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aertslab / pySCENIC

pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.
http://scenic.aertslab.org
GNU General Public License v3.0
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error when runing adjacencies = grnboost2(ex_matrix, tf_names=tf_names, verbose=True) #58

Closed yuliusema4 closed 5 years ago

yuliusema4 commented 5 years ago

Hi

I successfully run a handful datasets. But got error for the last dataset, here are the message:

adjacencies = grnboost2(ex_matrix, tf_names=tf_names, verbose=True) preparing dask client parsing input /home/yu_liu/.local/lib/python3.6/site-packages/arboreto/algo.py:214: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead. expression_matrix = expression_data.as_matrix() creating dask graph 8 partitions computing dask graph distributed.comm.tcp - WARNING - Closing dangling stream in distributed.comm.tcp - WARNING - Closing dangling stream in distributed.comm.tcp - WARNING - Closing dangling stream in distributed.worker - WARNING - Compute Failed Function: check_meta args: ( TF target importance 587 PDX1 NaN 4.557409 43 IKZF2 NaN 1.383575 541 EMX1 NaN 1.264551 650 ZNF300 NaN 1.249224 1023 STAT5B NaN 1.134848 1019 INSM1 NaN 1.116987 920 ZBTB43 NaN 1.111201 299 HES1 NaN 1.086385 285 ZBTB24 NaN 1.067774 1529 SP9 NaN 1.051621 298 TFDP2 NaN 1.042101 1294 MTF1 NaN 0.993883 853 MYRFL NaN 0.990080 1076 IRX5 NaN 0.980175 1322 ZNF836 NaN 0.951067 327 ETV3 NaN 0.924852 987 NHLH1 NaN 0.874245 1509 ZNF726 NaN 0.873340 170 ADNP NaN 0.849130 1438 ZXDB NaN 0.641062 1251 NKRF NaN 0.138088 59 PRDM1 NaN 0.125185 25 NME2 NaN 0.122399 711 TGIF2LX NaN 0.119234 4 SOX8 NaN 0.117549 551 TBR1 NaN 0.061086 1205 FOXD4L1 NaN 0.059487 616 ZNF787 NaN 0.041974 855 ZMAT1 kwargs: {} Exception: ValueError('Metadata mismatch found in from_delayed.\n\nPartition type: DataFrame\n+--------+---------+----------+\n| Column | Found | Expected |\n+--------+---------+----------+\n| target | float64 | object |\n+--------+---------+----------+',)

distributed.worker - WARNING - Compute Failed Function: check_meta args: ( TF target importance 182 KLF5 NaN 1.796901 1400 ZNF780A NaN 1.255619 113 SRCAP NaN 1.044298 1529 SP9 NaN 1.014616 1551 JRK NaN 0.969606 94 SREBF1 NaN 0.959533 497 ZNF141 NaN 0.903381 722 BATF NaN 0.891596 363 SOHLH2 NaN 0.848746 1432 ZNF813 NaN 0.833871 415 RUNX2 NaN 0.828340 966 HSFX2 NaN 0.792233 960 FOXN2 NaN 0.778318 106 NFKB2 NaN 0.763673 127 REST NaN 0.758959 534 DMTF1 NaN 0.735569 266 PRDM4 NaN 0.719243 170 ADNP NaN 0.696940 327 ETV3 NaN 0.677151 416 GCM2 NaN 0.620082 795 ANKZF1 NaN 0.582343 11 GTF2IRD1 NaN 0.567201 85 MEF2A NaN 0.508835 1071 KCMF1 NaN 0.501975 51 ZNF800 NaN 0.498035 338 PLAGL1 NaN 0.403004 256 FOXN1 NaN 0.276789 405 SNAI1
kwargs: {} Exception: ValueError('Metadata mismatch found in from_delayed.\n\nPartition type: DataFrame\n+--------+---------+----------+\n| Column | Found | Expected |\n+--------+---------+----------+\n| target | float64 | object |\n+--------+---------+----------+',)

distributed.worker - WARNING - Compute Failed Function: check_meta args: ( TF target importance 1315 ZNF250 NaN 1.250947 1228 SP1 NaN 1.181255 299 HES1 NaN 1.159840 1141 ZNF816 NaN 1.137491 1183 ZNF721 NaN 1.126200 694 POU4F1 NaN 1.118718 753 ZBTB8A NaN 1.084263 249 CAMTA2 NaN 1.061497 1122 ZNF664 NaN 1.051428 76 FOXJ2 NaN 0.943101 1495 ONECUT3 NaN 0.903678 316 NFE2L2 NaN 0.773948 455 PIN1 NaN 0.647856 1501 MXD3 NaN 0.642214 502 RHOXF2 NaN 0.621273 357 MXI1 NaN 0.596881 312 ATF2 NaN 0.563965 106 NFKB2 NaN 0.550881 1538 ZNF469 NaN 0.536368 1254 MYT1L NaN 0.535402 957 ATF7 NaN 0.534378 984 BPTF NaN 0.530043 600 IRF8 NaN 0.525196 415 RUNX2 NaN 0.518664 350 ZBTB45 NaN 0.505183 1600 ZNF850 NaN 0.495175 168 NFATC2 NaN 0.455970 1245 ZBTB6 NaN 0.447891 1401 ZNF461 kwargs: {} Exception: ValueError('Metadata mismatch found in from_delayed.\n\nPartition type: DataFrame\n+--------+---------+----------+\n| Column | Found | Expected |\n+--------+---------+----------+\n| target | float64 | object |\n+--------+---------+----------+',)

