From Cluster to Claim: Calibrating Interpretation in Single-Cell Transcriptomics

The widespread adoption of single-cell transcriptomics has expanded our ability to study cellular heterogeneity and molecular states. However, the high-resolution view it provides can also make it easier for descriptive data patterns to be translated into biological claims that go beyond the underlying evidence. We advocate a more calibrated approach to interpretation in single-cell transcriptomic studies. We focus on two common points of vulnerability: the direct equation of computational clusters with biological cell types and the treatment of pathway enrichment as sufficient evidence for mechanistic conclusions. These examples are offered as illustrations rather than as a systematic survey of the field. We therefore propose a three-tier logical framework-Observation, Inference, and Claim-to clarify the boundaries between statistical results, biological interpretation, and mechanistic claims. Single-cell transcriptomics is powerful for hypothesis generation, state discovery, and heterogeneity profiling, but strong mechanistic claims still require orthogonal validation. As large language models (LLMs) are increasingly used in cell-type annotation and biological narrative generation, explicit calibration between evidence strength and claim strength becomes even more important.