Abstract: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 $\Delta\text{AUC}=-0.000$, 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.
From: Axel Faes [view email]
[v1]
Mon, 22 Jun 2026 18:15:05 UTC (65 KB)
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