phyllis9811 / deepdiffchrome

Final Project for COMSW 4995: DeepDiffChrome

DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications

@article{ArDeepDiff18,
author = {Sekhon, Arshdeep and Singh, Ritambhara and Qi, Yanjun},
title = {DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications},
journal = {Bioinformatics},
volume = {34},
number = {17},
pages = {i891-i900},
year = {2018},
doi = {10.1093/bioinformatics/bty612},
URL = {http://dx.doi.org/10.1093/bioinformatics/bty612},
eprint = {/oup/backfile/content_public/journal/bioinformatics/34/17/10.1093_bioinformatics_bty612/2/bty612.pdf}
}

Training Model

To train, validate and test the model for celltypes "Cell1" and "Cell2":

      python train.py --cell_1=Cell1 --cell_2=Cell2 --model_name=raw_d --epochs=120 --lr=0.0001 --data_root=data/ --save_root=Results/

Other Options

1. To specify DeepDiff variation:
   --model_name=
         raw_d: difference of HMs
         raw_c: concatenation of HMs
         raw: raw features- difference and concatenation of HMs
         raw_aux: raw features and auxiliary Cell type specific prediction features
         aux: auxiliary Cell type specific prediction features
         aux_siamese: auxiliary Cell type specific prediction features with siamese auxiliary
         raw_aux_siamese: raw features and auxiliary Cell type specific prediction features with siamese auxiliary

2. To save attention maps:
         use option --save_attention_maps : saves Level II attention values in .txt file

3. To change rnn size:
         --bin_rnn_size=32

Testing

To only test on a saved model:
python train.py --test_on_saved_model --model_name=raw_d --data_root=data/ --save_root=Results/

Note: Due to the acceptable file sizes, we could not upload our training data and the model checkpoints.