scProca: A Cross-Attention-Enhanced Deep Generative Model for Single-Cell Transcriptomics and Proteomics Integration and Imputation.

Abstract

Understanding the molecular mechanisms of complex diseases requires insight into cellular interactions and protein expression. While large-scale sequencing enables disease subtyping and patient stratification, integrating proteomics and transcriptomics data offers a deeper view of cellular states. Recent methods combine scRNA-seq, which provides broad cellular coverage, with transcriptomics and proteomics co-profiling, which provides more comprehensive molecular measurements. However, many models adopt simplistic strategies for joint analysis. We introduce scProca, a deep generative model that incorporates inter-cellular relationships via cross-attention mechanisms to handle heterogeneous inputs, whether from RNA-seq or co-profiling datasets. scProca achieves state-of-the-art integration and imputation, remains robust under high protein sparsity, generalizes across species and tissues, scales to large datasets, and is compatible with experimental batches, demonstrating strong flexibility for complex experimental settings.