Statistical Visibility of Curated TF-Target Regulatory Relationships and Reverse Consistency of Top-Ranked TF-Gene Pairs in Single-Cell Expression Data.

BACKGROUND/OBJECTIVES: Large language models and automated analytical tools show potential for biomedical text understanding, knowledge integration, and single-cell data interpretation, but interpretations of specific gene regulatory relationships still require empirical grounding. For TF-target relationships, one relevant constraint is whether curated regulatory edges show detectable expression-level statistical evidence in the single-cell data being interpreted, because such evidence may vary across cellular states, conditions, and regulatory mechanisms. We therefore evaluated the statistical visibility of known transcription factor (TF)-target relationships in single-cell expression space to aid the interpretation of regulatory inference and automated-tool outputs. METHODS: We performed two complementary analyses: first, testing whether curated TF-target edges showed stronger pair-level associations than matched background gene pairs; and second, assessing whether high-scoring TF-gene pairs corresponded to existing regulatory knowledge and whether their target genes showed pathway coherence. RESULTS: Across four peripheral blood mononuclear cell (PBMC) datasets, four adult tissues, and seven adult cell types, curated TF-target relationships showed weak but reproducible statistical visibility rather than strong separation. For all five association metrics, the mean area under the precision-recall curve (AUPRC) was only slightly above the random-ranking baseline of 0.5. High-scoring pairs were more often supported by curated resources, and their target genes showed context-specific Hallmark pathway coherence. CONCLUSIONS: Single-cell expression associations can provide useful but limited statistical clues for TF-target regulation and should be interpreted as complementary rather than definitive regulatory evidence.