保护隐私的联邦张量分解，跨机构恢复免疫细胞协同调控程序。
Privacy-preserving federated tensor decomposition of single-cell immune data: recovering multicellular programs across institutions
- 各机构本地计算程序子空间，协调者通过堆叠SVD融合结果。
- 在261例红斑狼疮数据中准确恢复干扰素通路，跨机构相关性达0.989。
- 无需共享原始细胞数据，适合多机构、多族裔免疫研究。
对捐赠者×细胞类型×基因的单细胞数据进行张量分解，可揭示跨细胞类型的协同转录变异模式（即多细胞程序），并用于疾病分层。然而，免疫单细胞图谱日益呈现多机构、多族裔特征，且受监管限制，患者细胞难以集中。本文提出一种联邦估计器：各站点本地计算程序子空间，协调者通过联邦全局均值中心化的堆叠SVD合并，理论上等价于集中式分解（仅截断误差）。该中心化策略有效抵抗站点标签混淆（程序AUC从0.861提升至0.957）。仅子空间参与传输，聚合兼容安全聚合。在包含261名系统性红斑狼疮患者的图谱中，成功恢复经典干扰素程序（ISG富集AUC 0.998；病例对照分离度0.958；自举ΔAUC=-0.000，95% CI [-0.004, +0.012] vs. 集中式）；跨机构与多族裔划分下依然有效，且在三个真实新冠站点间子空间相关性达0.989。即使无站点观测全部细胞类型（相关性1.000，由构造保证），仍可恢复，而固定特征联邦PCA无法实现。在间质性肺病图谱中，恢复程序预测能力优于最优单细胞类型（AUC 0.96 vs. 0.91；95% CI 差异不包含零），优势在联邦下持续存在；肝脏队列验证一致（p=0.005）。成员推断攻击显示，安全聚合将攻击AUC从0.91降至0.61。该方法可在不共享细胞的前提下，实现跨机构、跨族裔的多细胞免疫程序恢复。
原文摘要 · Abstract (English)
Tensor decomposition of donor $\times$ cell-type $\times$ gene single-cell data recovers \emph{multicellular programs}: coordinated axes of inter-individual transcriptional variation that span cell types and stratify disease. Yet immune single-cell atlases are increasingly multi-institution, multi-ancestry, and governed, so patient cells often cannot be pooled. We present a federated estimator: each site computes a local program subspace, and a coordinator merges these by stacked SVD under federated global-mean centering, provably equivalent (up to truncation) to the centralised decomposition. This centering makes the merge robust to site-label confounding (program AUC $0.957$ vs.\ $0.861$ for naive per-site centering). Only program subspaces leave a site, and aggregation is compatible with secure aggregation. On a 261-donor systemic lupus erythematosus atlas it recovers the canonical interferon program (ISG enrichment AUC $0.998$; case--control separation $0.958$; bootstrap $Δ\text{AUC}=-0.000$, 95\% CI $[-0.004,+0.012]$ vs.\ centralised), across institution-scale and multi-ancestry partitions, and across three \emph{real} COVID-19 sites (subspace correlation $0.989$). It recovers the program when \emph{no site observes all cell types} (correlation $1.000$, exact by construction), which fixed-feature federated PCA cannot. On an interstitial-lung-disease atlas the recovered program predicts disease better than the best single cell type (AUC $0.96$ vs.\ $0.91$; gap 95\% CI excludes zero) and the advantage survives federation; a liver cohort is consistent ($p=0.005$). Membership-inference shows secure aggregation cuts attack AUC from $0.91$ to $0.61$. The method enables cross-institution, cross-ancestry recovery of multicellular immune programs without sharing cells.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。