eQTL_Trees

This repo contains the codes and data used for the work: Predicting target genes of noncoding regulatory variants with ICE

Note that all pickle files (dataset and saved models) need to be downloaded through git-lfs

All raw data are downloaded from GTEx database (GTEx V7, tissue-specific data) and ORegAnno database. Curation process can be found in the methods section of the paper, related codes are stored under scripts/support_scripts. Generated data files include:

• Data/assembled_balanced_dataset_123.pkl - main dataset for the cross-validation study, each entry represents a variant-gene pair, in the same form as GTEx entry: [gene_id, variant_id, tss_distance, ma_samples, ma_count, maf, pval_nominal, slope, slope_se]
• Data/assembled_balanced_dataset_123_Xy.pkl - features and labels for the main dataset, each entry corresponds to a row in the feature 2d-array, names and descriptions of the features can be found in the supplementary spreadsheet and scripts/generate_X.py
• Data/test_pairs.pkl - test dataset collected from ORegAnno, same format as main dataset
• Data/test_pairs_Xy.pkl - features and labels for the test dataset
• Data/ranking_analysis.pkl.pkl - selected variants (from the main dataset) with extra negative pairs collected from GTEx, used for Figure S6
• Data/ranking_analysis.pkl_Xy.pkl - features and labels for the ranking analysis dataset

Trained (xgboost) models are stored under scripts:

• scripts/random_assembled_balanced_dataset_123_Xy_models.pkl - models trained under random cross-validation, split can be reproduced through functions in split.py, see run.py for usage. The first model under 'FULL' key (models['FULL'][0]) is used for the feature importance analysis in this work.
• scripts/position_assembled_balanced_dataset_123_Xy_models.pkl - models trained under position-based cross-validation
• scripts/maf_assembled_balanced_dataset_123_Xy_models.pkl - models trained under maf split (threshold 0.01)

Scripts used to train/evaluate models can be found in scripts/run.py More detailed analysis (to reproduce figures in the manuscript) can be found in:

• scripts/feature_importance.py - Figure 2, S1, S2
• scripts/pred_distribution.py - Figure S3, S4
• scripts/rank_analysis.py - Figure S6
• scripts/test_pairs.py - Figure 1, S5

Requirements

• numpy
• pandas
• sklearn
• xgboost
• xgbfir
• matplotlib
• seaborn