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Repressive TF prediction: "AnanseScanpy_outs/maelstrom/final.out.txt" not found #15

@bueredlemon11

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@bueredlemon11

Hello,

I have tried to reproduce the results of scANANSE manuscript using exemplary data. However, I got stuck on the final step that should predict the repressive TFs. I ran the pipeline in python following the code indicated in “AnanseScanpy_equivalent.pdf”.

On this line:

asc.import_scanpy_maelstrom(anndata=adata, cluster_id="predicted.id", maelstrom_dir="AnanseScanpy_outs/maelstrom/")

I received an error message

FileNotFoundError: [Errno 2] No such file or directory: 'AnanseScanpy_outs/maelstrom/final.out.txt

When I checked the generated files, the maelstrom folder and pfmscorefile.tsv file weren’t generated in previous steps of AnanseScanpy and anansnake. I understand that this line imports the motif enrichment results into the Scanpy object, but in my case this result is stored in another folder /AnanseScanpy_outs/gimme/pfmscorefile.tsv, which looks like

image

I therefore manually created a maelstrom folder and put the file pfmscorefile.tsv in there. I then changed the function import_scanpy_maelstrom() on line 187 in “anansescanpy_import.py” script as follows:

# Import the output df from maelstrom
    # maelstrom_df = pd.read_csv(str(maelstrom_dir + "final.out.txt"),sep="\t",index_col=0)
maelstrom_df = pd.read_csv(str(maelstrom_dir + "pfmscorefile.tsv"),sep=",",index_col=0)

But I received another error from another function per_cluster_df()
ValueError: Length mismatch: Expected axis has 0 elements, new values have 131368 elements

Thus, I wanted to ask: what is this “AnanseScanpy_outs/maelstrom/final.out.txt” file? Should I exclude first 4 rows of pfmscorefile.tsv and save it as final.out.txt? The anansnake step didn't give me error messages.

Please find attached log file:
2024-09-23T143049.814049.snakemake.log

I ran scANANSE on a high-performance computing cluster provided within university, instead of my local PC. Could that be a reason why?

Your help would be greatly appreciated!

Best,
Xinqi

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