대한임상검사과학회지 (Korean Journal of Clinical Laboratory Science)

  • 제56권1호
  • /
  • Pages.10-20
  • /
  • 2024
  • /
  • 1738-3544(pISSN)
  • /
  • 2288-1662(eISSN)
대한임상검사과학회 (Korean Society for Clinical Laboratory Science)
권호 표지
DOI QR코드

DOI QR Code

단일 세포 RNA 시퀀싱 데이터에 대한 컴퓨터 분석의 작업과정

The Workflow for Computational Analysis of Single-cell RNA-sequencing Data

  • 우성훈 (연세대학교 소프트웨어디지털헬스케어융합대학 임상병리학과) ;
  • 정병출 (캘리포니아대학교 버클리캠퍼스 영양과학 및 독성학과)
  • Sung-Hun WOO (Department of Biomedical Laboratory Science, College of Software and Digital Healthcare Convergence, Yonsei University) ;
  • Byung Chul JUNG (Department of Nutritional Sciences and Toxicology, University of California)
  • 투고 : 2024.01.01
  • 심사 : 2024.02.02
  • 발행 : 2024.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

RNA-시퀀싱은 표본에 대한 전사체 전체의 패턴을 제공하는 기법이다. 그러나 RNA-시퀀싱은 표본 내 전체 세포에 대한 평균 유전자 발현만 제공할 수 있으며, 표본 내의 이질성(heterogeneity)에 대한 정보는 제공하지 못한다. 단일 세포 RNA-시퀀싱 기술의 발전을 통해 우리는 표본의 단일 세포 수준에서 이질성과 유전자 발현의 동역학(dynamics)에 대한 이해를 할 수 있게 되었다. 예를 들어, 우리는 단일 세포 RNA-시퀀싱을 통해 복잡한 조직을 구성하는 다양한 세포 유형을 식별할 수 있으며, 특정 세포 유형의 유전자 발현 변화와 같은 정보를 알 수 있다. 단일 세포 RNA-시퀀싱은 처음 도입된 이후 많은 이들의 관심을 끌게 되었으며, 이를 활용하기 위한 대규모 생물정보학(bioinformatics) 도구가 개발되었다. 그러나 단일 세포 RNA-시퀀싱에서 생성된 빅데이터 분석에는 데이터 전처리에 대한 이해와 전처리 이후 다양한 분석 기술에 대한 이해가 필요하다. 본 종설에서는 단일 세포 RNA-시퀀싱 데이터분석과 관련된 작업과정의 개요를 제시한다. 먼저 데이터의 품질 관리, 정규화 및 차원 감소와 같은 데이터의 전 처리 과정에 대해 설명한다. 그 이후, 가장 일반적으로 사용되는 생물정보학 도구를 활용한 데이터의 후속 분석에 대해 설명한다. 본 종설은 이 분야에 관심이 있는 새로운 연구자를 위한 가이드라인을 제공하는 것을 목표로 한다.

RNA-sequencing (RNA-seq) is a technique used for providing global patterns of transcriptomes in samples. However, it can only provide the average gene expression across cells and does not address the heterogeneity within the samples. The advances in single-cell RNA sequencing (scRNA-seq) technology have revolutionized our understanding of heterogeneity and the dynamics of gene expression at the single-cell level. For example, scRNA-seq allows us to identify the cell types in complex tissues, which can provide information regarding the alteration of the cell population by perturbations, such as genetic modification. Since its initial introduction, scRNA-seq has rapidly become popular, leading to the development of a huge number of bioinformatic tools. However, the analysis of the big dataset generated from scRNA-seq requires a general understanding of the preprocessing of the dataset and a variety of analytical techniques. Here, we present an overview of the workflow involved in analyzing the scRNA-seq dataset. First, we describe the preprocessing of the dataset, including quality control, normalization, and dimensionality reduction. Then, we introduce the downstream analysis provided with the most commonly used computational packages. This review aims to provide a workflow guideline for new researchers interested in this field.

