Present-State Resolution and Lineage History in Early Mouse Embryogenesis
Present-State Resolution and Lineage History in Early Mouse Embryogenesis examines how conclusions about developmental history depend on the resolution of the contemporaneous cellular state. Using 3,704 lineage-qualified cells from three E7.5 mouse embryos in the MELA dataset, the study joins source transcriptomes exactly to source-provided reconstructed lineage trees and separates coarse-state discordance, present-state molecular prediction, and incremental lineage prediction. Matched-control analysis shows that the historical collapse of coarse-state discordant pairs under finer molecular partitioning primarily reflects resolution of expression-associated annotation structure rather than a lineage-specific molecular-resolution effect. A fully inductive leave-one-embryo-out analysis then shows that contemporaneous molecular state improves prediction of a disjoint same-time molecular target beyond the coarse \(S_0\) comparator in each of the three held-out embryos. This ordering is observed under both signed-hash and training-fitted PCA target representations, with representation capacity influencing the retained predictive increment. Expanded lineage models test 76 three-embryo model/configuration comparisons with nested retuning under 999 conditional history permutations. One finite three-embryo positive configuration is observed, while the complete family calibration yields \(p=0.553\) and no material family-wise lineage contribution. A separate 125-bank discrete history diagnostic also yields no multiplicity-corrected departures. No tested finite molecular partition is promoted as biological identity. The paper therefore identifies additional predictive information in the measured molecular present while keeping exact state sufficiency, causal developmental memory, Markovianity, and future-state transition laws outside the authority of the present data.
Authors
- Zed James (ORCID: https://orcid.org/0009-0000-2120-0739)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-10-03
- DOI
- https://doi.org/10.5281/zenodo.23125763
- Primary Topic
- Gene Regulatory Network Analysis
- Type
- preprint