ESMF: Elastic Signature-based Matrix Factorization for Robust Cellular Deconvolution

Abstract

While bulk RNA sequencing offers clinical practicality through cost-efficiency and scalability for large cohort profiling, it obscures cellular heterogeneity critical for characterizing tissue microenvironment. Conversely, single-cell RNA sequencing (scRNA-seq) resolves cellular diversity at unprecedented resolution but faces translational barriers including technical complexity and tissue processing artifacts. These complementary yet constrained technologies necessitate computational frameworks that leverage scRNA-seq data insights to enhance bulk data interpretation. Current deconvolution methods, however, remain challenged by technical noise amplification, biological implausibility, and limited rare-cell detection across diverse platforms. We developed ESMF (Elastic Signature-based Matrix Factorization), a deconvolution framework addressing these gaps through: (1) elastic expression signature-based regularization incorporating gene expression distribution parameters (mean/variance) from scRNA-seq data references, (2) negative binomial modeling of technical noise, and (3) dual-validation of marker selection integrating literature-curated knowledge with computational screening. Validated across diverse synthetic and biological datasets using a multi-tiered framework, ESMF demonstrated competitive performance against established benchmarks, exhibiting improved cross-platform stability, enhanced rare-cell detection in clinically relevant samples, and biologically plausible expression estimates. ESMF effectively bridges bulk and single-cell paradigms by resolving cellular proportions and expression profiles with biological fidelity, providing a robust analytical platform for translational tissue microenvironment investigation.