The Cislo–Siggia (2025) landscape method was validated on real single-cell data — mESC neural-tube flow cytometry and scRNA-seq — with KL divergence ≲0.3 bits from the measured distributions

Beyond a synthetic "heteroclinic flip" toy landscape fitted from a known potential, the method of claim-cislo-siggia-2025-fit-waddington-landscape-directly-to-single-cell-gene-expression was applied to two real experimental single-cell sources from the same mouse-embryonic-stem-cell (mESC) system — a neuromesodermal progenitor driven toward the ventral neural tube under varying Sonic Hedgehog agonist (SAG) doses:

1. Multicolor flow cytometry — time courses sampled at 24-hour intervals across SAG concentrations; 8,000 cells subsampled across measurement times and doses. Fit quality: "The DKL between the simulated and measured distributions is ≲ 0.3 bits for all conditions," against measured entropies Hmeas ≳ 11 bits.
2. scRNA-seq — a separately published dataset (Fontaine et al.), restricted to 7,293 cells sequenced between Day 5 and Day 8, subsampled to 4,000 cells across 17 marker genes: "The optimized DKL ∼ 0.3 bits for the measured and simulated distributions, compared to an Hmeas ∼ 10.5 bits."

That the same pipeline clears both a clean flow-cytometry modality and "the noisy, high-dimensional data typical of RNA-seq" is the applicability claim: a KL divergence of ~0.3 bits against ~10.5–11 bits of measured entropy means the fitted landscape reproduces the observed distributions to a small fraction of their total spread. The mechanism producing these fits — a Fokker–Planck operator discretized on sampled points — is in claim-cislo-siggia-2025-landscape-from-discretized-fokker-planck-on-sampled-data-points.