Reconstructing developmental and disease progression with sample-level embeddings

Abstract Single-cell genomics is transformative for characterizing cellular heterogeneity, but many translational questions require comparing samples rather than individual cells. Typical analyses compare “case” and “control” groups, ignoring sample-level variation within them. Here we present scSLIDE, a framework that transforms each sample’s single-cell data into a compact profile describing where its cells fall in high-dimensional space. By comparing these density profiles, scSLIDE calculates embeddings to characterization variation across samples. Applied to COVID-19 infection, Alzheimer’s disease, and zebrafish embryogenesis, we show that scSLIDE can be used to cluster patients, reconstruct sample-level disease trajectories, and identify coordinated cellular programs across samples. We discover independent axes of infection and severity, a molecular disease progression that aligns with pathology-based estimates of neurodegeneration, and embryonic “pseudostages” varying across and within timepoints. In each case, we demonstrate how case-control analyses collapse rich biological variation into binary labels, and how sample-level embedding represents a powerful analytical alternative. Competing Interest Statement In the past 3 years, R.S. has received compensation from Parse Biosciences, ImmunAI, Nanostring, 10x Genomics, Neptune Bio, and the NYC Pandemic Response Lab. R.S. and Y.H. are co-founders and equity holders of Neptune Bio. Y.H. was an employee at Neptune Bio from August 2023 to July 2025. The other authors declare that they have no competing interests.