Revealing a coherent cell state landscape across single cell datasets with CONCORD.

Batch integration, denoising, and dimensionality reduction remain fundamental challenges in single-cell data analysis. While many machine learning tools aim to overcome these challenges by engineering model architectures, we use a different strategy, building on the insight that optimized mini-batch sampling during training can profoundly influence learning outcomes. We present CONCORD, a self-supervised learning approach that implements a unified, probabilistic data sampling scheme combining neighborhood-aware and dataset-aware sampling: the former enhancing resolution while the latter removing batch effects. Using only a minimalist one-hidden-layer neural network and contrastive learning, CONCORD achieves state-of-the-art performance without relying on deep architectures, auxiliary losses, or supervision. It generates high-resolution cell atlases that seamlessly integrate data across batches, technologies, and species, without relying on prior assumptions about data structure. The resulting latent representations are denoised, interpretable, and biologically meaningful-capturing gene co-expression programs, resolving subtle cellular states, and preserving both local geometric relationships and global topological organization. We demonstrate CONCORD's broad applicability across diverse datasets, establishing it as a general-purpose framework for learning unified, high-fidelity representations of cellular identity and dynamics.