JackXu2333 / Reproducibility_Analysis_on_GIT

Reproducibility Analysis on Genomic Impact Transformer (GIT)

Reproducibility Analysis on Genomic Impact Transformer (GIT)

Introduction

This repository contains PyTorch implementation of GIT model and GIT variants from reproducibility analysis by Sijie Xu, and in the original paper by Yifeng Tao, Chunhui Cai, William W. Cohen, Xinghua Lu. From genome to phenome: Predicting multiple cancer phenotypes based on somatic genomic alterations via the genomic impact transformer. Proceedings of the Pacific Symposium on Biocomputing (PSB). 2020.

The preprocessed TCGA dataset, and gene embeddings mentioned in the paper, and reproducibility analysis are also released below.

Link to the Tao et al.'s github repository

Prerequisites

The code runs on Python 3.6 (The original implementation by Tao et al. is in Python 2.7), and following packages are used:

• PyTorch, Jupyter Notebook, pandas, pickle, numpy, random, argparse, os.

The code automatically enable CUDA when available.

Data

The required data can be downloaded to the directory:

cd genome-transformer
wget www.cs.cmu.edu/~yifengt/paper/2019a/data.tar.gz -O data.tar.gz
tar -zxvf data.tar.gz

Additionally, the pretrained gene embedding files need to be copied to data_degraded/ and data_noBin/ in order to run tests in the reproducibility analysis:

find data_degraded data_noBin -exec cp data/gene_emb_pretrain.npy {} \;

TCGA dataset

The preprocessed SGA-DEG TCGA dataset is available at data/dataset.pkl, which contains SGAs, DEGs, cancer types, and barcodes of 4,468 cancer samples from TCGA:

data = pickle.load( open("data/dataset.pkl", "rb") )

Gene embeddings

Two types of gene embeddings are available:

• Gene2Vec-pretrained gene embeddings: data/gene_emb_pretrain.npy;
• Gene2Vec-pretrained + GIT-finetuned gene embeddings: data/gene_emb_finetune.npy.

You may extract the gene embeddings in Python:

gene_emb_matrix = numpy.load("data/gene_emb_finetune.npy")

It is a 19782 by 512 matrix, where the index of each row can be mapped to a gene name through data/idx2gene.txt.

Replicate experiments

Vanilla GIT model

You can train the GIT from scratch and then evaluate its performance on test set:

python3 test_run.py

You may run more GIT variants, e.g., GIT-init, GIT-attn, GIT-can etc., by checking the code and comments of test_run.py, or:

python3 test_run.py --help

Reproducibility experiments

Please refer to the MLP_Testing.ipynb for code used in verifying baseline result from the original paper and performing the additional baseline test described in the reproducibility analysis.

Please refer to the GIT_Testing.ipynb for code used in GIT model verifying empirical result from the original paper and performing modified input test described in the reproducibility analysis.

Please also refer to GIT_Testing_NOBIN.ipynb for code used in performing non-binary target test described in the reproducibility analysis.

The file used in constructing the dataset for the modified input test and non-binary target test can be found via Data_Uilts.ipynb

Note that utlis.py has been modified in order to perform test in the reproducibility analysis, but its usage remains unchanged

If you find the data or code from this repository helpful, please cite the original paper:

@inproceedings{tao2020git,
  title = {From Genome to Phenome: Predicting Multiple Cancer Phenotypes based on Somatic Genomic Alterations via the Genomic Impact Transformer},
  author = {Tao, Yifeng  and 
    Cai, Chunhui  and 
    Cohen, William W.  and 
    Lu, Xinghua},
  booktitle={Pacific Symposium on Biocomputing},
  year = {2020},
}