科研速览 · Science Skim继续刷下去 · Keep skimming →
◇ bioRxiv2026-09-16· bioinformatics

Estimand-aware and donor-aware triangulation of predefined gene-set signals across human tendon transcriptomic datasets

Y. Liu, Z. Wang, Q. Peng, Y. Li, B. Chen

原始摘要(英文原文)· Original abstract
We analysed five predefined programmes across three human tendon transcriptomic contexts: acute exercise-associated fibroblast pseudobulks (E-MTAB-15400; 4 control and 4 exercise samples), mechanically stretched donor-derived cells (GSE150482; 3 normal and 3 tendinopathy donors), and paired lesional versus grossly normal-appearing tendon (GSE26051; 23 donor pairs). Inference was conducted at the sample, donor or donor-pair level. Non-stimulated GSE150482 single-cell RNA sequencing comprised only N1 and D1, whereas stretched single-cell RNA sequencing comprised N1-N3 and D1-D3; the conditions also used Drop-seq and 10X Chromium, respectively. A donor-replicated disease-by-stimulation interaction is therefore not estimable. Within-context Benjamini-Hochberg adjustment supported acute mechanical-response, extracellular-matrix (ECM) and protein-folding differences and paired-lesion collagen, integrin and ECM differences, but no stretched donor-background difference. Exact label permutations, a conservative global 15-test adjustment, leave-one-unit analyses, a Fib1-Fib4 pooled sensitivity and a paired exact Wilcoxon sensitivity qualified these findings. Integrin was fully nested in ECM, while collagen overlapped 52/79 genes with ECM. Collagen fibril organization showed the most directionally consistent matrix-associated pattern across the three non-equivalent contrasts, without establishing a disease-specific loading response or causal mechanism. The analysis demonstrates why public tendon datasets must be integrated by estimand and biological inference unit rather than pooled as interchangeable replications.
读原文 · Read the paper ↗

AI 追问PRO

登录后使用 AI 追问

讨论区

登录后参与讨论

相关论文 · Related

Estimand-aware and donor-aware triangulation of predefined gene-set signals across human tendon transcriptomic datasets — 科研速览 Science Skim