生物技术通报 ›› 2021, Vol. 37 ›› Issue (1): 52-59.doi: 10.13560/j.cnki.biotech.bull.1985.2020-0469
收稿日期:
2020-04-21
出版日期:
2021-01-26
发布日期:
2021-01-15
作者简介:
张淼,女,硕士研究生,研究方向:轻工技术与工程;E-mail: 基金资助:
ZHANG Miao(), SUN Xiang-rui, XU Chun-ming()
Received:
2020-04-21
Published:
2021-01-26
Online:
2021-01-15
摘要:
单细胞RNA测序(Single cell RNA sequencing,scRNA-Seq)已经广泛应用于细胞分化、肿瘤微环境及多种疾病病因学研究。目前,由于scRNA-Seq具有低捕获率、高噪声、高变异性等特点,通过优化数据分析方法提高测序结果准确性已经成为测序领域的研究热点。对近年来数据分析过程中利用的数学方法进行了总结,讨论了数据分析的优势及存在的问题,以期为新算法的开发和应用提供参考,逐步提高测序结果的可靠性。
张淼, 孙祥瑞, 徐春明. 单细胞RNA测序数据分析方法研究进展[J]. 生物技术通报, 2021, 37(1): 52-59.
ZHANG Miao, SUN Xiang-rui, XU Chun-ming. Research Progress of Approaches in Single Cell RNA Sequencing Data Analysis[J]. Biotechnology Bulletin, 2021, 37(1): 52-59.
[1] |
Goodwin S, McPherson JD, McCombie WR. Coming of age:ten years of next-generation sequencing technologies[J]. Nature Reviews Genetics, 2016,17(6):333-351.
URL pmid: 27184599 |
[2] |
Shendure J, Balasubramanian S, Church GM, et al. DNA sequencing at 40:past, present and future[J]. Nature, 2017,550(7676):345-353.
doi: 10.1038/nature24286 URL pmid: 29019985 |
[3] |
See P, Lum J, Chen J, et al. A Single-cell sequencing guide for immunologists[J]. Frontiers in Immunology, 2018,9:2425.
doi: 10.3389/fimmu.2018.02425 URL pmid: 30405621 |
[4] | Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell[J]. Nature Methods, 2009,6(5):377-382. |
[5] |
Alessandra DM, Barbara DC . How to design a single-cell RNA-sequencing experiment:pitfalls, challenges and perspectives[J]. Briefings in Bioinformatics, 2018,20(4):1384-1394.
URL pmid: 29394315 |
[6] |
Trapnell C, Pachter L, Salzberg SL. TopHat:discovering splice junctions with RNA-Seq[J]. Bioinformatics, 2009,25(9):1105-1111.
URL pmid: 19289445 |
[7] | Dobin A, Davis Carrie A, Schlesinger F, et al. STAR:ultrafast universal RNA-seq aligner[J]. Bioinformatics, 2012,29(1):15-21. |
[8] |
Kim D, Langmead B, Salzberg SL. HISAT:a fast spliced aligner with low memory requirements[J]. Nature Methods, 2015,12(4):357-360.
URL pmid: 25751142 |
[9] |
Engström PG, Steijger T, Sipos B, et al. Systematic evaluation of spliced alignment programs for RNA-seq data[J]. Nature Methods, 2013,10(12):1185-1191.
doi: 10.1038/nmeth.2722 URL pmid: 24185836 |
[10] |
Crinier A, Milpied P, Escalière B, et al. High-dimensional single-cell analysis identifies organ-specific signatures and conserved NK cell subsets in humans and mice[J]. Immunity, 2018,49(5):971-986.
doi: 10.1016/j.immuni.2018.09.009 URL pmid: 30413361 |
[11] | Darmanis S, Sloan SA, Croote D, et al. Single-cell RNA-seq analysis of infiltrating neoplastic cells at the migrating front of human glioblastoma[J]. Cell reports, 2017,21(5):1399-1410. |
[12] |
Jiang P, Thomson JA, Stewart R, et al. Quality control of single-cell RNA-seq by SinQC[J]. Bioinformatics, 2016. 32(16):2514-2516.
