CellMSA: Context Modeling for Single-Cell Representation Learning

arXiv:2609.38908 · q-bio.GN, cs.AI, cs.LG · Submitted 2026-09-30 · Read on arXiv

q-bio.GN, cs.AI, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/PharMolix/CellMSA

Terminology

• Foundation Model — 16× in this paper · explained in 3 episodes
• Representation Learning — 13× in this paper · explained in 2 episodes
• Foundation Models — 10× in this paper · explained in 7 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Clustering — 9× in this paper · explained in 2 episodes
• Embeddings — 9× in this paper · explained in 2 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Downstream Tasks — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Accuracy — 6× in this paper · explained in 1 episode
• Consistency — 6× in this paper · explained in 3 episodes
• Interpretability — 6× in this paper · explained in 15 episodes
• Benchmarking — 5× in this paper · explained in 3 episodes
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Ontology — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• alpha — 3× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Gene Ontology — 2× in this paper
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Single-cell Integration Benchmarking — 2× in this paper
• Sparsity — 2× in this paper · explained in 4 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adjusted Rand Index — 1× in this paper
• Average Silhouette Width — 1× in this paper
• Batch-removal-adapted Silhouette — 1× in this paper
• External Validation — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Institute for AI Industry Research — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Normalized Mutual Information — 1× in this paper
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• LangCell: Language-Cell Pre-training for Cell Identity Understanding
• SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics
• Representation Learning with Contrastive Predictive Coding
• Ward's Hierarchical Clustering Method: Clustering Criterion and Agglomerative Algorithm
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Adam: A Method for Stochastic Optimization

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