2025/03/15 by Qin Zhu, Zuzhi Jiang, Binyamin Zuckerman +3 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Single-cell and spatial transcriptomics #Cell Image Analysis Techniques #Advanced Fluorescence Microscopy Techniques
paper · pdf · doi:10.1101/2025.03.13.643146
openalex publication_date 2025/03/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Revealing the underlying cell-state landscape from single-cell data requires overcoming the critical obstacles of batch integration, denoising, and dimensionality reduction. Here, we present CONCORD, a unified framework that simultaneously addresses these challenges within a single self-supervised model. At its core, CONCORD implements a unified probabilistic sampling strategy that corrects batch effects via dataset-aware sampling and enhances biological resolution through hard-negative sampling. Remarkably, using only a minimalist neural network with a single hidden layer and contrastive learning, CONCORD surpasses state-of-the-art performance without relying on deep architectures, auxiliary losses, or external supervision. It seamlessly integrates data across batches, technologies, and even species to generate high-resolution cell atlases. The resulting latent representations are denoised and biologically meaningful-capturing gene co-expression programs, revealing detailed lineage trajectories, and preserving both local geometric relationships and global topological structures. 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.