URL pmid: 27153613 |
[13] | Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[J]. Genome Biology, 2014,15(12):550. |
[14] |
Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data[J]. Genome Biology, 2010,11(3):R25.
doi: 10.1186/gb-2010-11-3-r25 URL pmid: 20196867 |
[15] |
Bacher R, Chu LF, Leng N, et al. SCnorm:robust normalization of single-cell RNA-seq data[J]. Nature Methods, 2017,14(6):584-586.
URL pmid: 28418000 |
[16] | Hicks SC, Townes FW, Teng M, et al. Missing data and technical variability in single-cell RNA-sequencing experiments[J]. Biostats, 2018,19(4):562-578. |
[17] |
Van DD, Sharma R, Nainys J, et al. Recovering gene interactions from single-cell data using data diffusion[J]. Cell, 2018,174(3):716-729.
URL pmid: 29961576 |
[18] | Li WV, Li JJ . An accurate and robust imputation method scImpute for single-cell RNA-seq data[J]. Nature Communications, 2018,9(1):997. |
[19] |
Huang M, Wang JS, Torre E, et al. SAVER:gene expression recovery for single-cell RNA sequencing[J]. Nature Methods, 2018,15(7):539-542.
doi: 10.1038/s41592-018-0033-z URL pmid: 29941873 |
[20] |
Gong WM, Kwak IlY, Pota P, et al. DrImpute:imputing dropout events in single cell RNA sequencing data[J]. BMC Bioinformatics, 2018,19(1):220.
URL pmid: 29884114 |
[21] | Talwar D, Mongia A, Sengupta D, et al. AutoImpute:Autoencoder based imputation of single-cell RNA-seq data[J]. Scientific Reports, 2018,8(1):16329. |
[22] |
Tran HTN, Ang KS, Chevrier M, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data[J]. Genome Biology, 2020,21(1):12.
doi: 10.1186/s13059-019-1850-9 URL pmid: 31948481 |
[23] |
Risso D, Ngai J, Speed TP, et al. Normalization of RNA-seq data using factor analysis of control genes or samples[J]. Nature Biotechnology, 2014,32(9):896-902.
URL pmid: 25150836 |
[24] | Leek JT. Svaseq:removing batch effects and other unwanted noise from sequencing data[J]. Nucleic Acids Research, 2014,42(21):161. |
[25] | Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Research, 2015,43(7):47. |
[26] |
Haghverdi L, Lun ATL, Morgan MD, et al. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors[J]. Nature Biotechnology, 2018,36(5):421-427.
URL pmid: 29608177 |
[27] |
Hie B, Bryson B, Berger B, et al. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama[J]. Nature Biotechnology, 2019,37(6):685-691.
URL pmid: 31061482 |
[28] | Zhu S, Qing T, Zheng Y, et al. Advances in single-cell RNA sequencing and its applications in cancer research[J]. Oncotarget, 2017,8(32):53763-53779. |
[29] |
Aljanahi AA, Mark D, Dunbar CE . An introduction to the analysis of single-cell RNA-sequencing data[J]. Molecular Therapy - Methods & Clinical Development, 2018,10:189-196.
doi: 10.1016/j.omtm.2018.07.003 URL pmid: 30094294 |
[30] | Fletcher RB, Diya D, John N. Creating lineage trajectory maps via integration of single-cell RNA-sequencing and lineage tracing[J]. Bioessays, 2018,40(8):56. |
[31] |
Byungjin H, Hyun LJ, Duhee B. Single-cell RNA sequencing technologies and bioinformatics pipelines[J]. Experimental & Molecular Medicine, 2018,50(8):96.
