Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Stoeckius, M. et al. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Jerby-Arnon, L. & Regev, A. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat. Biotechnol. 40, 1467–1477 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ramirez Flores, R. O., Lanzer, J. D., Dimitrov, D., Velten, B. & Saez-Rodriguez, J. Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease. eLife 12, e93161 (2023).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Tucker, L. R. Some mathematical notes on three-mode factor analysis. Psychometrika 31, 279–311 (1966).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 9, 559 (2008).ArticleÂ
Google ScholarÂ
Targ, S. Multiplexing droplet-based single cell RNA-sequencing using genetic barcodes. Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE96583 (2017).Perez, R. K. et al. Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus. Science 376, eabf1970 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Perez, R. K. et al. Multiplexed scRNA-seq reveals the cellular and genetic correlates of systemic lupus erythematosus. Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174188 (2021).Nehar-Belaid, D. et al. Mapping systemic lupus erythematosus heterogeneity at the single-cell level. Nat. Immunol. 21, 1094–1106 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Nehar-Belaid, D., Flynn, W. F., Banchereau, J., Pascual, V. & Robson, P. A single cell approach to map cellular subsets involved in systemic lupus erythematosus (SLE) heterogeneity. Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135779 (2020).Hooks, J. J. et al. Immune interferon in the circulation of patients with autoimmune disease. New Engl. J. Med. 301, 5–8 (1979).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Bennett, L. et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J. Exp. Med. 197, 711–723 (2003).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Kirou, K. A. et al. Activation of the interferon-α pathway identifies a subgroup of systemic lupus erythematosus patients with distinct serologic features and active disease. Arthritis Rheum. 52, 1491–1503 (2005).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Nikpour, M., Dempsey, A. A., Urowitz, M. B., Gladman, D. D. & Barnes, D. A. Association of a gene expression profile from whole blood with disease activity in systemic lupus erythaematosus. Ann. Rheum. Dis. 67, 1069–1075 (2008).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Weckerle, C. E. et al. Network analysis of associations between serum interferon α activity, autoantibodies, and clinical features in systemic lupus erythematosus. Arthritis Rheum. 63, 1044–1053 (2011).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Baechler, E. C. et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc. Natl Acad. Sci. USA 100, 2610–2615 (2003).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Crow, M. K., Kirou, K. A. & Wohlgemuth, J. Microarray analysis of interferon-regulated genes in SLE. Autoimmunity 36, 481–490 (2003).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Feng, X. et al. Association of increased interferon-inducible gene expression with disease activity and lupus nephritis in patients with systemic lupus erythematosus. Arthritis Rheum. 54, 2951–2962 (2006).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Iwata, Y. et al. p38 mitogen-activated protein kinase contributes to autoimmune renal injury in MRL-Faslpr mice. J. Am. Soc. Nephrol. 14, 57–67 (2003).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Jin, N. et al. The selective p38 mitogen-activated protein kinase inhibitor, SB203580, improves renal disease in MRL/lpr mouse model of systemic lupus. Int. Immunopharmacol. 11, 1319–1326 (2011).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Azodi, C. B., Zappia, L., Oshlack, A. & McCarthy, D. J. splatPop: simulating population scale single-cell RNA sequencing data. Genome Biol. 22, 341 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Klumpe, H. et al. The context-dependent, combinatorial logic of BMP signaling. Cell Syst. 13, 388–407 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Dodeller, F. & Schulze-Koops, H. The p38 mitogen-activated protein kinase signaling cascade in CD4 T cells. Arthritis Res. Ther. 8, 205 (2006).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wikenheiser, D. J. & Stumhofer, J. S. ICOS co-stimulation: friend or foe?. Front. Immunol. 7, 304 (2016).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Katan, M. B. Apolipoprotein E isoforms, serum cholesterol and cancer. Lancet 1, 507–508 (1986).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Burgess, S., Small, D. S. & Thompson, S. G. A review of instrumental variable estimators for Mendelian randomization. Stat. Methods Med. Res. 26, 2333–2355 (2017).ArticleÂ
