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pVACseq
terminated with an error exit status (1)
#66
Comments
(singularity) ubuntu@ip-172-31-6-189:~/epitope/work_normal/64/dfceaa8042580a81220fa7d392d531$ bash .command.run INFO: Converting SIF file to temporary sandbox... Done: Pipeline finished successfully. File /home/ubuntu/epitope/work_normal/64/dfceaa8042580a81220fa7d392d531/MHC_Class_I/10-TM-FreshFrozen_tumor.filtered.tsv contains list of filtered putative neoantigens. No MHC class II alleles chosen. Skipping MHC class II predictions. |
- GATK 4.5.0.0 - CNVkit 0.9.11 - BLAST 2.15 - multiqc 1.21 - pVACtools 4.1.1(fixes #43, #60, #66) - VEP 111.0 - added DeepImmuno to pVACseq predictors - remove sequenza from nextNEOpiENV and use it in separate ENVS - switch from SGE to slurm in cluster profile example - set process memory defaults for resource reservation to avoid OOM kills - set default process time directive for max process execution time - refine process CPU directive settings for - fix custom HLA file handling (fixes and closes #70, thanks to @mantczakaus) - added custom HLA file example (closes #72) - changed the way how pvacseq results are concatenated. Now a python script using pandas is doing the job.
Should be fixed now in v1.4.1, please reopen if not. |
Dear author,
Thank you for the nice pipeline. I recently ran nextNEOpi pipeline on one sample( DNA tumor, DNA normal and RNA tumor).
The pipeline produced all expected five folders. However, in analysis folder 4 subfolders (pVACseq,mixMHC2pred,CSIN,IGS,BCR_TCR)) were missing.
A)I have attached the screen shot of overall run
![Screenshot 2023-11-27 at 7 31 56 PM](https://private-user-images.githubusercontent.com/33466870/286675369-256de3bf-b4ec-4e22-b2d5-e909dd67ea62.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.MoujxdiqdZCZ9hDvTjyIaJ8Xemq3pKeHU_w0ubhxRd8)
B) I believe the error is ValueError: Input X contains NaN.
Below is the copy paste of actual error
Error executing process > 'pVACseq (10-TM-FreshFrozen)'
Caused by:
Process
pVACseq (10-TM-FreshFrozen)
terminated with an error exit status (1)Command executed:
pvacseq run
--iedb-install-directory /opt/iedb
-t 10
-p 10-TM-FreshFrozen_vep_phased.vcf.gz
-e1 8,9,10,11
-e2 15,16,17,18,19,20,21,22,23,24,25
--normal-sample-name 10-TM-FreshFrozen_normal
--tumor-purity 1
--netmhc-stab
--binding-threshold 500 --top-score-metric median --minimum-fold-change 0.0 --normal-cov 5 --tdna-cov 10 --trna-cov 10 --normal-vaf 0.02 --tdna-vaf 0.25 --trna-vaf 0.25 --expn-val 1 --maximum-transcript-support-level 1
10-TM-FreshFrozen_vep_somatic_gx.vcf.gz 10-TM-FreshFrozen_tumor HLA-B*18:01 NetMHCpan NetMHCpanEL MHCflurry MHCflurryEL NetMHCIIpan NetMHCIIpanEL ./
if [ -e ./MHC_Class_I/10-TM-FreshFrozen_tumor.filtered.tsv ]; then
mv ./MHC_Class_I/10-TM-FreshFrozen_tumor.filtered.tsv ./MHC_Class_I/10-TM-FreshFrozen_tumor_HLA-B18:01.filtered.tsv
fi
if [ -e ./MHC_Class_I/10-TM-FreshFrozen_tumor.all_epitopes.tsv ]; then
mv ./MHC_Class_I/10-TM-FreshFrozen_tumor.all_epitopes.tsv ./MHC_Class_I/10-TM-FreshFrozen_tumor_HLA-B18:01.all_epitopes.tsv
fi
if [ -e ./MHC_Class_II/10-TM-FreshFrozen_tumor.filtered.tsv ]; then
mv ./MHC_Class_II/10-TM-FreshFrozen_tumor.filtered.tsv ./MHC_Class_II/10-TM-FreshFrozen_tumor_HLA-B18:01.filtered.tsv
fi
if [ -e ./MHC_Class_II/10-TM-FreshFrozen_tumor.all_epitopes.tsv ]; then
mv ./MHC_Class_II/10-TM-FreshFrozen_tumor.all_epitopes.tsv ./MHC_Class_II/10-TM-FreshFrozen_tumor_HLA-B18:01.all_epitopes.tsv
fi
Command exit status:
1
Command output:
Making binding predictions on Allele HLA-B18:01 and Epitope Length 8 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.8.tsv_201-242 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 9 with Method MHCflurry - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.MHCflurry.HLA-B18:01.9.tsv_201-242
Making binding predic10.tsv_201-242
Making binding predictions on Allele HLA-B18:01 and Epitope Length 10 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.10.tsv_201-242
Making binding predictions on Allele HLA-B18:01 and Epitope Length 8 with Method MHCflurry - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.MHCflurry.HLA-B18:01.8.tsv_1-200
Making binding predictions on Allele HLA-B18:01 and Epitope Length 11 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.11.tsv_201-242
Making binding predictions on Allele HLA-B18:01 and Epitope Length 9 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.9.tsv_201-242 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 9 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.9.tsv_201-242
Making binding predictions on Allele HLA-B18:01 and Epitope Length 8 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.8.tsv_201-242 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 9 with Method MHCflurry - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.MHCflurry.HLA-B18:01.9.tsv_201-242
