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theislab / diffxpy

Differential expression analysis for single-cell RNA-seq data.
https://diffxpy.rtfd.io
BSD 3-Clause "New" or "Revised" License
179 stars 23 forks source link

diffxpy [Multiple tests for each gene]--de.test.versus_rest: ValueError: setting an array element with a sequence. #226

Open winglet0996 opened 10 months ago

winglet0996 commented 10 months ago

When running:

test_fac = de.test.versus_rest(
    data=adata,
    grouping="peri_state",
    test="wald",
    noise_model="nb"
)

output:

/home/winglet/mambaforge/envs/diffxpy/lib/python3.9/site-packages/numba/core/dispatcher.py:289: UserWarning: Numba extension module 'sparse._numba_extension' failed to load due to 'ModuleNotFoundError(No module named 'numba.targets')'.
  entrypoints.init_all()
training location model: False
training scale model: True
iter   0: ll=1227857339.940779
iter   1: ll=1227857339.940779, converged: 0.00% (loc: 100.00%, scale update: False), in 0.00sec
iter   2: ll=9055764.057215, converged: 0.03% (loc: 0.03%, scale update: True), in 112.15sec
iter   3: ll=9055764.057215, converged: 0.03% (loc: 100.00%, scale update: False), in 0.00sec
iter   4: ll=7000994.629178, converged: 54.09% (loc: 54.09%, scale update: True), in 101.73sec
iter   5: ll=7000994.629178, converged: 54.09% (loc: 100.00%, scale update: False), in 0.00sec
iter   6: ll=6557810.944337, converged: 86.71% (loc: 86.71%, scale update: True), in 52.19sec
iter   7: ll=6557810.944337, converged: 86.71% (loc: 100.00%, scale update: False), in 0.00sec
iter   8: ll=6446595.094442, converged: 97.53% (loc: 97.53%, scale update: True), in 21.61sec
iter   9: ll=6446595.094442, converged: 97.53% (loc: 100.00%, scale update: False), in 0.00sec
iter  10: ll=6432146.995729, converged: 99.63% (loc: 99.63%, scale update: True), in 12.08sec
iter  11: ll=6432146.995729, converged: 99.63% (loc: 100.00%, scale update: False), in 0.00sec
iter  12: ll=6432109.198854, converged: 99.98% (loc: 99.98%, scale update: True), in 11.10sec
iter  13: ll=6432109.198854, converged: 99.98% (loc: 100.00%, scale update: False), in 0.00sec
iter  14: ll=6429280.381111, converged: 99.98% (loc: 99.98%, scale update: True), in 0.97sec
iter  15: ll=6429280.381111, converged: 99.98% (loc: 100.00%, scale update: False), in 0.00sec
iter  16: ll=6429280.381111, converged: 100.00% (loc: 100.00%, scale update: True), in 0.89sec
/home/winglet/mambaforge/envs/diffxpy/lib/python3.9/site-packages/dask/array/core.py:2912: RuntimeWarning: divide by zero encountered in divide
  size = (limit / dtype.itemsize / largest_block) ** (1 / len(autos))
training location model: False
training scale model: True
iter   0: ll=1227860339.808143
iter   1: ll=1227860339.808143, converged: 0.00% (loc: 100.00%, scale update: False), in 0.00sec
iter   2: ll=9000583.288855, converged: 0.05% (loc: 0.05%, scale update: True), in 109.65sec
iter   3: ll=9000583.288855, converged: 0.05% (loc: 100.00%, scale update: False), in 0.00sec
iter   4: ll=7016689.032998, converged: 53.33% (loc: 53.33%, scale update: True), in 100.49sec
iter   5: ll=7016689.032998, converged: 53.33% (loc: 100.00%, scale update: False), in 0.00sec
iter   6: ll=6544436.664088, converged: 87.09% (loc: 87.09%, scale update: True), in 53.29sec
iter   7: ll=6544436.664088, converged: 87.09% (loc: 100.00%, scale update: False), in 0.00sec
