MOFUN-CCC: A Multi-omics Intermediate Fusion Network for Digital White Blood Cell Count Prediction

Abstract

As the volume of omics data continues to grow exponentially, there is an increasing demand for innovative methodologies that combine multi-omics data to extract meaningful clinical insights. Absolute cell counts are a fundamental component of clinical evaluations for disease diagnosis, treatment, and patient management. While cellular deconvolution can estimate relative cell type proportions from bulk data, obtaining absolute cell counts from omics data remains rarely studied. In response to the clinical needs and challenges, we introduce a novel multi-modal deep learning model with intermediate fusion: multi-omics fusion neural network- computational cell counting (MOFUN-CCC). This model is designed to predict absolute cell counts directly by integrating gene expression and DNA methylation data within a supervised framework, assuming that the underlying true cell components are shared across the two omics data. Comprehensive evaluations, including cross-validation, independent data testing, and real-world applications, demonstrate the model’s robustness, precision, and capacity to effectively capture biological variations. MOFUN-CCC represents a pioneering effort in the integration of multi-omics data for the prediction of absolute cell counts. With our user-friendly software (https://github.com/yuemolin/MOFUN-CCC) and web application (https://shiny.crc.pitt.edu/mofun_shiny/), this innovation holds the potential to make significant contributions to disease diagnosis, progression analysis, and clinical decision-making.