EPIANN

Inspired by machine translation models we develp an attention-based nerual network model, EPIANN.

Data Augmentation

There are 6 cell lines. which are celline=GM12878, HUVEC, HeLa-S3, IMR90, K562 and NHEK, and each comes with its own folder. Within each folder, there is a single file: celline.csv. celline.csv is a renamed copy of

https://github.com/shwhalen/targetfinder/tree/master/paper/targetfinder/celline/output-ep/pairs.csv

Before we actually train oorneural network model, we need to generate input data from genomic coordinates(hg19) of enhancers and promoters, along with the indicators of EPIs recorded in celline.csv. Data_Augmentation.R encoded an automatic data augmentation pipeline with several parameters specified in the following table.

Parameters	Explanation
celline	change it to one of the 6 cell lines with default = "IMR90"
folder	the name of the folder to hold all output files with default = "aug_50"
shift_distance	the step size to slide extended region around the enhancer and promoter with default = 50
enhancer_target_length	the length of extended enhancer with default = 3000
promoter_target_length	the length of extended promoter with default = 2000
positive_scalar	the augmentation ratio with default = 20
test_percent	the percent of test data among all with default = 0.1
random_seed	the random seed to sample test data with default = 1

You can find the output files with default parameters under the directory IMR90/aug_50/. The following files are currently not avaiable in the github repository because of the size limit (Work In Progress). They are avaiable in the repository.

IMR90/aug_50/IMR90_enhancer.fasta
IMR90/aug_50/IMR90_promoter.fasta
IMR90/aug_50/imbalanced/IMR90_enhancer.fasta
IMR90/aug_50/imbalanced/IMR90_promoter.fasta

Train Neural Netork Model

Under the directory IMR90/, you can find an example python script IMR90_EPIANN.py with the default setting. The parameters regarding inputs are explained in the following table.

Parameters	Explanation
celline	chanage it to one of the 6 cell lines with default = 'IMR90'
file_pre	change it to be the folder containing augmented data with default = 'aug_50/IMR90'
out_dir	change it to be the folder that contains the output with dedault = 'output/IMR90_EPIANN'
script_id	change it to be the current python script name in order to distinguish the outputs from multiple runs with default = 'IMR90_EPIANN'

The computational grpaph for the neural network is programmed using Tensorflow. On our setup, we use a single NVIDIA GTX 1080 or NVIDIA TITAN X with 5 CPU threads. A single batch takes about 6 seconds to train. All neural neural parameters can be altered in the script.

Neural Network Parameters	Explanation
enhancer_length	the length of input enhancers with default = 3000
promoter_length	the length of input promoters with default = 2000
BATCH_SIZE	the half of exact batch size with default = 32
num_filters	the number of convolution filters with default = 256
e_conv_width	the convolutional filter width with default = 15
dropout_rate_cnn	the dropout rate for the convolution layer with default = 0.2
dropout_rate	the dropout rate for all layers except the convolution layer with default = 0.2
pool_width	the max pooling size with default = 30
atten_hyper	the dimension of the attention-related parameters with default = 32
dense_neuron_coor	the dimension of the fully connected layers for coordinate prediction with default = [128, 64]
inter_dim	the dimension of the interaction quantification related parameters with default = 1
topk	the top-k pooling size with default = 32
dense_neuron	the dimension of the fully connected layers with default = 32
lamb	the hyperparameter which mediates the cross-entropy error and the regression error with default = 10
num_of_epoch	the number of epochs with default = 90
output_step	the step size to report performance on test dataset with default = 500 batches

Required Pre-installed Packages

R (3.4.2) Library dependencies

GenomicRanges 1.28.2
BSgenome.Hsapiens.UCSC.hg19.masked 1.3.99

Python (2.7.6) Module dependencies

Sklearn 0.18.1

os
pickle
time
tensorflow 1.3.0
numpy 1.13.3
Sklearn 0.19.1
Biopython 1.67