iter   8: ll=6420645.794305, converged: 97.37% (loc: 97.37%, scale update: True), in 20.90sec
iter   9: ll=6420645.794305, converged: 97.37% (loc: 100.00%, scale update: False), in 0.00sec
iter  10: ll=6391502.238352, converged: 99.47% (loc: 99.47%, scale update: True), in 11.37sec
iter  11: ll=6391502.238352, converged: 99.47% (loc: 100.00%, scale update: False), in 0.00sec
iter  12: ll=6389031.433432, converged: 99.93% (loc: 99.93%, scale update: True), in 9.90sec
iter  13: ll=6389031.433432, converged: 99.93% (loc: 100.00%, scale update: False), in 0.00sec
iter  14: ll=6389009.427554, converged: 99.99% (loc: 99.99%, scale update: True), in 8.26sec
iter  15: ll=6389009.427554, converged: 99.99% (loc: 100.00%, scale update: False), in 0.00sec
iter  16: ll=6389009.427554, converged: 100.00% (loc: 100.00%, scale update: True), in 0.62sec
/home/winglet/mambaforge/envs/diffxpy/lib/python3.9/site-packages/dask/array/core.py:2912: RuntimeWarning: divide by zero encountered in divide
  size = (limit / dtype.itemsize / largest_block) ** (1 / len(autos))
training location model: False
training scale model: True
iter   0: ll=1227909630.300988
iter   1: ll=1227909630.300988, converged: 0.00% (loc: 100.00%, scale update: False), in 0.00sec
iter   2: ll=9166533.750562, converged: 0.08% (loc: 0.08%, scale update: True), in 110.33sec
iter   3: ll=9166533.750562, converged: 0.08% (loc: 100.00%, scale update: False), in 0.00sec
iter   4: ll=7107714.147657, converged: 54.23% (loc: 54.23%, scale update: True), in 104.80sec
iter   5: ll=7107714.147657, converged: 54.23% (loc: 100.00%, scale update: False), in 0.00sec
iter   6: ll=6651136.907688, converged: 87.15% (loc: 87.15%, scale update: True), in 52.64sec
iter   7: ll=6651136.907688, converged: 87.15% (loc: 100.00%, scale update: False), in 0.00sec
iter   8: ll=6559446.893402, converged: 97.39% (loc: 97.39%, scale update: True), in 20.32sec
iter   9: ll=6559446.893402, converged: 97.39% (loc: 100.00%, scale update: False), in 0.00sec
iter  10: ll=6544054.628406, converged: 99.59% (loc: 99.59%, scale update: True), in 11.39sec
iter  11: ll=6544054.628406, converged: 99.59% (loc: 100.00%, scale update: False), in 0.00sec
iter  12: ll=6537830.581586, converged: 99.91% (loc: 99.91%, scale update: True), in 9.80sec
iter  13: ll=6537830.581586, converged: 99.91% (loc: 100.00%, scale update: False), in 0.00sec
iter  14: ll=6537830.581586, converged: 100.00% (loc: 100.00%, scale update: True), in 9.25sec
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[7], line 1
----> 1 test_fac = de.test.versus_rest(
      2     data=adata,
      3     grouping="peri_state",
      4     test="wald",
      5     noise_model="nb"
      6 )

File ~/mambaforge/envs/diffxpy/lib/python3.9/site-packages/diffxpy/diffxpy/testing/tests.py:1500, in versus_rest(data, grouping, as_numeric, test, gene_names, sample_description, noise_model, size_factors, batch_size, backend, train_args, training_strategy, is_sig_zerovar, quick_scale, dtype, pval_correction, keep_full_test_objs, **kwargs)
   1493     if keep_full_test_objs:
   1494         tests[0, i] = de_test_temp
   1496 de_test = DifferentialExpressionTestVsRest(
   1497     gene_ids=gene_names,
   1498     pval=pvals,
   1499     logfc=logfc,
-> 1500     ave=np.mean(data, axis=0),
   1501     groups=groups,
   1502     tests=tests,
   1503     correction_type=pval_correction
   1504 )
   1506 return de_test

