scAnnotatR: framework to accurately classify cell types in single-cell RNA-sequencing data
Tóm tắt
Automatic cell type identification is essential to alleviate a key bottleneck in scRNA-seq data analysis. While most existing classification tools show good sensitivity and specificity, they often fail to adequately not-classify cells that are missing in the used reference. Additionally, many tools do not scale to the continuously increasing size of current scRNA-seq datasets. Therefore, additional tools are needed to solve these challenges. scAnnotatR is a novel R package that provides a complete framework to classify cells in scRNA-seq datasets using pre-trained classifiers. It supports both Seurat and Bioconductor’s SingleCellExperiment and is thereby compatible with the vast majority of R-based analysis workflows. scAnnotatR uses hierarchically organised SVMs to distinguish a specific cell type versus all others. It shows comparable or even superior accuracy, sensitivity and specificity compared to existing tools while being able to not-classify unknown cell types. Moreover, scAnnotatR is the only of the best performing tools able to process datasets containing more than 600,000 cells. scAnnotatR is freely available on GitHub (
https://github.com/grisslab/scAnnotatR
) and through Bioconductor (from version 3.14). It is consistently among the best performing tools in terms of classification accuracy while scaling to the largest datasets.
Tài liệu tham khảo
Zanini F, Berghuis BA, Jones RC, di Robilant BN, Nong RY, Norton J, et al. Northstar enables automatic classification of known and novel cell types from tumor samples. Cold Spring Harbor Lab. 2020;10:820928. https://doi.org/10.1101/820928.
Kiselev VY, Yiu A, Hemberg M. scmap: projection of single-cell RNA-seq data across data sets. Nat Methods. 2018;15:359–62.
Brbić M, Zitnik M, Wang S, Pisco AO, Altman RB, Darmanis S, et al. MARS: discovering novel cell types across heterogeneous single-cell experiments. Nat Methods. 2020;17:1200–6.
Shao X, Liao J, Lu X, Xue R, Ai N, Fan X. scCATCH: automatic annotation on cell types of clusters from single-cell RNA sequencing data. IScience. 2020;23:100882. https://doi.org/10.1016/j.isci.2020.100882.
Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20:163–72.
Atakan Ekiz H, Conley CJ, Zac Stephens W, O’Connell RM. CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments. BMC Bioinform. 2020;21:191.
Fu R, Gillen AE, Sheridan RM, Tian C, Daya M, Hao Y, et al. clustifyr: an R package for automated single-cell RNA sequencing cluster classification. F1000Res. 2020;9:223.
Hou R, Denisenko E, Forrest ARR. scMatch: a single-cell gene expression profile annotation tool using reference datasets. Bioinformatics. 2019;35:4688–95.
Domanskyi S, Szedlak A, Hawkins NT, Wang J, Paternostro G, Piermarocchi C. Polled digital cell sorter (p-DCS): automatic identification of hematological cell types from single cell RNA-sequencing clusters. BMC Bioinform. 2019;20:1–16.
Zhang AW, O’Flanagan C, Chavez EA, Lim JLP, Ceglia N, McPherson A, et al. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat Methods. 2019;16:1007–15.
Li C, Liu B, Kang B, Liu Z, Liu Y, Chen C, et al. SciBet as a portable and fast single cell type identifier. Nat Commun. 2020;11:1818.
Pliner HA, Shendure J, Trapnell C. Supervised classification enables rapid annotation of cell atlases. Nat Methods. 2019;16:983–6.
de Kanter JK, Lijnzaad P, Candelli T, Margaritis T, Holstege FCP. CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing. Nucleic Acids Res. 2019;47:e95.
Zhang Z, Luo D, Zhong X, Choi JH, Ma Y, Wang S, et al. SCINA: a semi-supervised subtyping algorithm of single cells and bulk samples. Genes. 2019;10:531. https://doi.org/10.3390/genes10070531.
Alquicira-Hernandez J, Sathe A, Ji HP, Nguyen Q, Powell JE. scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol. 2019;20:264.
Boufea K, Seth S, Batada NN. scID uses discriminant analysis to identify transcriptionally equivalent cell types across single-cell RNA-Seq data with batch effect. IScience. 2020;23:100914. https://doi.org/10.1016/j.isci.2020.100914.
Lin Y, Cao Y, Kim HJ, Salim A, Speed TP, Lin DM, et al. scClassify: sample size estimation and multiscale classification of cells using single and multiple reference. Mol Syst Biol. 2020;16:e9389.
Ma F, Pellegrini M. ACTINN: automated identification of cell types in single cell RNA sequencing. Bioinformatics. 2019;36:533–8.
Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:1–26.
Abdelaal T, Michielsen L, Cats D, Hoogduin D, Mei H, Reinders MJT, et al. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol. 2019;20:1–19.
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, et al. Comprehensive integration of single-cell data. Cell. 2019;177:1888–1902.e21.
Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN, Geistlinger L, et al. Orchestrating single-cell analysis with Bioconductor. Nat Methods. 2019;17:137–45.
Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, et al. A Single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 2016;3:346–360.e4. https://doi.org/10.1016/j.cels.2016.08.011.
Muraro MJ, Dharmadhikari G, Grün D, Groen N, Dielen T, Jansen E, et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 2016;3:385–394.e3. https://doi.org/10.1016/j.cels.2016.09.002.
Segerstolpe Å, Palasantza A, Eliasson P, Andersson E-M, Andréasson A-C, Sun X, et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 2016;24:593.
Wang YJ, Schug J, Won KJ, Liu C, Naji A, Avrahami D, et al. Single-cell transcriptomics of the human endocrine pancreas. Diabetes. 2016;65:3028–38. https://doi.org/10.2337/db16-0405.
Xin Y, Kim J, Okamoto H, Ni M, Wei Y, Adler C, et al. RNA sequencing of single human islet cells reveals type 2 diabetes genes. Cell Metab. 2016;24:608–15. https://doi.org/10.1016/j.cmet.2016.08.018.
Satija Lab. [Cited 23 Nov 2020]. Available: https://satijalab.org/seurat/v3.1/pbmc3k_tutorial.html.
Smolander J. ILoReg package manual. 27 Oct 2020 [cited 7 Dec 2020]. Available: https://bioconductor.org/packages/release/bioc/vignettes/ILoReg/inst/doc/ILoReg.html.
Ding J, Adiconis X, Simmons SK, Kowalczyk MS, Hession CC, Marjanovic ND, et al. Systematic comparative analysis of single cell RNA-sequencing methods. Cold Spring Harbor Lab. 2019;10:632216. https://doi.org/10.1101/632216.
Single Cell Portal. [Cited 1 Jul 2021]. Available: https://singlecell.broadinstitute.org/single_cell/study/SCP345/ica-blood-mononuclear-cells-2-donors-2-sites.