Comparison of clustering tools in R for medium-sized 10x genomics single-cell RNA-sequencing data .

F1000Research

Volumn 7, Issue , 2018, Pages

  Freytag, Saskia ^a,b   Tian, Luyi ^a,b   Lönnstedt, Ingrid ^c   Ng, Milica ^c   Bahlo, Melanie ^a,b  

^a WALTER AND ELIZA HALL INSTITUTE OF MEDICAL RESEARCH   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

^c CSL LIMITED   (Australia)

Author keywords

10x Genomics; Benchmarking; Clustering; Single Cell RNA seq

Indexed keywords

ARTICLE; CELL ISOLATION; CLUSTER ANALYSIS; GENETIC DATABASE; GENOMICS; HUMAN; HUMAN CELL; INTERMETHOD COMPARISON; MEASUREMENT ACCURACY; RNA SEQUENCE; SEQUENCE ANALYSIS; SINGLE CELL RNA SEQUENCING; SOFTWARE; GENE EXPRESSION PROFILING; MONONUCLEAR CELL; SINGLE CELL ANALYSIS;

RNA;

GENE EXPRESSION PROFILING; GENOMICS; LEUKOCYTES, MONONUCLEAR; RNA; SINGLE-CELL ANALYSIS; SOFTWARE;

EID: 85059492831     PISSN: None     EISSN: 20461402     Source Type: Journal    
DOI: 10.12688/f1000research.15809.1     Document Type: Article

References (32)

1. Tanay A, Regev A: Scaling single-cell genomics from phenomenology to mechanism. Nature. 2017; 541(7637): 331–338. PubMed Abstract | Publisher Full Text | Free Full Text
2. Zappia L, Phipson B, Oshlack A: Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database. PLoS Comput Biol. 2018; 14(6): e1006245. PubMed Abstract | Publisher Full Text | Free Full Text
3. Ziegenhain C, Vieth B, Parekh S, et al.: Comparative Analysis of Single-Cell RNA Sequencing Methods. Mol Cell. 2017; 65(4): 631–643.e4. PubMed Abstract | Publisher Full Text
4. Haque A, Engel J, Teichmann SA, et al.: A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 2017; 9(1): 75. PubMed Abstract | Publisher Full Text | Free Full Text
5. Zheng GX, Terry JM, Belgrader P, et al.: Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017; 8: 14049. PubMed Abstract | Publisher Full Text | Free Full Text
6. Senabouth A, Lukowski S, Alquicira J, et al.: ascend: R package for analysis of single cell RNA-seq data. bioRxiv. 2017; 207704. Publisher Full Text
7. Lin P, Troup M, Ho JW: CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol. 2017; 18(1): 59. PubMed Abstract | Publisher Full Text | Free Full Text
8. Dey KK, Hsiao CJ, Stephens M: Visualizing the structure of RNA-seq expression data using grade of membership models. PLoS Genet. 2017; 13(3): e1006599. PubMed Abstract | Publisher Full Text | Free Full Text
9. Grün D, Lyubimova A, Kester L, et al.: Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature. 2015; 525(7568): 251–5. PubMed Abstract | Publisher Full Text
10. Grün D, Muraro MJ, Boisset JC, et al.: De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data. Cell Stem Cell. 2016; 19(2): 266–277. PubMed Abstract | Publisher Full Text | Free Full Text
11. Li H, Courtois ET, Sengupta D, et al.: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat Genet. 2017; 49(5): 708–718. PubMed Abstract | Publisher Full Text
12. Kiselev VY, Kirschner K, Schaub MT, et al.: SC3: consensus clustering of single-cell RNA-seq data. Nat Methods. 2017; 14(5): 483–486. PubMed Abstract | Publisher Full Text | Free Full Text
13. 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): 75. PubMed Abstract | Publisher Full Text | Free Full Text
14. Butler A, Hoffman P, Smibert P, et al.: Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018; 36(5): 411–420. PubMed Abstract | Publisher Full Text
15. Wang B, Ramazzotti D, De Sano L, et al.: SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning. Proteomics. 2018; 18(2): 1700232. PubMed Abstract | Publisher Full Text
16. Ji Z, Ji H: TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 2016; 44(13): e117. PubMed Abstract | Publisher Full Text | Free Full Text
17. McCarthy DJ, Campbell KR, Lun AT, et al.: Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics. 2017; 33(8): 1179–1186. PubMed Abstract | Publisher Full Text | Free Full Text
18. Holik AZ, Law CW, Liu R, et al.: RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods. Nucleic Acids Res. 2017; 45(5): e30. PubMed Abstract | Publisher Full Text | Free Full Text
19. Kang HM, Subramaniam M, Targ S, et al.: Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat Biotechnol. 2018; 36(1): 89–94. PubMed Abstract | Publisher Full Text | Free Full Text
20. bright regulatory T cells in pre-eclampsia. Clin Exp Immunol. 2007; 149(1): 139–145. PubMed Abstract | Publisher Full Text | Free Full Text
21. Jing Y, Gravenstein S, Chaganty NR, et al.: Aging is associated with a rapid decline in frequency, alterations in subset composition, and enhanced Th2 response in CD1d-restricted NKT cells from human peripheral blood. Exp Gerontol. 2007; 42(8): 719–732. PubMed Abstract | Publisher Full Text
22. Yip SH, Wang P, Kocher JA, et al.: Linnorm: improved statistical analysis for single cell RNA-seq expression data. Nucleic Acids Res. 2017; 45(22): e179. PubMed Abstract | Publisher Full Text | Free Full Text
23. Trapnell C, Cacchiarelli D, Grimsby J, et al.: The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014; 32(4): 381–386. PubMed Abstract | Publisher Full Text | Free Full Text
24. de Graaf CA, Choi J, Baldwin TM, et al.: Haemopedia: An Expression Atlas of Murine Hematopoietic Cells. Stem cell reports. 2016; 7(3): 571–582. PubMed Abstract | Publisher Full Text | Free Full Text
25. Hubert L, Arabie P: Comparing partitions. J Classif. 1985; 2(1): 193–218. Publisher Full Text
26. Studholme C, Hill DLG, Hawkes DJ: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn. 1999; 32(1): 71–86. Publisher Full Text
27. Rosenberg A, Hirschberg J: V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL). 2007. Reference Source
28. Tian L, Su S, Amann-Zalcenstein D, et al.: scPipe: a flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data. bioRxiv. 2017; 175927. Publisher Full Text
29. Dobin A, Davis CA, Schlesinger F, et al.: STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013; 29(1): 15–21. PubMed Abstract | Publisher Full Text | Free Full Text
30. Liao Y, Smyth GK, Shi W: The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 2013; 41(10): e108. PubMed Abstract | Publisher Full Text | Free Full Text
31. Guimaraes JC, Zavolan M: Patterns of ribosomal protein expression specify normal and malignant human cells. Genome Biol. 2016; 17(1): 236. PubMed Abstract | Publisher Full Text | Free Full Text
32. Duò A, Robinson MD, Soneson C: A systematic performance evaluation of clustering methods for single-cell RNA-seq data [version 1; referees: 2 approved with reservations]. F1000Res. 2018; 7: 1141. Publisher Full Text