#deepTAD: Identifying Topologically Associating Domains Based on Deep Learning Methods

About deepTAD

deepTAD, which is based on CNN-transformer model for feature extraction from Hi-C contact matrix, predicting the boundaries in the matrix of new samples by training the model, and subsequently, screening out the false-positive boundaries by using the Wilcox rank-sum test, and finally, completing the TAD assembly by cosine similarity.

Requirements

• python 3.7.12, tensorflow2.9.0 , numpy 1.21.6, pandas 1.3.5, scipy 1.7.3, matplotlib 3.7.2.

Usage

First--prepare_data

1. You can use "sh download_raw_hic.sh" to download the raw HiC data needed for the experiment.Then, use "sh dump_data_from_hic.sh to generate the contact frequency matrix for different normalization methods (KR or VC),all required files are in the prepare_data folder.

2. Normalized data for the other five cell lines (IMR90, K562, NHEK, HMEC, HUVEC) run the command "sh Cells_observed.sh"

Second--Sample generation

1. Identify the TAD on HIC002 using the six TAD callers 'CaTCH', 'CHAC', 'deDoc', 'DI', 'TopDom', 'Arrowhead', and find the boundaries common to any two methods

2. Generate 10×10 sample data from large Hi-C contact matrix matrices based on the generated boundaries and label them to generate training and validation datasets

3. The code needed for this section is in the "samples process.ipynb" in the sample generation folder.

Third --Model

Load the model weights and make predictions to get the TAD boundaries,Model weights for "Model/all.27.h5", run code in "Model/cnn+transformer.ipynb"

Final --Filtering false-positive borders and assembling merged TADs

1. Filter false positive boundaries with "bash assemble_filterTAD.sh"

2. The code to merge TAD is in "filter boundary and merge TAD/mergeTAD.ipynb"