Metadata-guided Feature Disentanglement for Functional Genomics

TL;DR

This paper introduces Metadata-guided Feature Disentanglement (MFD), a novel deep learning approach that separates biological signals from technical biases in large genomics datasets, improving interpretability and downstream prediction tasks.

Contribution

MFD is a new method that incorporates metadata into model training to disentangle biological features from technical biases in functional genomics data.

Findings

MFD improves model interpretability by linking features to experimental factors.

MFD enhances downstream tasks like enhancer prediction and variant discovery.

The approach maintains or improves predictive performance.

Abstract

With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learning (DL) models to predict epigenetic readouts, such as protein binding or histone modifications, from genome sequences. However, large dataset sizes come at a price of data consistency, often aggregating results from a large number of studies, conducted under varying experimental conditions. While data from large-scale consortia are useful as they allow studying the effects of different biological conditions, they can also contain unwanted biases from confounding experimental factors. Here, we introduce Metadata-guided Feature Disentanglement (MFD) - an approach that allows disentangling biologically relevant features from potential technical biases. MFD incorporates target metadata into model training, by conditioning weights of the model output layer on different experimental factors. It then separates the factors into disjoint groups and enforces independence of the corresponding feature subspaces with an adversarially learned penalty. We show that the metadata-driven disentanglement approach allows for better model introspection, by connecting latent features to experimental factors, without compromising, or even improving performance in downstream tasks, such as enhancer prediction, or genetic variant discovery. The code for our implemementation is available at https://github.com/HealthML/MFD