda==4.14.0 conda-content-trust @ file:///tmp/build/80754af9/conda-content-trust_1617045594566/work conda-package-handling @ file:///tmp/build/80754af9/conda-package-handling_1649087926789/work cryptography @ file:///tmp/build/80754af9/cryptography_1639400846433/work ctxcore==0.2.0 cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work Cython @ file:///tmp/build/80754af9/cython_1647832478439/work cytoolz==0.11.0 dask==2022.8.1 dill==0.3.5.1 distributed==2022.8.1 fonttools==4.25.0 frozendict==2.3.4 frozenlist==1.3.1 fsspec==2022.7.1 h5py==2.10.0 HeapDict==1.0.1 idna @ file:///tmp/build/80754af9/idna_1637925883363/work interlap==0.2.7 Jinja2==3.1.2 joblib @ file:///tmp/build/80754af9/joblib_1635411271373/work kiwisolver @ file:///opt/conda/conda-bld/kiwisolver_1653292039266/work llvmlite==0.38.0 locket==1.0.0 loompy==3.0.7 MarkupSafe==2.1.1 matplotlib @ file:///tmp/build/80754af9/matplotlib-suite_1647441664166/work mkl-fft==1.3.1 mkl-random @ file:///tmp/build/80754af9/mkl_random_1626186064646/work mkl-service==2.4.0 mock @ file:///tmp/build/80754af9/mock_1607622725907/work msgpack==1.0.4 multidict==6.0.2 multiprocessing-on-dill==3.5.0a4 networkx==2.8.6 numba @ file:///opt/conda/conda-bld/numba_1648040517072/work numexpr @ file:///tmp/build/80754af9/numexpr_1640704208950/work numpy @ file:///opt/conda/conda-bld/numpy_and_numpy_base_1651563629415/work numpy-groupies==0.9.19 packaging @ file:///tmp/build/80754af9/packaging_1637314298585/work pandas==1.4.3 partd==1.3.0 patsy==0.5.2 Pillow==9.0.1 psutil==5.9.1 pyarrow==9.0.0 pycosat==0.6.3 pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work pynndescent==0.5.7 pyOpenSSL @ file:///opt/conda/conda-bld/pyopenssl_1643788558760/work pyparsing @ file:///opt/conda/conda-bld/pyparsing_1661452539315/work pysam==0.19.1 pyscenic==0.12.0 PySocks @ file:///tmp/build/80754af9/pysocks_1605305779399/work python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work pytz==2022.2.1 PyYAML==6.0 requests @ file:///opt/conda/conda-bld/requests_1641824580448/work ruamel-yaml-conda @ file:///tmp/build/80754af9/ruamel_yaml_1616016699510/work scikit-learn @ file:///tmp/build/80754af9/scikit-learn_1642617107864/work scipy @ file:///tmp/build/80754af9/scipy_1641555001653/work seaborn @ file:///tmp/build/80754af9/seaborn_1629307859561/work sip==4.19.13 six @ file:///tmp/build/80754af9/six_1644875935023/work sortedcontainers==2.4.0 statsmodels @ file:///tmp/build/80754af9/statsmodels_1648033297787/work tables==3.6.1 tblib==1.7.0 threadpoolctl @ file:///Users/ktietz/demo/mc3/conda-bld/threadpoolctl_1629802263681/work toolz @ file:///tmp/build/80754af9/toolz_1636545406491/work tornado @ file:///tmp/build/80754af9/tornado_1606942300299/work tqdm @ file:///opt/conda/conda-bld/tqdm_1647339053476/work typing_extensions==4.3.0 umap-learn==0.5.3 urllib3 @ file:///opt/conda/conda-bld/urllib3_1643638302206/work velocyto==0.17.17 yarl==1.8.1 zict==2.2.0