PubMedÂ
Google ScholarÂ
Fox, J., Kleiber, C., Zeileis, A. & Kuschnig, N. ivreg: instrumental-variables regression by ‘2SLS’, ‘2SM’, or ‘2SMM’, with diagnostics. The Comprehensive R Archive Network https://zeileis.github.io/ivreg/ (2023).Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414 (2016).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Quach, H. et al. Genetic adaptation and neandertal admixture shaped the immune system of human populations. Cell 167, 643–656 (2016).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Chun, S. et al. Limited statistical evidence for shared genetic effects of eQTLs and autoimmune disease-associated loci in three major immune cell types. Nat. Genet. 49, 600–605 (2017).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Stephenson, E. et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat. Med. 27, 904–916 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cruikshank, W. W., Berman, J. S., Theodore, A. C., Bernardo, J. & Center, D. M. Lymphokine activation of T4+ T lymphocytes and monocytes. J. Immunol. 138, 3817–3823 (1987).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Winkler, M. S. et al. Human leucocyte antigen (HLA-DR) gene expression is reduced in sepsis and correlates with impaired TNFα response: a diagnostic tool for immunosuppression? PLoS ONE 12, e0182427 (2017).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Olwal, C. O. et al. Parallels in sepsis and COVID-19 conditions: implications for managing severe COVID-19. Front. Immunol. 12, 91 (2021).ArticleÂ
Google ScholarÂ
Giamarellos-Bourboulis, E. J. et al. Complex immune dysregulation in COVID-19 patients with severe respiratory failure. Cell Host Microbe 27, 992–1000 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Spinetti, T. et al. Reduced monocytic human leukocyte antigen-DR expression indicates immunosuppression in critically ill COVID-19 patients. Anesth. Analg. 131, 993–999 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
van der Wijst, M. G. P. et al. Type I interferon autoantibodies are associated with systemic immune alterations in patients with COVID-19. Sci. Transl. Med. 13, eabh2624 (2021).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Barkas, N., Petukhov, V., Kharchenko, P. V. & Biederstedt, E. pagoda2: single cell analysis and differential expression. The Comprehensive R Archive Network https://github.com/kharchenkolab/pagoda2 (2021).Li, J., Bien, J. & Wells, M. T. rTensor: an R package for multidimensional array (tensor) unfolding, multiplication, and decomposition. J. Stat. Softw. 87, 1–31 (2018).ArticleÂ
CASÂ
Google ScholarÂ
Sheehan, B. N. & Saad, Y. Higher order orthogonal iteration of tensors (HOOI) and its relation to PCA and GLRAM. In Proceedings of the 2007 SIAM International Conference on Data Mining (eds Apte, C., Liu, B., Parthasarathy, S. & Skillicorn, D) (Society for Industrial and Applied Mathematics, 2007).Kolda, T. G. & Bader, B. W. Tensor decompositions and applications. SIAM Rev. 51, 455–500 (2009).ArticleÂ
Google ScholarÂ
Unkel, S., Hannachi, A., Trendafilov, N. T. & Jolliffe, I. T. Independent component analysis for three-way data with an application from atmospheric. J. Agric. Biol. Environ. Stat. 16, 319–338 (2011).ArticleÂ
Google ScholarÂ
Zhou, G. & Cichocki, A. Fast and unique tucker decompositions via multiway blind source separation. Bull. Pol. Acad. Sci. Tech. Sci. 60, 389–405 (2012).
Google ScholarÂ
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).ArticleÂ
PubMedÂ
Google ScholarÂ
Badea, L. Extracting gene expression profiles common to colon and pancreatic adenocarcinoma using simultaneous nonnegative matrix factorization. Pac. Symp. Biocomput. 2008, 267–278 (2008).
Google ScholarÂ
Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wu, Y. et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9, 918 (2018).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Mitchel, J., Biederstedt, E. & Kharchenko, P. V. Single-cell analysis of inter-individual variability by interpretable tensor decomposition. GitHub https://github.com/kharchenkolab/scITD (2024).