File <__array_function__ internals>:200, in mean(*args, **kwargs)

File ~/mambaforge/envs/diffxpy/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3464, in mean(a, axis, dtype, out, keepdims, where)
   3461     else:
   3462         return mean(axis=axis, dtype=dtype, out=out, **kwargs)
-> 3464 return _methods._mean(a, axis=axis, dtype=dtype,
   3465                       out=out, **kwargs)

File ~/mambaforge/envs/diffxpy/lib/python3.9/site-packages/numpy/core/_methods.py:165, in _mean(a, axis, dtype, out, keepdims, where)
    164 def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
--> 165     arr = asanyarray(a)
    167     is_float16_result = False
    169     rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)

ValueError: setting an array element with a sequence. The requested array would exceed the maximum number of dimension of 32.
packages in environment at /home/winglet/mambaforge/envs/diffxpy:

 Name                    Version                   Build  Channel
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_openmp_mutex             4.5                       2_gnu    conda-forge
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alabaster                 0.7.13                   pypi_0    pypi
anndata                   0.7.8                    pypi_0    pypi
anyio                     3.7.1              pyhd8ed1ab_0    conda-forge
argon2-cffi               23.1.0             pyhd8ed1ab_0    conda-forge
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arrow                     1.2.3              pyhd8ed1ab_0    conda-forge
asttokens                 2.2.1              pyhd8ed1ab_0    conda-forge
astunparse                1.6.3                    pypi_0    pypi
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attrs                     23.1.0             pyh71513ae_1    conda-forge
babel                     2.12.1             pyhd8ed1ab_1    conda-forge
backcall                  0.2.0              pyh9f0ad1d_0    conda-forge
backports                 1.0                pyhd8ed1ab_3    conda-forge
backports.functools_lru_cache 1.6.5              pyhd8ed1ab_0    conda-forge
bandit                    1.7.2                    pypi_0    pypi
batchglm                  0.7.4                    pypi_0    pypi
beautifulsoup4            4.12.2             pyha770c72_0    conda-forge
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brotli                    1.0.9                h166bdaf_9    conda-forge
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ca-certificates           2023.7.22            hbcca054_0    conda-forge
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cachetools                5.3.1                    pypi_0    pypi
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colorama                  0.4.6              pyhd8ed1ab_0    conda-forge
comm                      0.1.4              pyhd8ed1ab_0    conda-forge
commonmark                0.9.1                    pypi_0    pypi
contourpy                 1.1.0            py39h7633fee_0    conda-forge
cycler                    0.11.0             pyhd8ed1ab_0    conda-forge
dask                      2021.4.1                 pypi_0    pypi
debugpy                   1.6.8            py39h3d6467e_0    conda-forge
decorator                 5.1.1              pyhd8ed1ab_0    conda-forge
defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge
diffxpy                   0+unknown                 dev_0    <develop>
dm-tree                   0.1.8                    pypi_0    pypi
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entrypoints               0.4                pyhd8ed1ab_0    conda-forge
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fonttools                 4.42.1           py39hd1e30aa_0    conda-forge
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h5py                      3.9.0           nompi_py39h87cadad_102    conda-forge
hdf5                      1.14.2          nompi_h4f84152_100    conda-forge
icu                       72.1                 hcb278e6_0    conda-forge
idna                      3.4                pyhd8ed1ab_0    conda-forge
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six                       1.16.0             pyh6c4a22f_0    conda-forge
smmap                     5.0.0                    pypi_0    pypi
sniffio                   1.3.0              pyhd8ed1ab_0    conda-forge
snowballstemmer           2.2.0                    pypi_0    pypi
soupsieve                 2.3.2.post1        pyhd8ed1ab_0    conda-forge
sparse                    0.9.1                    pypi_0    pypi
sphinx                    7.2.3                    pypi_0    pypi
sphinx-autodoc-typehints  1.24.0                   pypi_0    pypi
sphinxcontrib-applehelp   1.0.7                    pypi_0    pypi
sphinxcontrib-devhelp     1.0.5                    pypi_0    pypi
sphinxcontrib-htmlhelp    2.0.4                    pypi_0    pypi
sphinxcontrib-jsmath      1.0.1                    pypi_0    pypi
sphinxcontrib-qthelp      1.0.6                    pypi_0    pypi
sphinxcontrib-serializinghtml 1.1.9                    pypi_0    pypi
stack_data                0.6.2              pyhd8ed1ab_0    conda-forge
statsmodels               0.14.0           py39h0f8d45d_1    conda-forge
stdlib-list               0.9.0                    pypi_0    pypi
stevedore                 5.1.0                    pypi_0    pypi
tbb                       2021.10.0            h00ab1b0_0    conda-forge
tensorboard               2.13.0                   pypi_0    pypi
tensorboard-data-server   0.7.1                    pypi_0    pypi
tensorflow                2.13.0                   pypi_0    pypi
tensorflow-estimator      2.13.0                   pypi_0    pypi
tensorflow-io-gcs-filesystem 0.33.0                   pypi_0    pypi
tensorflow-probability    0.21.0                   pypi_0    pypi
termcolor                 2.3.0                    pypi_0    pypi
terminado                 0.17.1             pyh41d4057_0    conda-forge
threadpoolctl             3.2.0              pyha21a80b_0    conda-forge
tinycss2                  1.2.1              pyhd8ed1ab_0    conda-forge
tk                        8.6.12               h27826a3_0    conda-forge
toml                      0.10.2                   pypi_0    pypi
tomli                     2.0.1              pyhd8ed1ab_0    conda-forge
toolz                     0.11.2                   pypi_0    pypi
tornado                   6.3.3            py39hd1e30aa_0    conda-forge
tqdm                      4.66.1             pyhd8ed1ab_0    conda-forge
traitlets                 5.9.0              pyhd8ed1ab_0    conda-forge
typing-extensions         4.5.0                    pypi_0    pypi
typing_extensions         4.7.1              pyha770c72_0    conda-forge
typing_utils              0.1.0              pyhd8ed1ab_0    conda-forge
tzdata                    2023c                h71feb2d_0    conda-forge
umap-learn                0.5.3            py39hf3d152e_1    conda-forge
unicodedata2              15.0.0           py39hb9d737c_0    conda-forge
uri-template              1.3.0              pyhd8ed1ab_0    conda-forge
urllib3                   1.26.16                  pypi_0    pypi
wcwidth                   0.2.6              pyhd8ed1ab_0    conda-forge
webcolors                 1.13               pyhd8ed1ab_0    conda-forge
webencodings              0.5.1                      py_1    conda-forge
websocket-client          1.6.2              pyhd8ed1ab_0    conda-forge
werkzeug                  2.3.7                    pypi_0    pypi
wheel                     0.41.2             pyhd8ed1ab_0    conda-forge
wrapt                     1.15.0                   pypi_0    pypi
xlrd                      1.2.0                    pypi_0    pypi
xorg-libxau               1.0.11               hd590300_0    conda-forge
xorg-libxdmcp             1.1.3                h7f98852_0    conda-forge
xz                        5.2.6                h166bdaf_0    conda-forge
yaml                      0.2.5                h7f98852_2    conda-forge
zeromq                    4.3.4                h9c3ff4c_1    conda-forge
zipp                      3.16.2             pyhd8ed1ab_0    conda-forge
zstd                      1.5.2                hfc55251_7    conda-forge