distributed.worker - WARNING - Compute Failed Function: check_meta args: ( TF target importance 662 HMBOX1 NaN 1.177350 442 IRF3 NaN 1.169355 1589 ZNF878 NaN 1.115477 283 SIM1 NaN 1.113157 407 HIF3A NaN 1.086393 1123 GATA2 NaN 1.055013 1396 ZNF595 NaN 1.046055 363 SOHLH2 NaN 1.022817 1292 SKOR1 NaN 1.020794 776 GMEB1 NaN 1.018376 169 SALL4 NaN 0.963850 837 PKNOX2 NaN 0.943171 1286 HES4 NaN 0.917107 1138 TSHZ1 NaN 0.906140 996 KLF11 NaN 0.887013 1408 ZNF823 NaN 0.870434 410 TRERF1 NaN 0.861682 29 ZBTB32 NaN 0.827065 1221 ZNF74 NaN 0.816359 243 LHX3 NaN 0.771538 1555 ZBED5 NaN 0.731618 90 MNT NaN 0.722224 1477 DPRX NaN 0.719250 287 TFEB NaN 0.624701 942 FOS NaN 0.588143 1576 HOXA10 NaN 0.326919 275 VDR NaN 0.320166 227 HOXA13 NaN 0.311231 1049 ISX kwargs: {} Exception: ValueError('Metadata mismatch found in from_delayed.\n\nPartition type: DataFrame\n+--------+---------+----------+\n| Column | Found | Expected |\n+--------+---------+----------+\n| target | float64 | object |\n+--------+---------+----------+',)

distributed.worker - WARNING - Compute Failed Function: check_meta args: ( TF target importance 559 XPA NaN 1.430642 1335 ZNF124 NaN 0.973843 1047 ZNF519 NaN 0.922602 721 ZKSCAN2 NaN 0.779462 652 RBAK NaN 0.715117 1290 HMX3 NaN 0.712181 591 ESR2 NaN 0.653312 1512 MEF2B NaN 0.626067 242 CREB3 NaN 0.625131 473 SOX15 NaN 0.613608 288 TBP NaN 0.605124 1469 PBX2 NaN 0.596615 297 BCL6 NaN 0.559075 778 MYSM1 NaN 0.550524 135 ZNF343 NaN 0.546342 1360 ZNF705A NaN 0.528863 61 MXD1 NaN 0.491887 221 TFEC NaN 0.474558 137 KDM2B NaN 0.465349 1285 ZNF383 NaN 0.463501 1491 DPF3 NaN 0.450312 127 REST NaN 0.448734 1559 ZNF709 NaN 0.442962 763 ZBTB7B NaN 0.438281 363 SOHLH2 NaN 0.434157 348 NR4A3 NaN 0.433643 1246 ZNF749 NaN 0.431052 613 PRDM15 NaN 0.424422 1045 DDIT3 kwargs: {} Exception: ValueError('Metadata mismatch found in from_delayed.\n\nPartition type: DataFrame\n+--------+---------+----------+\n| Column | Found | Expected |\n+--------+---------+----------+\n| target | float64 | object |\n+--------+---------+----------+',)

distributed.worker - WARNING - Compute Failed Function: check_meta args: ( TF target importance 785 ATF3 NaN 4.439906e+00 41 TBPL1 NaN 2.159733e+00 589 MBNL2 NaN 9.670047e-01 764 ZNF394 NaN 9.517704e-01 145 ZFHX4 NaN 8.575002e-01 1607 ZBTB8B NaN 8.268765e-01 1435 ZNF69 NaN 8.237028e-01 209 KLF1 NaN 8.105438e-01 237 GLI3 NaN 7.734310e-01 984 BPTF NaN 7.378804e-01 963 ZNF415 NaN 7.123150e-01 895 BATF2 NaN 5.479090e-01 1305 ZNF33A NaN 5.248561e-01 686 THYN1 NaN 5.120804e-01 1132 TIGD5 NaN 5.018900e-01 801 KLF15 NaN 4.725726e-01 293 NR3C1 NaN 4.360651e-01 1048 PROP1 NaN 4.342129e-01 812 FAM170A NaN 4.338765e-01 646 CSRNP1 NaN 4.311894e-01 538 TFCP2 NaN 4.105137e-01 1379 ZNF165 NaN 3.725640e-01 967 ZNF672 NaN 3.703189e-01 82 ZFAT NaN 3.629495e-01 1486 ZNF783 NaN 3.089804e-01 251 HNF1B NaN 2.606121e-01 305 CENPA NaN 2.33 kwargs: {} Exception: ValueError('Metadata mismatch found in from_delayed.\n\nPartition type: DataFrame\n+--------+---------+----------+\n| Column | Found | Expected |\n+--------+---------+----------+\n| target | float64 | object |\n+--------+---------+----------+',)

distributed.worker - WARNING - Compute Failed Function: check_meta args: ( TF target importance 662 HMBOX1 NaN 1.177350 442 IRF3 NaN 1.169355 1589 ZNF878 NaN 1.115477 283 SIM1 NaN 1.113157 407 HIF3A NaN 1.086393 1123 GATA2 NaN 1.055013 1396 ZNF595 NaN 1.046055 363 SOHLH2 NaN 1.022817 1292 SKOR1 NaN 1.020794 776 GMEB1 NaN 1.018376 169 SALL4 NaN 0.963850 837 PKNOX2 NaN 0.943171 1286 HES4 NaN 0.917107 1138 TSHZ1 NaN 0.906140 996 KLF11 NaN 0.887013 1408 ZNF823 NaN 0.870434 410 TRERF1 NaN 0.861682 29 ZBTB32 NaN 0.827065 1221 ZNF74 NaN 0.816359 243 LHX3 NaN 0.771538 1555 ZBED5 NaN 0.731618 90 MNT NaN 0.722224 1477 DPRX NaN 0.719250 287 TFEB NaN 0.624701 942 FOS NaN 0.588143 1576 HOXA10 NaN 0.326919 275 VDR NaN 0.320166 227 HOXA13 NaN 0.311231 1049 ISX kwargs: {} Exception: ValueError('Metadata mismatch found in from_delayed.\n\nPartition type: DataFrame\n+--------+---------+----------+\n| Column | Found | Expected |\n+--------+---------+----------+\n| target | float64 | object |\n+--------+---------+----------+',)

shutting down client and local cluster /home/yu_liu/miniconda3/envs/pyscenic2/lib/python3.6/site-packages/sklearn/ensemble/gradient_boosting.py:1636: RuntimeWarning: invalid value encountered in true_divide importances /= importances.sum() /home/yu_liu/miniconda3/envs/pyscenic2/lib/python3.6/site-packages/sklearn/ensemble/gradient_boosting.py:1636: RuntimeWarning: invalid value encountered in true_divide importances /= importances.sum() /home/yu_liu/miniconda3/envs/pyscenic2/lib/python3.6/site-packages/sklearn/ensemble/gradient_boosting.py:1636: RuntimeWarning: invalid value encountered in true_divide importances /= importances.sum() /home/yu_liu/miniconda3/envs/pyscenic2/lib/python3.6/site-packages/sklearn/ensemble/gradient_boosting.py:1636: RuntimeWarning: invalid value encountered in true_divide importances /= importances.sum() finished Traceback (most recent call last): File "", line 1, in File "/home/yu_liu/.local/lib/python3.6/site-packages/arboreto/algo.py", line 41, in grnboost2 early_stop_window_length=early_stop_window_length, limit=limit, seed=seed, verbose=verbose) File "/home/yu_liu/.local/lib/python3.6/site-packages/arboreto/algo.py", line 135, in diy .compute(graph, sync=True) \ File "/home/yu_liu/.local/lib/python3.6/site-packages/distributed/client.py", line 2504, in compute result = self.gather(futures) File "/home/yu_liu/.local/lib/python3.6/site-packages/distributed/client.py", line 1655, in gather asynchronous=asynchronous) File "/home/yu_liu/.local/lib/python3.6/site-packages/distributed/client.py", line 675, in sync return sync(self.loop, func, *args, *kwargs) File "/home/yu_liu/.local/lib/python3.6/site-packages/distributed/utils.py", line 277, in sync six.reraise(error[0]) File "/home/yu_liu/miniconda3/envs/pyscenic2/lib/python3.6/site-packages/six.py", line 693, in reraise raise value File "/home/yu_liu/.local/lib/python3.6/site-packages/distributed/utils.py", line 262, in f result[0] = yield future File "/home/yu_liu/miniconda3/envs/pyscenic2/lib/python3.6/site-packages/tornado/gen.py", line 1133, in run value = future.result() File "/home/yu_liu/miniconda3/envs/pyscenic2/lib/python3.6/site-packages/tornado/gen.py", line 1141, in run yielded = self.gen.throw(*exc_info) File "/home/yu_liu/.local/lib/python3.6/site-packages/distributed/client.py", line 1496, in _gather traceback) File "/home/yu_liu/miniconda3/envs/pyscenic2/lib/python3.6/site-packages/six.py", line 692, in reraise raise value.with_traceback(tb) File "/home/yu_liu/miniconda3/envs/pyscenic2/lib/python3.6/site-packages/dask/dataframe/utils.py", line 498, in check_meta errmsg)) ValueError: Metadata mismatch found in from_delayed.

Partition type: DataFrame +--------+---------+----------+ | Column | Found | Expected | +--------+---------+----------+ | target | float64 | object | +--------+---------+----------+

yuliusema4 commented 5 years ago

file format problem