Python package for predicting single-cell CpG methylation states from DNA sequence and neighboring CpG sites using deep neural networks (Angermueller et al., 2016).

Angermueller, Christof, Heather Lee, Wolf Reik, and Oliver Stegle. “Accurate Prediction of Single-Cell DNA Methylation States Using Deep Learning.” bioRxiv, May 27, 2016, 055715. doi:10.1101/055715.

Clone the DeepCpG repository into you current directory:

  git clone https://github.com/cangermueller/deepcpg.git

Install DeepCpG and its dependencies:

python setup.py install

1. Store known CpG methylation states of each cell into a tab-delimted file with the following columns:
   • Chromosome (without chr)
   • Position of the CpG site on the chromosome
   • Binary methylation state of the CpG sites (0=unmethylation, 1=methylated)

Example:

1   3000827   1.0
1   3001007   0.0
1   3001018   1.0
...
Y   90829839  1.0
Y   90829899  1.0
Y   90829918  0.0

2. Run dcpg_data.py to create the input data for DeepCpG:

  dcpg_data.py
  --cpg_profiles ./cpg/cell1.tsv ./cpg/cell2.tsv ./cpg/cell3.tsv
  --dna_files ./dna/*.dna.chromosome.*.fa*
  --cpg_wlen 50
  --out_dir ./data

./cpg/cell[123].tsv store the methylation data from step 1., dna contains the DNA database, e.g. mm10 for mouse or hg38 for human, and output data files will be stored in ./data.

3. Fine-tune a pre-trained model or train your own model from scratch with dcpg_train.py:

  dcpg_train.py
    ./data/c{1,2,3}_*.h5
    --val_data ./data/c{10,11,13}_*.h5
    --dna_model CnnL2h128
    --cpg_model RnnL1
    --joint_model JointL2h512
    --nb_epoch 30
    --out_dir ./model

This command uses chromosomes 1-3 for training and 10-13 for validation. dna_model, cpg_model, and joint_model specify the architecture of the CpG, DNA, and joint model, respectively. Training will stop after at most 30 epochs and model files will be stored in ./model.

4. Use dcpg_eval.py to predict missing methylation states and evaluate prediction performances:

  dcpg_eval.py
    ./data/c*.h5
    --model_files ./model/model.json ./model/model_weights_val.h5
    --out_data ./eval/data.h5
    --out_report ./eval/report.tsv

This command predicts missing methylation states of all cells and chromosomes and evaluates prediction performances using known methylation states. Predicted states will be stored in ./eval/data.h5 and performance metrics in ./eval/report.tsv.

Interactive examples on how to use DeepCpG can be found here.

Pre-trained models can be downloaded from the DeepCpG model zoo.

• /deepcpg/: Source code
• /docs: Documentation
• /examples/: Examples for using DeepCpG
• /script/: Executable scripts for data creation, model training, and interpretation
• /tests: Test files

• Christof Angermueller
• [email protected]
• https://cangermueller.com