키워드

참고문헌

  1. Woo SH, Jung BC. Big data analytics in RNA-sequencing. Korean J Clin Lab Sci. 2023;55:235-243. https://doi.org/10.15324/kjcls.2023.55.4.235
  2. Jung BC, You D, Lee I, Li D, Schill RL, Ma K, et al. TET3 plays a critical role in white adipose development and diet-induced remodeling. Cell Rep. 2023;42:113196. https://doi.org/10.1016/j.celrep.2023.113196
  3. Kim TK, Bae EJ, Jung BC, Choi M, Shin SJ, Park SJ, et al. Inflammation promotes synucleinopathy propagation. Exp Mol Med. 2022;54:2148-2161. https://doi.org/10.1038/s12276-022-00895-w
  4. Park S, Lee C, Ku BM, Kim M, Park WY, Kim NKD, et al. Paired analysis of tumor mutation burden calculated by targeted deep sequencing panel and whole exome sequencing in non-small cell lung cancer. BMB Rep. 2021;54:386-391. https://doi.org/10.5483/bmbrep.2021.54.7.045
  5. Li X, Wang CY. From bulk, single-cell to spatial RNA sequencing. Int J Oral Sci. 2021;13:36. https://doi.org/10.1038/s41368-021-00146-0
  6. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6:377-382. https://doi.org/10.1038/nmeth.1315
  7. Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med. 2018;50:1-14. https://doi.org/10.1038/s12276-018-0071-8
  8. Haque A, Engel J, Teichmann SA, Lonnberg T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 2017;9:75. https://doi.org/10.1186/s13073-017-0467-4
  9. Mair F, Erickson JR, Voillet V, Simoni Y, Bi T, Tyznik AJ, et al. A targeted multi-omic analysis approach measures protein expression and low-abundance transcripts on the single-cell level. Cell Rep. 2020;31:107499. https://doi.org/10.1016/j.celrep.2020.03.063
  10. Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049. https://doi.org/10.1038/ncomms14049
  11. Lun ATL, Riesenfeld S, Andrews T, Dao TP, Gomes T, Marioni JC. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 2019;20:63. https://doi.org/10.1186/s13059-019-1662-y
  12. Yang S, Corbett SE, Koga Y, Wang Z, Johnson WE, Yajima M, et al. Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biol. 2020;21:57. https://doi.org/10.1186/s13059-020-1950-6
  13. Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ, Marioni JC, et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 2016;17:29. https://doi.org/10.1186/s13059-016-0888-1
  14. Osorio D, Cai JJ. Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control. Bioinformatics. 2021;37:963-967. https://doi.org/10.1093/bioinformatics/btaa751
  15. Emont MP, Jacobs C, Essene AL, Pant D, Tenen D, Colleluori G, et al. A single-cell atlas of human and mouse white adipose tissue. Nature. 2022;603:926-933. https://doi.org/10.1038/s41586-022-04518-2
  16. Wolock SL, Lopez R, Klein AM. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 2019;8:281-291.e9. https://doi.org/10.1016/j.cels.2018.11.005
  17. McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 2019;8:329-337.e4. https://doi.org/10.1016/j.cels.2019.03.003
  18. Xi NM, Li JJ. Benchmarking computational doublet-detection methods for single-cell RNA sequencing data. Cell Syst. 2021;12:176-194.e6. https://doi.org/10.1016/j.cels.2020.11.008
  19. Lu J, Sheng Y, Qian W, Pan M, Zhao X, Ge Q. scRNA-seq data analysis method to improve analysis performance. IET Nanobiotechnol. 2023;17:246-256. https://doi.org/10.1049/nbt2.12115
  20. Wu Y, Zhang K. Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol. 2020;16:408-421. https://doi.org/10.1038/s41581-020-0262-0
  21. Lun AT, Bach K, Marioni JC. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 2016;17:75. https://doi.org/10.1186/s13059-016-0947-7
  22. Vallejos CA, Risso D, Scialdone A, Dudoit S, Marioni JC. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat Methods. 2017;14:565-571. https://doi.org/10.1038/nmeth.4292
