This repository contains datasets and scripts to reproduce results from "Improving representation learning of genomic sequence motifs in convolutional netowrks with exponential activations" by Peter K. Koo and Matthew Ploenzke.

Dependencies

Python 3
Dependencies: Tensorflow r1.15 and r2.0 (tested), matplotlib, numpy, scipy, sklearn, shap, logomaker, pandas, h5py
meme suite (5.1.0) to run Tomtom (Note: Parsing issues may occur if a different version of meme is used)

Overview of main code

There are 6 main tasks and code is labeled accordingly. The description of each script is given below.

Task 1 -- Classifying TF binding from synthetic data

task1_step1_train_models.py
task1_step2_tomtom.sh
task1_step3_filter_match.py
task1_step4_plot_filter_match.ipynb
task1_step5_plot_filters_with_hits.ipynb
task1_step6_plot_1stlayer_activation_comparison.ipynb
task1_step7_PWM_scan_comparison.ipynb
task1_step8_log_activation_train.py
task1_step9_log_activation_tomtom.sh
task1_step10_log_activation_filter_match.py
task1_step11_log_activation_plot_filter_match.ipynb

Task 2 -- Classifying TF binding from in vivo data

task2_step1_train_models.py
task2_step2_tomtom.sh
task2_step3_filter_match.py
task2_step4_plot_filter_match.ipynb
task2_step5_plot_filters_with_hits.ipynb

Task 3 -- Classifying cis-regulatory codes from synthetic data

task3_step1_train_model.py
task3_step2_attribution_scores.py
task3_step3_plot_attr_score_comparison.ipynb
task3_step4_plot_attr_logo_comparison.ipynb
task3_step5_log_activation_train_model.py
task3_step6_log_activation_attribution_scores.py
task3_step7_log_activation_plot_attr_score_comparison.ipynb

Task 4 -- Classifying chromatin accessibility for DNase-seq data from the Basset dataset

task4_step1_train_model.py
task4_step2_tomtom.sh
task4_step3_plot_attr_score_comparison.ipynb

Task 5 -- Classifying TF binding for ZBED2 ChIP-seq data

task5_step1_train_model.py
task5_step2_plot_attr_score_comparison.ipynb

Task 6 -- Classifying TF binding for IRF1 ChIP-seq data

task6_step1_train_model.py
task6_step2_plot_attr_score_comparison.ipynb

Other

helper.py
- functions that are useful to run analysis
plot_activation_functions.ipynb
tfomics
- additional useful functions for NN models and interpretability
model_zoo
- CNN models used in this study
generate_data
- Notebooks used to generate data for Task 1, Task2, and Task3

Control Experiments

Check stability of training/validation metrics throughout training

visualize_training_history.ipynb
gradient_analysis_step1_train.py
gradient_analysis_step2_visualize.ipynb

Check robustness to different initialization strategies

initialization_step1_train.py
initialization_step2_tomtom.sh
initialization_step3_filter_match.py
initialization_step4_visualize_filter_match.ipynb

Check robustness to Normal intialization with varying std. dev.

initialization_sweep_step1_train.py
initialization_sweep_step2_tomtom.sh
initialization_sweep_step3_filter_match.py
initialization_sweep_step4_visualize_filter_match.ipynb

Check robustness to varying scaling factor in exponential activations

exp_scale_sweep_step2_tomtom.py
exp_scale_sweep_step3_filter_match.py
exp_scale_sweep_step4_visualize.ipynb

Check robustness of threshold used to visualize filter representations

threshold_sweep_step1_test.py
threshold_sweep_step2_tomtom.sh
threshold_sweep_step3_filter_match.py
threshold_sweep_step4_visualize_filter_match.ipynb

Check robustness to number of background sequences for attribution methods

background_analysis_step1_sweep.py
background_analysis_step2_plot_sweep.ipynb

Figures

The figures from the paper are generated by the corresponding notebooks.

Main Figures

Fig. 1a: code/plot_activations.ipynb
Fig. 1b: code/task1_step4_plot_filters_with_hits.ipynb
Fig. 1c: code/task1_step5_plot_filter_match.ipynb
Fig. 1d: code/task2_step4_plot_filter_match.ipynb
Fig. 2a-c: code/task3_step4_plot_attr_score_comparisons.ipynb
Fig. 2d: code/task3_step5_plot_attr_logo_comparisons.ipynb
Fig. 3a: code/task4_step3_plot_attr_score_comparison.ipynb
Fig. 3b: code/task5_step2_plot_attr_score_comparison.ipynb
Fig. 3c: code/task6_step2_plot_attr_score_comparison.ipynb

Extended Data Figures

Extended Data Fig. 1a,b: code/task1_step5_plot_filter_match.ipynb
Extended Data Fig. 1c,d: code/task1_step7_PWM_scan_comparison.ipynb
Extended Data Fig. 1e: code/task1_step11_log_activation_plot_filter_match.ipynb
Extended Data Fig. 2a,b: code/task3_step4_plot_attr_score_comparisons.ipynb
Extended Data Fig. 2c,d: code/task3_step5_plot_attr_logo_comparisons.ipynb

Supplemental Figures

Supp Fig. 1: code/task1_step4_plot_filters_with_hits.ipynb
Supp Fig. 2: code/task1_step4_plot_filters_with_hits.ipynb
Supp Fig. 3: code/controls/visualize_training_history.ipynb
Supp Fig. 4: code/controls/gradient_analysis_step2_visualize.ipynb
Supp Fig. 5a: code/controls/initialization_step4_visualize_filter_match.ipynb
Supp Fig. 5b: code/controls/initialization_sweep_step4_visualize_filter_match.ipynb
Supp Fig. 6: code/task2_step5_plot_filters_with_hits.ipynb
Supp Fig. 7: code/plot_activations.ipynb
Supp Fig. 8: code/task2_step4_plot_filter_match.ipynb
Supp Fig. 9: code/controls/exp_scale_sweep_step4_visualize.ipynb
Supp Fig. 10: code/task1_step6_plot_1stlayer_activation_comparison.ipynb
Supp Fig. 11a: code/task3_step4_plot_attr_score_comparisons.ipynb
Supp Fig. 11b: code/task3_step7_log_activation_plot_attr_score_comparison.ipynb
Supp Fig. 12: code/task4_step3_plot_attr_score_comparison.ipynb
Supp Fig. 13: code/task5_step2_plot_attr_score_comparison.ipynb
Supp Fig. 14: code/task6_step2_plot_attr_score_comparison.ipynb
Supp Fig. 15: code/controls/threshold_sweep_step4_visualize_filter_match.ipynb
Supp Fig. 16: code/controls/background_analysis_step2_plot_num_background_sweep.ipynb

p-koo/exponential_activations