Hi,
I have previously run CNVkit on default bin sizes, but now I'd like to try how using autobin affects the results. I have paired tumor-normal samples sequenced with hybrid capture method, am I supposed to use all of them as input for autobin, or only either tumor or normal samples? I understand that only the file with median file size is used for optimal bin size estimation, but obviously the set of files given as input affects the estimation. Another problem I have is that when I tested the command with either tumor or normal samples, it worked for tumor samples but for normal samples it gave an error:
Detected file format: bed
Detected file format: bed
Estimated read length 130.0
Wrote /tmp/tmpvncrgu42.bed with 100 regions
Limiting est. bin size 3250849 to given max. 500000
Splitting large targets
Traceback (most recent call last):
File "/miniconda3/bin/cnvkit.py", line 10, in
sys.exit(main())
File "/miniconda3/lib/python3.9/site-packages/cnvlib/cnvkit.py", line 10, in main
args.func(args)
File "/miniconda3/lib/python3.9/site-packages/cnvlib/commands.py", line 568, in _cmd_autobin
target_out_arr = target.do_target(
File "/miniconda3/lib/python3.9/site-packages/cnvlib/target.py", line 18, in do_target
tgt_arr = tgt_arr.subdivide(avg_size, 0)
File "/miniconda3/lib/python3.9/site-packages/skgenome/gary.py", line 689, in subdivide
return self.as_dataframe(subdivide(self.data, avg_size, min_size, verbose))
File "/miniconda3/lib/python3.9/site-packages/skgenome/subdivide.py", line 18, in subdivide
return pd.DataFrame.from_records(
File "/miniconda3/lib/python3.9/site-packages/pandas/core/frame.py", line 2297, in from_records
first_row = next(data)
File "/miniconda3/lib/python3.9/site-packages/skgenome/subdivide.py", line 44, in _split_targets
nbins = int(round(span / avg_size)) or 1
TypeError: unsupported operand type(s) for /: 'int' and 'NoneType'
Any tips how to solve this? The error confuses me since the bam files for tumor and normal have been generated similarly and I haven't had previous problems with the normal files.
Hi,
I have previously run CNVkit on default bin sizes, but now I'd like to try how using autobin affects the results. I have paired tumor-normal samples sequenced with hybrid capture method, am I supposed to use all of them as input for autobin, or only either tumor or normal samples? I understand that only the file with median file size is used for optimal bin size estimation, but obviously the set of files given as input affects the estimation. Another problem I have is that when I tested the command with either tumor or normal samples, it worked for tumor samples but for normal samples it gave an error:
Detected file format: bed
Detected file format: bed
Estimated read length 130.0
Wrote /tmp/tmpvncrgu42.bed with 100 regions
Limiting est. bin size 3250849 to given max. 500000
Splitting large targets
Traceback (most recent call last):
File "/miniconda3/bin/cnvkit.py", line 10, in
sys.exit(main())
File "/miniconda3/lib/python3.9/site-packages/cnvlib/cnvkit.py", line 10, in main
args.func(args)
File "/miniconda3/lib/python3.9/site-packages/cnvlib/commands.py", line 568, in _cmd_autobin
target_out_arr = target.do_target(
File "/miniconda3/lib/python3.9/site-packages/cnvlib/target.py", line 18, in do_target
tgt_arr = tgt_arr.subdivide(avg_size, 0)
File "/miniconda3/lib/python3.9/site-packages/skgenome/gary.py", line 689, in subdivide
return self.as_dataframe(subdivide(self.data, avg_size, min_size, verbose))
File "/miniconda3/lib/python3.9/site-packages/skgenome/subdivide.py", line 18, in subdivide
return pd.DataFrame.from_records(
File "/miniconda3/lib/python3.9/site-packages/pandas/core/frame.py", line 2297, in from_records
first_row = next(data)
File "/miniconda3/lib/python3.9/site-packages/skgenome/subdivide.py", line 44, in _split_targets
nbins = int(round(span / avg_size)) or 1
TypeError: unsupported operand type(s) for /: 'int' and 'NoneType'
Any tips how to solve this? The error confuses me since the bam files for tumor and normal have been generated similarly and I haven't had previous problems with the normal files.