DeepCT

Disclaimer:

This code is provided under Apache License, Version 2.0

How to run:

Install requirements

pip install -r requirements.txt

If you run into any problems with installation of selene, you can also consult selene's official instruction

Run scripts specified by config files, e.g.:

python -m selene_sdk model_configs/config_file.yml

Data

There are several scripts to download and process the data.

data/download_boix_et_al_data.sh to download raw *.bigWig files
data/process_boix_et_al_data.py to preprocess the *.bigWig files
data/annotate_biox_data.py to produce selene-style .bed file containing intervals of present track peaks.
data/generate_target_files.py to produce dataset files to specify in configuration files to run training (see data/README.md for more info).

Training the models

Make sure you have prepared your data.
Run training using your configuration file, for example model_configs/train_example.yml.

Config files for benchmarks mentioned in the paper (most configs assume necessary data files have already been generated and placed into a folder dataset_data):

cell type specificity: model_configs/boix_train_masked_ct.yml (classification) and boix_qDeepCT_train_masked_ct.yml (regression).

Note: Before running this config you should also generate all necessary data files for tracks forming a maximal clique and place them into a folder dataset_data/maximal_clique_with0 and additionally run src/split_intervals.py and src/split_cell_types.npy and place their results in the same folder;
sequence specificity: model_configs/boix_all_multi_ct_train.yml (classification) and model_configs/boix_allNonTreated_highqual_multi_ct_qDeepCT_train_2021-11-15-03-30-59.yml (regression);
masked tracks prediction: model_configs/boix_allNonTreated_highqual_masked_multi_ct_qDeepCT_train.yml.

Inference

Prepare pre-trained model and input files.
Run inference using your configuration file, for example model_configs/inference_example.yml.