RNA-seq Based Simulations and a Cellular-Evolutionary Analysis Framework for Hyper G-Matrices

Abstract: This study integrates Hyper G-matrix theory with RNA-seq analysis to distinguish quantitative scaling from qualitative structural changes in gene regulatory networks. The Hyper G-matrix condition (A -T = D 1 BD 2 ) provides a mathematical foundation for testing whether biological differences arise from expression changes or network reorganization. Using a 4×4 simulation comparing normal and tumor tissues, we demonstrate that the Hyper-G test discriminates between conserved architecture (p=0.71) and network rewiring (p=0.004). The framework’s key contribution is separating parametric changes from topological reorganization, with implications for understanding cancer and evolution. Multi-dimensional analysis reveals how matrix properties translate to cellular phenotypes, establishing a new paradigm for genomic data analysis with applications in cancer research and evolutionary biology.