Abstract
Single-cell transcriptome sequencing, often referred to as single-cell RNA sequencing (scRNA-seq), is used to measure gene expression at the single-cell level and provides a higher resolution of cellular differences than bulk RNA-seq. With more detailed and accurate information, scRNA-seq will greatly promote the understanding of cell functions, disease progression, and treatment response. Although the scRNA-seq experimental protocols have been improved very quickly, many challenges in the scRNA-seq data analysis still need to be overcome. In this chapter, we focus on the introduction and discussion of the research status in the field of scRNA-seq data normalization and cluster analysis, which are the two most important challenges in the scRNA-seq data analysis. Particularly, we present a protocol to discover and validate cancer stem cells (CSCs) using scRNA-seq. Suggestions have also been made to help researchers rationally design their scRNA-seq experiments and data analysis in their future studies.
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References
Gao S, Ou J, Xiao K (2014) R language and Bioconductor in bioinformatics applications (Chinese Edition). Tianjin Science and Technology Translation Publishing, Co. Ltd, Tianjin
Stegle O, Teichmann SA, Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16(3):133–145
Zhang M, Sun H, Fei Z, Zhan F, Gong X, Gao S (2014) Fastq_clean: an optimized pipeline to clean the Illumina sequencing data with quality control. 2014 I.E. international conference on bioinformatics and biomedicine, pp 44–48
Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M, Leonhardt H, Heyn H, Hellmann I, Enard W (2017) Comparative analysis of single-cell RNA sequencing methods. Mol Cell 65(4):631–643
Gao S, Tian X, Chang H, Sun Y, Wu Z, Cheng Z, Dong P, Zhao Q, Ruan J, Bu W (2017) Two novel lncRNAs discovered in human mitochondrial DNA using PacBio full-length transcriptome data. Mitochondrion. https://doi.org/10.1016/j.mito.2017.08.002
Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, Mccarthy DJ, Marioni JC, Teichmann SA (2016) Classification of low quality cells from single-cell RNA-seq data. Genome Biol 17(1):29
Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106
Robinson MD, Mccarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140
Zhang Y, Li D, Sun B (2015) Do housekeeping genes exist? PLoS One 10(5):e0123691
Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M, Gingeras TR, Oliver B (2011) Synthetic spike-in standards for RNA-seq experiments. Genome Res 21(9):1543–1551
Risso D, Ngai J, Speed TP, Dudoit S (2014) Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol 32(9):896–902
Lovén J, Orlando DA, Sigova AA, Lin CY, Rahl PB, Burge CB, Levens DL, Lee TI, Young RA (2012) Revisiting global gene expression analysis. Cell 151(3):476–482
Islam S, Zeisel A, Joost S, La MG, Zajac P, Kasper M, Lönnerberg P, Linnarsson S (2014) Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods 11(2):163–166
Lun AT, Bach K, Marioni JC (2016) Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol 17(1):75
Ren Y, Zhang J, Sun Y, Wu Z, Ruan J, He B, Liu G, Gao S, Bu W (2016) Full-length transcriptome sequencing on PacBio platform (in Chinese). Chin Sci Bull 11(61):1250–1254
Satija R, Farrell JA, Gennert D, Schier AF, Regev A (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33(5):495–502
Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometr Intell Lab Syst 2(1–3):37–52
Hyvarinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13(4–5):411–430
Balakrishnama S, Ganapathiraju A (1998) Linear discriminant analysis – a brief tutorial. Procof Intjoint Confon Neural Networks 3(94):387–391
Carroll JD, Arabie P (1980) Multidimensional scaling. Annu Rev Psychol 31(31):607–649
Maaten LVD, Hinton G (2008) Viualizing data using t-SNE. J Mach Learn Res 9(2605):2579–2605
Levina E, Bickel PJ (2004) Maximum likelihood estimation of intrinsic dimension. Adv Neural Inf Proces Syst 17:777–784
Camastra F, Vinciarelli A (2002) Estimating the intrinsic dimension of data with a fractal-based method. IEEE Trans Pattern Anal Mach Intell 24(10):1404–1407
Pettis KW, Bailey TA, Jain AK, Dubes RC (1979) An intrinsic dimensionality estimator from near-neighbor information. IEEE Trans Pattern Anal Mach Intell PAMI-1(1):25–37
Costa JA, Hero AO (2004) Geodesic entropic graphs for dimension and entropy estimation in manifold learning. IEEE Trans Signal Process 52(8):2210–2221
Kfgl B (2002) Intrinsic dimension estimation using packing numbers. Adv Neural Inform Process Syst NIPS-02:697–704
Pettit JB, Tomer R, Achim K, Richardson S, Azizi L, Marioni J (2014) Identifying cell types from spatially referenced single-cell expression datasets. PLoS Comput Biol 10(9):e1003824
O'Flaherty JD, Barr M, Fennell D, Richard D, Reynolds J, O'Leary J, O’Byrne K (2012) The cancer stem-cell hypothesis: its emerging role in lung cancer biology and its relevance for future therapy. J Thorac Oncol 7(12):1880–1890
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) Star: ultrafast universal rna-seq aligner. Bioinformatics 29(1):15–21
Wu Z, Liu W, Jin X, Yu D, Wang H, Liu L, Ruan J, Gao S (2018) NormExpression: an R package to normalize gene expression data using evaluated methods. bioRxiv. https://doi.org/10.1101/251140
Acknowledgments
I appreciate help equally from the people listed below. They are Professor Wenjun Bu; Professor Lin Liu; Ph.D. student Hua Wang; Master’s student Yu Sun and Deshui Yu from College of Life Sciences, Nankai University; Professor Jishou Ruan; PhD student Zhenfeng Wu from School of Mathematical Sciences, Nankai University; and Associate Professor Weixiang Liu from Shenzhen University.
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Gao, S. (2018). Data Analysis in Single-Cell Transcriptome Sequencing. In: Huang, T. (eds) Computational Systems Biology. Methods in Molecular Biology, vol 1754. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7717-8_18
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DOI: https://doi.org/10.1007/978-1-4939-7717-8_18
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