This study develops and implements a versatile cell multiplexing and data analysis pipeline that uses consortium-level epigenetic data to focus analysis of cell type identity and function to understand mechanisms of cell differentiation. We engineer a cell barcoding system enabling upscaled, customisable experimental design of single-cell RNA sequencing. We employ this in a multiplexed study of in vitro human induced pluripotent stem cell differentiation to generate a dataset capturing 62,208 cells across 62 independent experimental conditions comprising 8 differentiation time points and 9 developmental signalling perturbations with replicates. Following this, we develop and implement a computational pipeline that overcomes limitations of mathematically-defined analysis algorithms by using an orthogonal epigenetic reference point to understand cell type diversity in our data. To do this, we utilise an algorithm (TRIAGE) that summarises H3K27me3 repression of genes across 111 diverse cell types in the NIH Epigenome Roadmap data, to identify cell diversity in our data. We then use shared patterns of H3K27me3 deposition over a list of identity-defining genes for each cell type to infer epigenetically co-modulated gene sets that reveal the functional and regulatory basis of each cell type. This TRIAGE-informed analysis applied to our dataset identified 48 well-defined cell types spanning ectoderm, endoderm, and mesoderm lineages to study the temporal, signalling, and gene regulatory basis of multi-lineage iPSC differentiation. Overall, this cellular barcoding and computational pipeline facilitates analysis of cell differentiation, applicable across diverse fields of developmental biology, drug discovery, and disease modelling.