URL pmid: 30089861 |
[32] | Alexander P, Vladimir B . Visualization of SNPs with t-SNE[J]. PLoS One, 2013,8(2):e56883. |
[33] |
Gate D, Saligrama N, Leventhal O, et al. Clonally expanded CD8 T cells patrol the cerebrospinal fluid in Alzheimer's disease[J]. Nature, 2020,577(7790):399-404.
doi: 10.1038/s41586-019-1895-7 URL pmid: 31915375 |
[34] | Becht E, McInnes L, Healy J, et al. Dimensionality reduction for visualizing single-cell data using UMAP[J]. Nat Biotechnol 2019,37:38-44. |
[35] | Wei Z, Shu C, Zhang C, et al. A short review of variants calling for single-cell-sequencing data with applications[J]. The International Journal of Biochemistry & Cell Biology, 2017,92:218-226. |
[36] | Minzhe G, Hui W, Steven PS, et al. SINCERA:A pipeline for single-cell rna-seq profiling analysis[J]. PLoS Computational Biology, 2015,11(11):e1004575. |
[37] | Iacono G, Mereu E, Guillaumet-Adkins A, et al. bigSCale:an analytical framework for big-scale single-cell data[J]. Genome Research, 2018,28(6):878-890. |
[38] |
Lin PJ, Troup M, Ho JWK. CIDR:Ultrafast and accurate clustering through imputation for single-cell RNA-seq data[J]. Genome Biology, 2017,18(1):59.
URL pmid: 28351406 |
[39] |
Xu C, Su ZC. Identification of cell types from single-cell transcriptomes using a novel clustering method[J]. Bioinformatics, 2015,31(12):1974-1980.
URL pmid: 25805722 |
[40] | Grün D, Muraro MJ, Boisset JC, et al. De novo prediction of stem cell identity using single-cell transcriptome data[J]. Cell Stem Cell, 2016,19(2):266-277. |
[41] |
Levine JH, Simonds EF, Bendall SC, et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis[J]. Cell, 2015,162(1):184-197.
URL pmid: 26095251 |
[42] | Kiselev VY, Kirschner K, Schaub MT, et al. SC3:consensus clustering of single-cell RNA-seq data[J]. Nature Methods, 2017,14(5):483-486. |
[43] | Ankur S, Elaine YC, Vibhor KAS, et al. Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy[J]. Nature Communications, 2018,9(1):4931. |
[44] | Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to single-cell differential expression analysis[J]. Nature Methods, 2014,11(7):740-742. |
[45] | Finak G, McDavid A, Yajima M, et al. MAST:a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data[J]. Genome Biology, 2015,16:278. |
[46] |
Qiu X, Hill A, Packer J, et al. Single-cell mRNA quantification and differential analysis with Census[J]. Nature Methods, 2017,14(3):309-315.
URL pmid: 28114287 |
[47] |
Chen L, Zheng S. BCseq:accurate single cell RNA-seq quantification with bias correction[J]. Nucleic Acids Research, 2018,46(14):e82.
URL pmid: 29718338 |
[48] | Soneson C, Robinson M. Bias, robustness and scalability in single-cell differential expression analysis[J]. Nature Methods, 2018,15(4):255-261. |
[49] |
Halpern KB, Shenhav R, Matcovitch-Natan O, et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver[J]. Nature, 2017,542(7641):352-356.
URL pmid: 28166538 |
[50] | Perešíni P, Kuzniar M, Kostic D. Dynamic, Fine-grained data plane monitoring with monocle[J]. IEEE/ACM Transactions on Networking, 2018,26(1):534-547. |
[51] |
Jaehoon S, Daniel AB, Yunhua ZS, et al. Single-cell RNA-Seq with waterfall reveals molecular cascades underlying adult neurogenesis[J]. Cell Stem Cell, 2015,17(3):360-372.
doi: 10.1016/j.stem.2015.07.013 URL pmid: 26299571 |
[52] |
Ji ZC, Ji HK. TSCAN:Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis[J]. Nucleic Acids Research, 2016,44(13):e117.
doi: 10.1093/nar/gkw430 URL pmid: 27179027 |
[53] |
Juliá M, Amalio T, Antonio R. Sincell:an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq[J]. Bioinformatics, 2015,31(20):3380-3382.
doi: 10.1093/bioinformatics/btv368 URL pmid: 26099264 |
[54] |
Welch JD, Hartemink AJ, Prins JF. SLICER:inferring branched, nonlinear cellular trajectories from single cell RNA-seq data[J]. Genome Biology, 2016,17(1):106.
