Awesome
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
<p align="center"> https://<i></i>github.com/shwhalen/targetfinder/tree/master/paper/targetfinder/<b>celline</b>/output-ep/pairs.csv </p>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