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The NetworkTFs() function does not return a data frame or matrix with transcription factor names as column names. As a result, when the program tries to find transcription factors to use - tf_use <- colnames(motif2tf) - R returns an error of incorrect dimensions shortly after saying it's fitting a model. This is during the early part of the infer_grn step.

Instead, the NetworkTFs() function returns a named numeric, with names being the actual transcription factors. Therefore, my proposed edit is to change the line "tf_use <- colnames(motif2tf)" to "tf_use <- names(motif2tf) %>% na.omit()" and follow it with "motif2tf <- motif2tf[tf_use, drop = FALSE]."

The NetworkTFs() function does not return a data frame or matrix with transcription factor names as column names. As a result, when the program tries to find transcription factors to use - tf_use <- colnames(motif2tf) - R returns an error of incorrect dimensions shortly after saying it's fitting a model. This is during the early part of the infer_grn step. 

Instead, the NetworkTFs() function returns a named numeric, with names being the actual transcription factors. Therefore, my proposed edit is to change the line "tf_use <- colnames(motif2tf)" to "tf_use <- names(motif2tf) %>% na.omit()" and follow it with "motif2tf <- motif2tf[tf_use, drop = FALSE]."
@joschif
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joschif commented Jan 6, 2023

The function NetworkTFs() should return a sparse matrix with TFs a colnames, if this is not the case there is a chance something went wrong earlier in the workflow. What did you pass as motif_tfs to find_motifs()?

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