jianhao2016 / E2M

source code and sample data for E2M

E2M

E2M source code and sample dataset.

Requirements

• python 3.0+
• numpy, TensorFlow r1.2, matplotlib

Data Description

Raw methylation and expression files are available for download via the GDC data portal.

A small dataset for GBM may be found in the folder /data/GBM/ for testing and validation purposes. Additional larger datasets are available upon request. The folder /data/download_from_TCGA also contains sample codes for downloading/extracting other genes/cancer datasets of interest. All datasets are stored in a Python dictionary format.

How to Use the Software

Folder /data/ includes codes for downloading/extracting/preprocessing of the data. More details can be found inside the data folder under the README heading.

The folder /script/ contains codes for MR (Multiclass Regression), FCNN (Fully Connected Neural Network), E2M and visualization.

Install

# download METHCOMP, go to terminal and enter:
git clone https://github.com/jianhao2016/E2M.git

Run

• To run E2M on the test file, first ensure that all the packages have been installed with the correct version of Python. Then go to folder /script/inception_with_diff_type_of_error/ and use the following command:

  python multi_gene_multi_cancer_inception.py --cancer_type gbm --gene mgmt --quantization 0

  This command will train an unquantized E2M on the provided GBM dataset and list the accuracy of the given test set. You can also use python multi_gene_multi_cancer_inception.py --help to see all available configuration options.

• To run MR or FCNN, go to folder /script/MR_and_FCNN/ and use the following command:

  python multi_class_regression.py --cancer_type gbm --gene mgmt

  or

  python fcnn_multi_gene_multi_cancer.py --cancer_type gbm --gene mgmt

  respectively.

• The PCA/histogram/Heatmap visualization codes may be run in a similar way, with the corresponding files PCA_visualization.py, plot_histogram.py and selected_gene_heatmap.py

Output Files

The prediction accuracy and error will be printed on the screen. All trained models will be stored in /data/ in TensorFlow SavedModel format.