Predict-TF-Preference

R scripts to predict preferences between two TFs using data from predict-tf-binding

Dependencies

1. R (Included Dockerfile provides r-base, which is adequate)
2. Python 2.7 for filtering NegCtrl (Included Dockerfile installs python 2.7)

Calculating Preference scores

This script is designed to extract binding probabilities out of BED files generated by Predict-TF-Binding.

./predict-tf-preference.R tf1 tf2 tf1.bed tf2.bed prefs.bed

Where:

• tf1 is the name of the first transcription factor (e.g. E2f1)
• tf2 is the name of the second transcription factor (e.g. E2f4)
• tf1.bed is a bed file containing prediction scores for the first TF
• tf2.bed is a bed file containing prediction scores for the second TF
• prefs.bed is the name of the output file you'd like to write.

Filtering for NegCtrl

This script is designed to remove preference scores favoring a TF where the binding prediction was below a NegCtrl threshold

./filter-preference-threshold.py --spaces tf1.bed tf2.bed prefs.bed tf1_threshold tf2_threshold

Where:

• tf1.bed is a bed file containing prediction scores for the first TF
• tf2.bed is a bed file containing prediction scores for the second TF
• tf1_threshold is the NegCtrl threshold value (float) for tf1
• tf2_threshold is the NegCtrl threshold value (float) for tf2
• prefs.bed is the preferences generated by predict-tf-preference.R

Notes

• tf1.bed and tf2.bed must be the same length
• Only certain combinations are valid. See predict-tf-preference.R for details.
• script assumes that the first 3 columns of tf1.bed and tf2.bed (chrom, start, stop) are identical, and only difference will be the predictions in the 4th column
• Output file is generated by reusing the first 3 columns of tf1.bed and replacing score column with the calculated preference.