diffxpy [Multiple tests for each gene]--de.test.versus_rest: ValueError: setting an array element with a sequence. #226
Description
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
_libgcc_mutex 0.1 conda_forge conda-forge
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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