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Is it mandatory to use FIMO, or would any motif matching algorithm that provides confidence scores suffice? I've tried to use MotifMatchR (implemented natively in the Signac package) as a PWM, however the results I obtain are somewhat confusing. Given this is the only step in which I differ from your analysis, I was wondering if this might be the cause.
If FIMO is the only motif matching algorithm you'd recommend using, could you give a bit more information on how to do it? I couldn't see any python/R package to apply it. Any help would be appreciated!
Thank you again,
Best,
The text was updated successfully, but these errors were encountered:
Dear authors,
First of all, thank you very much for this great work and package!
I have a question regarding the TF-activity inference. In your tutorial (https://github.com/dpeerlab/SEACells/blob/main/notebooks/SEACell_tf_activity.ipynb), you use a precomputed PWM matrix. In the methods of the associated paper, you mention you used FIMO to compute the scores.
Is it mandatory to use FIMO, or would any motif matching algorithm that provides confidence scores suffice? I've tried to use MotifMatchR (implemented natively in the Signac package) as a PWM, however the results I obtain are somewhat confusing. Given this is the only step in which I differ from your analysis, I was wondering if this might be the cause.
If FIMO is the only motif matching algorithm you'd recommend using, could you give a bit more information on how to do it? I couldn't see any python/R package to apply it. Any help would be appreciated!
Thank you again,
Best,
The text was updated successfully, but these errors were encountered: