CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data

Genome Biology

Volumn 18, Issue 1, 2017, Pages

  Lin, Peijie ^a,b   Troup, Michael ^a   Ho, Joshua W K ^a,b  

^a VICTOR CHANG CARDIAC RESEARCH INSTITUTE   (Australia)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Cell type; Clustering; Dimensionality reduction; Dropout; Imputation; ScRNA seq; Single cell

Indexed keywords

RNA;

ALGORITHM; ARTICLE; CLUSTER ANALYSIS; CLUSTERING THROUGH IMPUTATION AND DIMENSIONALITY REDUCTION ALGORITHM; CONTROLLED STUDY; DATA SYNTHESIS; GENE CLUSTER; GENE EXPRESSION; MATHEMATICAL COMPUTING; MULTIFACTOR DIMENSIONALITY REDUCTION; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; RNA SEQUENCE; SIMULATION; SINGLE CELL ANALYSIS; ANTIBODY SPECIFICITY; BIOLOGY; BRAIN; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENETICS; GENOMICS; HUMAN; INFORMATION PROCESSING; METABOLISM; PROCEDURES; SEQUENCE ANALYSIS; SOFTWARE;

ALGORITHMS; BRAIN; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATASETS AS TOPIC; GENE EXPRESSION PROFILING; GENOMICS; HUMANS; ORGAN SPECIFICITY; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; SOFTWARE;

EID: 85016502564     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-017-1188-0     Document Type: Article

References (26)

1. Pierson E, Yau C. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 2015; 16(1):1-10.
2. Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014; 11(7):740-2.
3. Scialdone A, Natarajan KN, Saraiva LR, Proserpio V, Teichmann SA, Stegle O, et al.Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods. 2015; 85:54-61.
4. Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, et al.Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol. 2015; 33(2):155-60.
5. Lun AT, Bach K, Marioni JC. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 2016; 17(1):1.
6. Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, et al.MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 2015; 16(1):1-13.
7. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al.The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014; 32(4):381-6.
8. van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008; 9(Nov):2579-605.
9. Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G, Juréus A, et al.Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-Seq. Science. 2015; 347(6226):1138-42.
10. Zurauskiene J, Yau C. pcaReduce: Hierarchical clustering of single cell transcriptional profiles. BMC Bioinform. 2016; 17(1):140.
11. Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, et al.SC3-consensus clustering of single-cell RNA-Seq data. bioRxiv. 2016:036558.
12. Xu C, Su Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics. 2015; 31:1974-80.
13. Grün D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N, et al.Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature. 2015; 525(7568):251-5.
14. Prabhakaran S, Azizi E, Pe'er D. Dirichlet process mixture model for correcting technical variation in single-cell gene expression data. In: Proceedings of the 33rd International Conference on Machine Learning: 2016. p. 1070-9.
15. McDavid A, Dennis L, Danaher P, Finak G, Krouse M, Wang A, et al.Modeling bi-modality improves characterization of cell cycle on gene expression in single cells. PLoS Comput Biol. 2014; 10(7):1003696.
16. Bacher R, Kendziorski C. Design and computational analysis of single-cell RNA sequencing experiments. Genome Biol. 2016; 17(1):1.
17. Ronan T, Qi Z, Naegle KM. Avoiding common pitfalls when clustering biological data. Sci Signal. 2016; 9(432):6.
18. Caliński T, Harabasz J. A dendrite method for cluster analysis. Commun Stat. 1974; 3(1):1-27.
19. Hubert L, Arabie P. Comparing partitions. J Classif. 1985; 2(1):193-218.
20. Darmanis S, Sloan SA, Zhang Y, Enge M, Caneda C, Shuer LM, et al.A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci. 2015; 112(23):7285-90.
21. Cahoy JD, Emery B, Kaushal A, Foo LC, Zamanian JL, Christopherson KS, et al.A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J Neurosci. 2008; 28(1):264-78.
22. Li J, Klughammer J, Farlik M, Penz T, Spittler A, Barbieux C, et al.Single-cell transcriptomes reveal characteristic features of human pancreatic islet cell types. EMBO Rep. 2016; 17(2):178-87.
23. Trapnell C. Defining cell types and states with single-cell genomics. Genome Res. 2015; 25(10):1491-8.
24. Cailliez F. The analytical solution of the additive constant problem. Psychometrika. 1983; 48(2):305-8.
25. Cattell RB. The scree test for the number of factors. Multivar Behav Res. 1966; 1(2):245-76.
26. Murtagh F, Legendre P. Ward's hierarchical agglomerative clustering method: which algorithms implement Ward's criterion?J Classif. 2014; 31(3):274-95.