ValueError: columns cannot be a set about cite-seq-count HOT 5 OPEN

Hello @jamesboot
A few things I would test out.

I think it might be because this version doesn't check for 0 counts and I think this is what is happening there.

1. Are you sure about the UMI stopping at 16bp? Not super important, but it might give you a little increase in your UMI counts depending on your library's diversity and its size.
2. Can you run the same without the whitelist? I suspect There is no cell barcode overlap. This usually happens on SCv3 from 10x runs.

from cite-seq-count.

Hi Patrick,

Thanks very much for your quick response. I tried running without the whitelist (all other options the same as above) but still got the same error message:

Correcting umis
Traceback (most recent call last):
  File "/data/home/hmy961/citeseq-counts-env/bin/CITE-seq-Count", line 8, in <module>
    sys.exit(main())
  File "/data/home/hmy961/citeseq-counts-env/lib/python3.8/site-packages/cite_seq_count/__main__.py", line 603, in main
    io.write_dense(
  File "/data/home/hmy961/citeseq-counts-env/lib/python3.8/site-packages/cite_seq_count/io.py", line 48, in write_dense
    pandas_dense = pd.DataFrame(sparse_matrix.todense(), columns=columns, index=index)
  File "/data/home/hmy961/citeseq-counts-env/lib/python3.8/site-packages/pandas/core/frame.py", line 639, in __init__
    raise ValueError("columns cannot be a set")
ValueError: columns cannot be a set

I'm pretty sure the UMI stops at 16bp. We are using 10X 5' v2 chemistry if that helps.

from cite-seq-count.

I think this is a bug with the newest pandas version: facebook/Ax#1153

Can you try to reinstall with pandas 1.4?

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I think this is a bug with the newest pandas version: facebook/Ax#1153

Can you try to reinstall with pandas 1.4?

This fixed the error for me. I think it's because, in pandas 1.5, they no longer allow DataFrame columns to be set by a set datatype. io.py has a line that creates a DataFrame by setting the columns with a set datatype.

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Sorry for taking some time to come back to this. Running with pandas 1.4 fixed the problem for me too! Thanks for your help!

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