MultiPert: An adversarial alignment and dual attention framework for single-cell multi-omics perturbation prediction

TL;DR

MultiPert is a new deep learning framework that predicts how cells respond to perturbations using multi-omics data, offering better accuracy and insights into biological pathways.

Contribution

Introduces MultiPert, a novel framework for multi-omics perturbation prediction using adversarial alignment and dual attention mechanisms.

Findings

MultiPert achieves superior accuracy in predicting gene expression and protein abundance compared to existing methods.

The framework generalizes to unseen perturbations and uncovers regulatory mechanisms of immune checkpoint molecules.

Enrichment analyses reveal immune-related pathways from perturbed transcriptomic predictions.

Abstract

Precise prediction of perturbation responses is essential in systems biology research, as it plays a pivotal role in characterizing cellular identities and elucidating the regulatory mechanisms of biological pathways. Existing perturbation-responses prediction approaches are predominantly confined to single-modality transcriptomic data, limiting their capacity to capture cross-layer molecular effects. Here, we present MultiPert, a deep learning framework specifically designed for predicting perturbation responses in single-cell multi-omics data. MultiPert employs modality-specific encoders with dedicated pretraining, integrates perturbation through a dual-attention mechanism, and achieves cross-modal alignment via adversarial training. Benchmarking on human THP-1 and kidney multi-omics datasets demonstrates that MultiPert reliably predicts both perturbed gene expression and protein abundance profiles, achieving superior accuracy and stability compared to state-of-the-art strategies. MultiPert generalizes to unseen perturbations and uncovers regulatory mechanisms of immune checkpoint molecules based on perturbed proteomic predictions. In addition, enrichment analyses of perturbed transcriptomic predictions reveal immune-related pathways. By providing an integrated and interpretable framework, MultiPert expands the scope of perturbation modeling at the multi-omics level, thereby offering a robust methodological foundation for comprehensive research into pathogenesis and drug discovery.

In systems biology research, accurately predicting how cells respond to perturbations—such as gene knockout or drug intervention—is crucial for understanding cell identities and the regulatory mechanisms of biological pathways. However, most existing methods only support scRNA-seq data and cannot capture perturbation effects from single-cell multi-omics data. To address this limitation, we developed MultiPert to predict perturbation responses while integrating single-cell multi-omics data. It uses dedicated encoders for different molecular layers to capture unique biological signals, aligns multi-omics data through adversarial training, and fuses perturbation information via a dual attention mechanism. Experiments on different tissues show that MultiPert outperforms existing methods in predicting gene expression and protein abundance. It can also predict unseen perturbations and uncover the regulatory mechanisms. We hope this work provides a more comprehensive tool for studying disease pathogenesis and drug discovery, making multi-omics-level perturbation research more accessible.

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