.png){kind=icon}
.png){kind=icon}
Return
scImmuneCo: a compendium of cell-type-specific functional modules for decoding immune responses from single-cell RNA-seq data
.png){kind=icon}
OA
AI
F
C
X
H
Y
Y
X
W
DOI：10.1093/bib/bbag366.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Traditional, knowledge-driven pathway annotations and bulk transcriptomic analyses often fail to capture the cellular specificity and mechanistic heterogeneity of immune responses. We present scImmuneCo, a comprehensive resource of immune cell-specific co-expression modules derived from single-cell RNA sequencing across 17 immunological conditions and 1.78 million cells. Using a modified graph-based framework, we constructed 873 robust modules spanning 7 major immune cell types, providing stable, cell-type-specific interaction networks for functional inference. scImmuneCo resolves complex biology at cellular resolution. We identify 20 interferon-related modules that reveal both conserved and cell-type-specific regulatory programs, clarifying disease-dependent differences that are invisible to pathway tools treating interferon signaling as a unitary process. We also uncover age-associated CD8+ T cell programs, capturing state transitions from naive to effector/memory cells and exposing a progressive imbalance in translation and cytotoxicity with age. Together, these results demonstrate the power of high-resolution, data-driven functional inference to link gene groups to biological roles and disease processes. To support broad application, we provide an R package (https://github.com/FrankQYW/scImmuneCo_R) for module-based analysis of both single-cell and bulk transcriptomic data, along with an interactive web portal (http://www.scimmuneco.site/) for visualization and gene-module exploration. scImmuneCo offers a scalable and interpretable framework for dissecting immune mechanisms and identifying disease-relevant transcriptional programs with cellular resolution.
Journal
IF:
7.7
Papers:
5.6K
Citations:
2.7W
Organization
Cited Papers