Context-dependent correlations mislead transcriptomic network inference in bulk and single-cell data

作者：Amir Asiaee, Polina Bombina, Reginald L. McGee, Jake Reed, Zachary B. Abrams, Lynne V. Abruzzo, Kevin R. Coombes · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2026 · DOI：10.64898/2026.06.23.733936 · 研究领域：Single-cell and spatial transcriptomics、Ferroptosis and cancer prognosis、MicroRNA in disease regulation

Abstract Background Correlation is the dominant input to co-expression module discovery and miRNA-target inference. Both rely on an implicit assumption: a Pearson coefficient pooled across heterogeneous samples, whether tissues, cancer types, or cell types, estimates one biologically meaningful quantity. Simpson’s paradox makes this assumption fragile in principle, since between- group mean shifts can dominate or reverse within-group associations. How often this happens in real transcriptomic data has not been quantified. Results Across 8,890 TCGA tumors from 31 cancer cohorts and 23,170,038 miRNA–mRNA pairs, 94.8% of pairs showed both positive and negative within-cohort correlations. Restricting to the high-variance domain of one million pairs, 13.3% of pooled correlations with | r global |≥0.2 reversed against the within-cohort majority at sign tolerance ε = 0.05. Heterogeneity was the rule rather than the exception (median I 2 = 0.86, IQR 0.80–0.90), and 99.5% of pairs rejected equal correlation across cohorts at FDR < 0.05. Of 692,770 experimentally validated miRTarBase v10 targets measurable in our data, only 0.9% were uniformly negative across cohorts. The pattern recurred across modalities. In GTEx, 21.0% of pooled signs disagreed with the tissue majority, and 23.5% of pairs flipped sign after tissue-mean removal. In 10x PBMC scRNA-seq, 13.1% of gene–gene correlations flipped after cell-type-mean removal; in CITE-seq, 37.9% of protein–RNA pairs flipped under a joint...