Coordinated, multicellular patterns of transcriptional variation that stratify patient cohorts are revealed by tensor decomposition

Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).Article 
CAS 
PubMed 

Google Scholar 
Stoeckius, M. et al. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jerby-Arnon, L. & Regev, A. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat. Biotechnol. 40, 1467–1477 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
PubMed 
PubMed Central 

Google Scholar 
Tucker, L. R. Some mathematical notes on three-mode factor analysis. Psychometrika 31, 279–311 (1966).Article 
CAS 
PubMed 

Google Scholar 
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 9, 559 (2008).Article 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
Bennett, L. et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J. Exp. Med. 197, 711–723 (2003).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Crow, M. K., Kirou, K. A. & Wohlgemuth, J. Microarray analysis of interferon-regulated genes in SLE. Autoimmunity 36, 481–490 (2003).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
Azodi, C. B., Zappia, L., Oshlack, A. & McCarthy, D. J. splatPop: simulating population scale single-cell RNA sequencing data. Genome Biol. 22, 341 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Klumpe, H. et al. The context-dependent, combinatorial logic of BMP signaling. Cell Syst. 13, 388–407 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).Article 
CAS 
PubMed 

Google Scholar 
Dodeller, F. & Schulze-Koops, H. The p38 mitogen-activated protein kinase signaling cascade in CD4 T cells. Arthritis Res. Ther. 8, 205 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
Wikenheiser, D. J. & Stumhofer, J. S. ICOS co-stimulation: friend or foe?. Front. Immunol. 7, 304 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Katan, M. B. Apolipoprotein E isoforms, serum cholesterol and cancer. Lancet 1, 507–508 (1986).Article 
CAS 
PubMed 

Google Scholar 
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
PubMed 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Quach, H. et al. Genetic adaptation and neandertal admixture shaped the immune system of human populations. Cell 167, 643–656 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stephenson, E. et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat. Med. 27, 904–916 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
PubMed 
PubMed Central 

Google Scholar 
Olwal, C. O. et al. Parallels in sepsis and COVID-19 conditions: implications for managing severe COVID-19. Front. Immunol. 12, 91 (2021).Article 

Google Scholar 
Giamarellos-Bourboulis, E. J. et al. Complex immune dysregulation in COVID-19 patients with severe respiratory failure. Cell Host Microbe 27, 992–1000 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
CAS 

Google Scholar 
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).Article 

Google Scholar 
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).Article 

Google Scholar 
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 Scholar 
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).Article 
PubMed 

Google Scholar 
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 Scholar 
Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, Y. et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9, 918 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
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).

Hot Topics

Related Articles