Making binding predictions on Allele HLA-B18:01 and Epitope Length 10 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.10.tsv_201-242 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 11 with Method MHCflurry - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.MHCflurry.HLA-B18:01.11.tsv_201-242
Making binding predictions on Allele HLA-B18:01 and Epitope Length 9 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.9.tsv_201-242 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 11 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.11.tsv_201-242 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 11 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.11.tsv_201-242
Forcing tensorflow backend.
Making binding predictions on Allele HLA-B18:01 and Epitope Length 8 with Method MHCflurry - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.MHCflurry.HLA-B18:01.8.tsv_201-242 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 8 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.8.tsv_201-242
Forcing tensorflow backend.
Making binding predictions on Allele HLA-B18:01 and Epitope Length 10 with Method MHCflurry - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.MHCflurry.HLA-B18:01.10.tsv_201-242 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 10 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.10.tsv_201-242
Forcing tensorflow backend.
Making binding predictions on Allele HLA-B18:01 and Epitope Length 8 with Method MHCflurry - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.MHCflurry.HLA-B18:01.8.tsv_1-200 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 8 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.8.tsv_1-200
Forcing tensorflow backend.
Making binding predictions on Allele HLA-B18:01 and Epitope Length 11 with Method MHCflurry - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.MHCflurry.HLA-B18:01.11.tsv_1-200 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 11 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.11.tsv_1-200
Forcing tensorflow backend.
Making binding predictions on Allele HLA-B18:01 and Epitope Length 10 with Method MHCflurry - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.MHCflurry.HLA-B18:01.10.tsv_1-200 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 10 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.10.tsv_1-200
Forcing tensorflow backend.
Making binding predictions on Allele HLA-B18:01 and Epitope Length 9 with Method MHCflurry - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.MHCflurry.HLA-B18:01.9.tsv_1-200 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 9 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.9.tsv_1-200
Making binding predictions on Allele HLA-B18:01 and Epitope Length 11 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.11.tsv_201-242 - Completed
Forcing tensorflow backend.
Making binding predictions on Allele HLA-B18:01 and Epitope Length 9 with Method MHCflurry - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.MHCflurry.HLA-B18:01.9.tsv_201-242 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 8 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.8.tsv_201-242 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 10 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.10.tsv_201-242 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 9 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.9.tsv_1-200 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 9 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.9.tsv_1-200
Making binding predictions on Allele HLA-B18:01 and Epitope Length 8 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.8.tsv_1-200 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 8 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.8.tsv_1-200
Making binding predictions on Allele HLA-B18:01 and Epitope Length 10 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.10.tsv_1-200 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 10 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.10.tsv_1-200
Making binding predictions on Allele HLA-B18:01 and Epitope Length 9 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.9.tsv_1-200 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 11 with Method NetMHCpan - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan.HLA-B18:01.11.tsv_1-200 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 11 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.11.tsv_1-200
Making binding predictions on Allele HLA-B18:01 and Epitope Length 8 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.8.tsv_1-200 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 10 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.10.tsv_1-200 - Completed
