DeepCpG: Deep neural networks for predicting single-cell DNA methylation

DeepCpG is a deep neural network for predicting the methylation state of CpG dinucleotides in multiple cells. It allows to accurately impute incomplete DNA methylation profiles, to discover predictive sequence motifs, and to quantify the effect of sequence mutations. (Angermueller et al, 2017).

If you encounter any issue or if you have ideas how to improve DeepCpG, please submit an issue.

DeepCpG model training and applications. (a) Sparse single-cell CpG profiles, for example as obtained from scBS-seq or scRRBS-seq. Methylated CpG sites are denoted by ones, un-methylated CpG sites by zeros, and question marks denote CpG sites with unknown methylation state (missing data). (b) Modular architecture of DeepCpG. The DNA module consists of two convolutional and pooling layers to identify predictive motifs from the local sequence context, and one fully connected layer to model motif interactions. The CpG module scans the CpG neighbourhood of multiple cells (rows in b), using a bidirectional gated recurrent network (GRU), yielding compressed features in a vector of constant size. The fusion module learns interactions between higher-level features derived from the DNA- and CpG module to predict methylation states in all cells. (c,d) The trained DeepCpG model can be used for different downstream analyses, including genome-wide imputation of missing CpG sites (c) and the discovery of DNA sequence motifs that are associated with DNA methylation levels or cell-to-cell variability (d).

Angermueller, Christof, Heather Lee, Wolf Reik, and Oliver Stegle. Accurate Prediction of Single-Cell DNA Methylation States Using Deep Learning. http://biorxiv.org/content/early/2017/02/01/055715 bioRxiv, February 1, 2017, 55715. doi:10.1101/055715.

Installation
Getting started with DeepCpG in 30 seconds
Examples
Model Zoo
FAQ
Content
Contact

Installation

The easiest way to install DeepCpG is to use PyPI:

pip install deepcpg

Alternatively, you can checkout the repository,

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

and then install DeepCpG using setup.py:

python setup.py install

Getting started with DeepCpG in 30 seconds

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 starting with one
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

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.

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 module, respectively (see manuscript for details). Training will stop after at most 30 epochs and model files will be stored in ./model.

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.

Export imputed methylation profiles to bedGraph files:

dcpg_eval_export.py
  ./eval/data.h5
  -o ./eval
  -f bedGraph

Examples

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

Model Zoo

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

FAQ

Why am I getting warnings 'No CpG site at position X!' when using `dcpg_data.py`? This means that some sites in --cpg_profile files are not CpG sites, e.g. there is no CG dinucleotide at the given position in the DNA sequence. Make sure that --dna_files point to the correct genome and CpG sites are correctly aligned. Since DeepCpG currently does not support allele-specific methylation, data from different alleles must be merged (recommended) or only one allele be used.

How can I train models on one or more GPUs? DeepCpG use the Keras deep learning library, which supports Theano or Tensorflow as backend. If you are using Tensorflow, DeepCpG will automatically run on all available GPUs. If you are using Theano, you have to set the flag device=GPU in the THEANO_FLAGS environment variable.

THEANO_FLAGS='device=gpu,floatX=float32'

You can find more information about Keras backends here, and about parallelization here.

Content

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

BioCogito/deepcpg