README.md

DeepCpG examples

Here you can find IPython notebooks and shell scripts that illustrate how to use DeepCpG.

To setup the required dependencies, execute setup.sh:

bash setup.sh

Notebooks

./notebooks contains IPython notebooks, which can be executed interactively. They can be exported to shell scripts and executed in the terminal by selecting File -> Download as -> Bash (.sh). This is recommended for large-scale experiments, e.g. training models on the entire data set.

• DeepCpG basics: Pre-processing data, training models, and evaluating models.
• Fine-tuning: Fine-tuning a pre-trained model to speed-up training.
• Motif analysis: Visualizing and analyzing learned motifs.
• Mutations effects: Computing and visualizing mutations effects.
• Predicting statistics: Predicting statistics such as cell-to-cell variance.

Shell scripts

./scripts contains shell scrips with recommended default parameters. They may help you to easily build a DeepCpG pipeline for creating data, training models, and evaluating models. Set test_mode variable in scripts to 1 for testing, and 0 otherwise.

• lib.sh: Global variable definitions and functions.
• data.sh: Creates DeepCpG data files.
• train.sh: Trains DeepCpG DNA, CpG, and Joint model separately.
• eval.sh: Evaluates prediction performances and imputes methylation profiles.