Making binding predictions on Allele HLA-B18:01 and Epitope Length 11 with Method NetMHCpanEL - File MHC_Class_I/tmp/10-TM-FreshFrozen_tumor.netmhcpan_el.HLA-B18:01.11.tsv_1-200 - Completed
Command error:
/opt/conda/lib/python3.8/site-packages/keras/src/engine/training_v1.py:2359: UserWarning:
Model.state_updates
will be removed in a future version. This property should not be used in TensorFlow 2.0, asupdates
are applied automatically.updates=self.state_updates,
WARNING:tensorflow:From /opt/conda/lib/python3.8/site-packages/tensorflow/python/ops/init_ops.py:94: calling VarianceScaling.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /opt/conda/lib/python3.8/site-packages/tensorflow/python/ops/init_ops.py:94: calling Zeros.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
/opt/conda/lib/python3.8/site-packages/keras/src/engine/training_v1.py:2359: UserWarning:
Model.state_updates
will be removed in a future version. This property should not be used in TensorFlow 2.0, asupdates
are applied automatically.updates=self.state_updates,
/opt/conda/lib/python3.8/site-packages/keras/src/engine/training_v1.py:2359: UserWarning:
Model.state_updates
will be removed in a future version. This property should not be used in TensorFlow 2.0, asupdates
are applied automatically.updates=self.state_updates,
/opt/conda/lib/python3.8/site-packages/keras/src/engine/training_v1.py:2359: UserWarning:
Model.state_updates
will be removed in a future version. This property should not be used in TensorFlow 2.0, asupdates
are applied automatically.updates=self.state_updates,
/opt/conda/lib/python3.8/site-packages/keras/src/engine/training_v1.py:2359: UserWarning:
Model.state_updates
will be removed in a future version. This property should not be used in TensorFlow 2.0, asupdates
are applied automatically.updates=self.state_updates,
/opt/conda/lib/python3.8/site-packages/keras/src/engine/training_v1.py:2359: UserWarning:
Model.state_updates
will be removed in a future version. This property should not be used in TensorFlow 2.0, asupdates
are applied automatically.updates=self.state_updates,
/opt/conda/lib/python3.8/site-packages/keras/src/engine/training_v1.py:2359: UserWarning:
Model.state_updates
will be removed in a future version. This property should not be used in TensorFlow 2.0, asupdates
are applied automatically.updates=self.state_updates,
/opt/conda/lib/python3.8/site-packages/keras/src/engine/training_v1.py:2359: UserWarning:
Model.state_updates
will be removed in a future version. This property should not be used in TensorFlow 2.0, asupdates
are applied automatically.updates=self.state_updates,
/opt/conda/lib/python3.8/site-packages/keras/src/engine/training_v1.py:2359: UserWarning:
Model.state_updates
will be removed in a future version. This property should not be used in TensorFlow 2.0, asupdates
are applied automatically.updates=self.state_updates,
WARNING:tensorflow:From /opt/conda/lib/python3.8/site-packages/tensorflow/python/ops/init_ops.py:94: calling VarianceScaling.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /opt/conda/lib/python3.8/site-packages/tensorflow/python/ops/init_ops.py:94: calling Zeros.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
/opt/conda/lib/python3.8/site-packages/keras/src/engine/training_v1.py:2359: UserWarning:
Model.state_updates
will be removed in a future version. This property should not be used in TensorFlow 2.0, asupdates
are applied automatically.updates=self.state_updates,
CRITICAL:pymp:An exception occured in thread 8: (<class 'ValueError'>, Input X contains NaN.
LogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values).
Traceback (most recent call last):
File "/opt/conda/bin/pvacseq", line 8, in
sys.exit(main())
File "/opt/conda/lib/python3.8/site-packages/pvactools/tools/pvacseq/main.py", line 123, in main
args[0].func.main(args[1])
File "/opt/conda/lib/python3.8/site-packages/pvactools/tools/pvacseq/run.py", line 138, in main
pipeline.execute()
File "/opt/conda/lib/python3.8/site-packages/pvactools/lib/pipeline.py", line 451, in execute
self.call_iedb(chunks)
File "/opt/conda/lib/python3.8/site-packages/pvactools/lib/pipeline.py", line 357, in call_iedb
p.print("Making binding predictions on Allele %s and Epitope Length %s with Method %s - File %s - Completed" % (a, epl, method, filename))
File "/opt/conda/lib/python3.8/site-packages/pymp/init.py", line 148, in exit
raise exc_t(exc_val)
ValueError: Input X contains NaN.
LogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
INFO: Cleaning up image...
Work dir:
/home/ubuntu/epitope/work_normal/64/dfceaa8042580a81220fa7d392d531
Tip: you can replicate the issue by changing to the process work dir and entering the command
bash .command.run
The text was updated successfully, but these errors were encountered: