Demaria O, Cornen S, Daëron M, Morel Y, Medzhitov R, Vivier E. Harnessing innate immunity in cancer therapy. Nature. 2019;574:45–56.
Article
CAS
PubMed
Google Scholar
Zheng C, Zheng L, Yoo JK, Guo H, Zhang Y, Guo X, et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell. 2017;169:1342–56.
Article
CAS
PubMed
Google Scholar
Shen-Orr SS, Tibshirani R, Khatri P, Bodian DL, Staedtler F, Perry NM, et al. Cell type–specific gene expression differences in complex tissues. Nat Methods. 2010;7(4):287–9.
Article
CAS
PubMed
PubMed Central
Google Scholar
Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37:773–82.
Article
CAS
PubMed
PubMed Central
Google Scholar
Kang K, Meng Q, Shats I, Umbach DM, Li M, Li Y, et al. CDSeq: a novel complete deconvolution method for dissecting heterogeneous samples using gene expression data. PLoS Comput Biol. 2019;15:e1007510.
Article
PubMed
PubMed Central
Google Scholar
Zaitsev K, Bambouskova M, Swain A, Artyomov MN. Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures. Nat Commun. 2019;10:1–16.
Article
CAS
Google Scholar
Shen-Orr SS, Gaujoux R. Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr Opin Immunol. 2013;25(5):571–8.
Article
CAS
PubMed
Google Scholar
Erkkilä T, Lehmusvaara S, Ruusuvuori P, Visakorpi T, Shmulevich I, Lähdesmäki H. Probabilistic analysis of gene expression measurements from heterogeneous tissues. Bioinformatics. 2010;26:2571–7.
Article
PubMed
PubMed Central
Google Scholar
Qiao W, Quon G, Csaszar E, Yu M, Morris Q, Zandstra PW. PERT: a method for expression deconvolution of human blood samples from varied microenvironmental and developmental conditions. PLoS Comput Biol. 2012;8(12):e1002838.
Article
CAS
PubMed
PubMed Central
Google Scholar
Gong T, Szustakowski JD. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics. 2013;29(8):1083–5.
Article
CAS
PubMed
Google Scholar
Zhong Y, Wan YW, Pang K, Chow LML, Liu Z. Digital sorting of complex tissues for cell type-specific gene expression profiles. BMC Bioinform. 2013;14:1–10.
Google Scholar
Gaujoux R, Seoighe C. Semi-supervised nonnegative matrix factorization for gene expression deconvolution: a case study. Infect Genet Evol. 2012;12(5):913–21.
Article
CAS
PubMed
Google Scholar
Li Y, Xie X. A mixture model for expression deconvolution from RNA-seq in heterogeneous tissues. BMC Bioinform. 2013;14(5):S11.
Google Scholar
Ahn J, Yuan Y, Parmigiani G, Suraokar MB, Diao L, Wistuba II, et al. DeMix: deconvolution for mixed cancer transcriptomes using raw measured data. Bioinformatics. 2013;29:1865–71.
Article
CAS
PubMed
PubMed Central
Google Scholar
Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453–7.
Article
CAS
PubMed
PubMed Central
Google Scholar
Wang X, Park J, Susztak K, Zhang NR, Li M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat Commun. 2019;10:1–9.
Article
Google Scholar
Avila Cobos F, Alquicira-Hernandez J, Powell JE, Mestdagh P, De Preter K. Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun. 2020;11:1–14.
Article
Google Scholar
Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:1–12.
Article
Google Scholar
Puram SV, Tirosh I, Parikh AS, Patel AP, Yizhak K, Gillespie S, et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell. 2017;171:1611–24.
Article
CAS
PubMed
PubMed Central
Google Scholar
Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature. 2019;570:332–7.
Article
CAS
PubMed
PubMed Central
Google Scholar
Griffiths TL, Steyvers M. Finding scientific topics. Proc Natl Acad Sci. 2004;101:5228–35.
Article
CAS
PubMed
PubMed Central
Google Scholar
Pastushenko I, Brisebarre A, Sifrim A, Fioramonti M, Revenco T, Boumahdi S, et al. Identification of the tumour transition states occurring during EMT. Nature. 2018;556:463–8.
Article
CAS
PubMed
Google Scholar
Han X, Zhou Z, Fei L, Sun H, Wang R, Chen Y, et al. Construction of a human cell landscape at single-cell level. Nature. 2020;581:303–9.
Article
CAS
PubMed
Google Scholar
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36:411–20.
Article
CAS
PubMed
PubMed Central
Google Scholar
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, et al. Comprehensive integration of single-cell data. Cell. 2019;177:1888–902.
Article
CAS
PubMed
PubMed Central
Google Scholar
Chambers JM. Linear models. In: Statistical Models in S. Routledge; 2017. p. 95–144.
Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453.
Article
CAS
PubMed
PubMed Central
Google Scholar
Du R, Carey V, Weiss ST. DeconvSeq: deconvolution of cell mixture distribution in sequencing data. Bioinformatics. 2019;35:5095–102.
Article
CAS
PubMed
Google Scholar
Hao Y, Yan M, Heath BR, Lei YL, Xie Y. Fast and robust deconvolution of tumor infiltrating lymphocyte from expression profiles using least trimmed squares. PLoS Comput Biol. 2019;15:e1006976.
Article
CAS
PubMed
PubMed Central
Google Scholar
Riplley B, Venables B, Bates DM, Firth D, Hornik K, Gebhardt A. Package “MASS”. support functions and datasets for Venables and Ripley’s MASS. 2018. Document freely available on the internet at: http://www.r-project.org. Accessed 12 Dec 2020.
Altboum Z, Steuerman Y, David E, Barnett-Itzhaki Z, Valadarsky L, Keren-Shaul H, et al. Digital cell quantification identifies global immune cell dynamics during influenza infection. Mol Syst Biol. 2014;10:720.
Article
PubMed
PubMed Central
Google Scholar
Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1.
Article
PubMed
PubMed Central
Google Scholar
Jew B, Alvarez M, Rahmani E, Miao Z, Ko A, Garske KM, et al. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat Commun. 2020;11:1–11.
Google Scholar
Dong M, Thennavan A, Urrutia E, Li Y, Perou CM, Zou F, et al. SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references. Brief Bioinform. 2021;22:416–27.
Article
PubMed
Google Scholar
Aguet F, Barbeira AN, Bonazzola R, Brown A, Castel SE, Jo B, et al. The GTEx consortium atlas of genetic regulatory effects across human tissues. Science (80- ). 2020;369:1318–30.
Article
CAS
Google Scholar
Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol. 2015;19(1A):A68
Gandal MJ, Zhang P, Hadjimichael E, Walker RL, Chen C, Liu S, et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science. 2018;362(6420):eaat8127.
Trapnell C. Defining cell types and states with single-cell genomics. Genome Res. 2015;25(10):1491–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Hunt GJ, Freytag S, Bahlo M, Gagnon-Bartsch JA. Dtangle: accurate and robust cell type deconvolution. Bioinformatics. 2019;35:2093–9.
Article
CAS
PubMed
Google Scholar
Ding J, Adiconis X, Simmons SK, Kowalczyk MS, Hession CC, Marjanovic ND, et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol. 2020;38:737–46.
Article
CAS
PubMed
PubMed Central
Google Scholar
McKenzie AT, Wang M, Hauberg ME, Fullard JF, Kozlenkov A, Keenan A, et al. Brain cell type specific gene expression and co-expression network architectures. Sci Rep. 2018;8:1–19.
Google Scholar