doi: 10.1186/s13059-016-0975-3 URL pmid: 27215581 |
[55] |
Manu S, Michelle DT, Shlomit RZ, et al. Wishbone identifies bifurcating developmental trajectories from single-cell data[J]. Nature Biotechnology, 2016,34(6):637-645.
doi: 10.1038/nbt.3569 URL pmid: 27136076 |
[1] | 娄慧, 朱金成, 杨洋, 张薇. 抗、感品种棉花根系分泌物对尖孢镰刀菌生长及基因表达的影响[J]. 生物技术通报, 2023, 39(9): 156-167. |
[2] | 付钰, 贾瑞瑞, 何荷, 王良桂, 杨秀莲. 两种砧木楸树嫁接苗生长差异及转录组比较分析[J]. 生物技术通报, 2023, 39(8): 251-261. |
[3] | 吴昊, 刘紫微, 郑颖, 戴雅文, 时权. 单细胞水平解析人牙龈间充质干细胞异质性[J]. 生物技术通报, 2023, 39(7): 325-332. |
[4] | 杨洋, 朱金成, 娄慧, 韩泽刚, 张薇. 海岛棉与枯萎病菌的互作转录组分析[J]. 生物技术通报, 2023, 39(6): 259-273. |
[5] | 谢洋, 邢雨蒙, 周国彦, 刘美妍, 银珊珊, 闫立英. 黄瓜二倍体及其同源四倍体果实转录组分析[J]. 生物技术通报, 2023, 39(3): 152-162. |
[6] | 陈桂芳, 杨佳怡, 高运华, 任歌. 染色质免疫共沉淀测序技术研究进展[J]. 生物技术通报, 2022, 38(7): 40-50. |
[7] | 金姣姣, 刘自刚, 米文博, 徐明霞, 邹娅, 徐春梅, 赵彩霞. 利用RNA-Seq鉴定调控甘蓝型油菜叶片光合特性的低温胁迫应答基因[J]. 生物技术通报, 2022, 38(4): 126-142. |
[8] | 张雨函, 范熠, 李婷婷, 庞爽, 刘为, 白可喻, 张西美. 基于宏基因组测序的植物叶表微生物富集及DNA提取方法[J]. 生物技术通报, 2022, 38(3): 256-263. |
[9] | 孙燕, 王金刚, 臧丹丹, 赵恒田, 刘淑华. 不同发育时期蓝果忍冬果实的转录组分析[J]. 生物技术通报, 2022, 38(12): 204-213. |
[10] | 刘传和, 贺涵, 何秀古, 赖秋勤, 刘开, 邵雪花, 赖多, 匡石滋, 肖维强. 转录组与代谢组联合分析菠萝网纱覆盖防寒机制[J]. 生物技术通报, 2022, 38(11): 58-69. |
[11] | 陈正启, 余金凤, 冯云利, 马明, 岳万松, 郭相. 金耳菌丝及子实体的转录组差异分析[J]. 生物技术通报, 2021, 37(6): 73-84. |
[12] | 过冬冬, 孙芬, 贺轩昂, 羊东晔, 黄来强. 单细胞测序技术在肝脏疾病的应用与展望[J]. 生物技术通报, 2021, 37(1): 137-144. |
[13] | 朱庆元, 李天晴. 单细胞转录组测序技术在心脏发育、疾病以及医学中的应用[J]. 生物技术通报, 2021, 37(1): 145-154. |
[14] | 王丹蕊, 沈文丽, 魏子艳, 王尚, 邓晔. 单细胞测序技术在微生物生态领域中的应用[J]. 生物技术通报, 2020, 36(10): 237-246. |
[15] | 于海亮, 邹文斌, 王晓慧, 林雨鑫, 戴国俊, 张涛, 张跟喜, 谢恺舟, 王金玉, 施会强. 京海黄鸡柔嫩艾美耳球虫感染后盲肠转录组分析[J]. 生物技术通报, 2019, 35(11): 64-71. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||