Convolutional Neural Network (CNN) for HI-C Interaction Count Prediction

Usage

python hicnn.py [chromosome #] [cell type]

python hicnn.py 1 Gm12878

Input Files

• input/[chromosome #]/raw_sequence.txt

  chr1_5000_10000 CACTGTAAAAATGGAAGTAATTCCCATTAGGACCAGCAAAACCTGAGGCTAAAAAAAGACAGTAAAAGCTCATGCCAAAAGCTGAATTTTACTTAATATAAAGAAAGGTGGCAGTTTCCAATTTCAGTAGAAAGTAGGAGTGTCAAATTGCTACAGAAACTGCCATCCTCCAGAGACTGACGACCCGAATGAACCCAGAGGCAATTTTTTATTCTCATGAGATGGCTTGCTTAGATATTTCTGGGAAGGAGCAGTAGGTCTTAGGAAAGGTTAGAATGTTGTTGTTTCCTGGTAACTACTTGCAGAGGTTGATAGGAGTCAATGAGACCAA...

• input/[choromosome #]/[cell type]/markers.txt (Currently not in use as of 5/7/2017)

  [region index] \t [chromatin marker signal 1] \t [chromatin marker signal 2] \t ... \n

• input/[choromosome #]/[cell type]/train[dataset fold].txt

  [enhancer region index] \t [promoter region index] \t [interaction count] \n

• input/[choromosome #]/[cell type]/test[dataset fold].txt

  [enhancer region index] \t [promoter region index] \t [interaction count] \t [pairwise distance] \t [RIPPLE predicted count] \n

Output Files

File	Description
output/[chromosome #]_[cell type].hdf5	learned model saved in hdf5 format
output/[chromosome #]_[cell type].txt	log file of learning curve
output/[chromosome #]_[cell type]_distribution.png	mean and standard deviation of actual counts plotted next to those of predicted counts, by pairwise distance
output/[chromosome #]_[cell type]_pearson.png	Pearson correlation coefficient between predicted and actual counts, by pairwise distance
output/[chromosome #]_[cell type]_scatter.png	scatterplot of predicted vs actual interaction counts