What Single-Cell Foundation Models Uniquely Offer: A Review of Their Comparative Advantages in Transcriptomic Representation Learning

Abstract

Single-cell foundation models (scFMs)-transformer networks pretrained by self-supervision on tens of millions of single-cell transcriptomes-have moved rapidly from proof of concept to a central methodological theme in computational biology. Yet much of the literature evaluates them on the same downstream tasks (cell-type annotation, batch integration, perturbation prediction) where strong, inexpensive classical baselines already exist, and on several of these tasks the foundation-model advantage is modest or contested. This review takes a different framing: rather than asking whether scFMs win every benchmark, we ask what they offer that task-specific and classical methods structurally cannot. We identify and analyze six comparative advantages: (i) label-efficient transfer and zero-/few-shot inference from a single pretrained backbone; (ii) atlas-scale generalization and reference-free integration across datasets, tissues, and technologies; (iii) a unified multi-task, multi-omic interface that amortizes engineering and modeling effort; (iv) context-dependent, attention-derived gene and cell embeddings that enable network inference and in silico perturbation; (v) predictable scaling behavior with data, parameters, and compute; and (vi) cross-species and cross-modality knowledge transfer, including the interplay between what protein language models already encode and what genuinely requires single-cell pretraining. For each advantage we summarize the supporting evidence, the limits exposed by recent benchmarks and linear-baseline critiques, and the open questions. We conclude that the durable value proposition of scFMs is reusability and breadth-a single artifact that transfers across problems-rather than uniform state-of-the-art accuracy, and we outline what would strengthen the case for that proposition.