Current clustering algorithms in single cell pipelines rely on mathematical principles to evaluate data complexity, often relying on data-driven computational threshold without biological justification. This study develops a cell type identification method using epigenetic data from hundreds of diverse bio-samples as a biological reference point to infer cell diversity from single cell data. We use H3K27me3 domains deposited across >800 cell types to calculate a repressive tendency score (RTS), a quantitative value assigned to every gene that correlates to cell-type specificity. Using a topographical map concept, we utilize the rank order of genes based on RTS values as contour lines in a map to demarcate cell relationships. Using a weighted density estimation plot-based visualisation approach, RTS values act as a weighting parameter to determine cell populations in 2D whole-genome UMAP space. Analysis using an adjusted rand index (ARI) demonstrates the requirement of RTS gene rank order in anchoring cell diversity across diverse in vivo and in vitro data sets. Importantly, correlation analysis demonstrates significantly more diverse cell populations when anchoring single cell data using RTS values compared to equivalent clustering resolutions using Seurat. We couple this with a probabilistic gene clustering method to parse gene regulatory networks underpinning any cell type. Orthogonal patterns of H3K27me3 deposition patterns across hundreds of EpiMap cell types were fit into a Gaussian mixture model to identify gene modules that govern cell identity and function. We demonstrate the power of these methods by analysing atlases of in vivo and in vitro cell organogenesis and diversification and provide a web accessible dashboard for access to data and software. Collectively, genome wide epigenetic repression provides a powerful biological reference point for identifying and studying genetic regulation in single cell expression data.