  23. Choudhary S, Satija R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol. 2022;23:27. https://doi.org/10.1186/s13059-021-02584-9
  24. Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 2019;20:296. https://doi.org/10.1186/s13059-019-1874-1
  25. Chandrashekar G, Sahin F. A survey on feature selection methods. Comput Electr Eng. 2014;40:16-28. https://doi.org/10.1016/j.compeleceng.2013.11.024
  26. Yang P, Huang H, Liu C. Feature selection revisited in the single-cell era. Genome Biol. 2021;22:321. https://doi.org/10.1186/s13059-021-02544-3
  27. Sheng J, Li WV. Selecting gene features for unsupervised analysis of single-cell gene expression data. Brief Bioinform. 2021;22:bbab295. https://doi.org/10.1093/bib/bbab295
  28. Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15:e8746. https://doi.org/10.15252/msb.20188746
  29. Do VH, Canzar S. A generalization of t-SNE and UMAP to single-cell multimodal omics. Genome Biol. 2021;22:130. https://doi.org/10.1186/s13059-021-02356-5
  30. Meyer BH, Pozo ATR, Nunan Zola WM. Global and local structure preserving GPU t-SNE methods for large-scale applications. Expert Syst Appl. 2022;201:116918. https://doi.org/10.1016/j.eswa.2022.116918
  31. Lee JA, Renard E, Bernard G, Dupont P, Verleysen M. Type 1 and 2 mixtures of Kullback-Leibler divergences as cost functions in dimensionality reduction based on similarity preservation. Neurocomputing. 2013;112:92-108. https://doi.org/10.1016/j.neucom.2012.12.036
  32. Ge H, Zhu Z, Lou K, Wei W, Liu R, Damasevicius R, et al. Classification of infrared objects in manifold space using Kullback-Leibler divergence of gaussian distributions of image points. Symmetry. 2020;12:434. https://doi.org/10.3390/sym12030434
  33. Kobak D, Berens P. The art of using t-SNE for single-cell transcriptomics. Nat Commun. 2019;10:5416. https://doi.org/10.1038/s41467-019-13056-x
  34. Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2018. [Epub ahead of print]. https://doi.org/10.1038/nbt.4314
  35. Sainburg T, McInnes L, Gentner TQ. Parametric UMAP embed-dings for representation and semisupervised learning. Neural Comput. 2021;33:2881-2907. https://doi.org/10.1162/neco_a_01434
  36. Andrews TS, Kiselev VY, McCarthy D, Hemberg M. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nat Protoc. 2021;16:1-9. https://doi.org/10.1038/s41596-020-00409-w
  37. Kobak D, Linderman GC. Initialization is critical for preserving global data structure in both t-SNE and UMAP. Nat Biotechnol. 2021;39:156-157. https://doi.org/10.1038/s41587-020-00809-z
  38. Traag VA, Waltman L, van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019;9:5233. https://doi.org/10.1038/s41598-019-41695-z
  39. Hairol Anuar SH, Abas ZA, Yunos NM, Mohd Zaki NH, Hashim NA, Mokhtar MF, et al. Comparison between Louvain and Leiden algorithm for network structure: a review. J Phys Conf Ser. 2021;2129:012028. https://doi.org/10.1088/1742-6596/2129/1/012028
  40. El Bouchefry K, de Souza RS. Learning in big data: introduction to machine learning. In: Skoda P, Adam F, editors. Knowledge discovery in big data from astronomy and earth observation: AstroGeoInformatics. Elsevier: 2020. p. 225-249.
  41. Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19:15. https://doi.org/10.1186/s13059-017-1382-0
  42. Martini E, Kunderfranco P, Peano C, Carullo P, Cremonesi M, Schorn T, et al. Single-cell sequencing of mouse heart immune infiltrate in pressure overload-driven heart failure reveals extent of immune activation. Circulation. 2019;140:2089-2107. https://doi.org/10.1161/circulationaha.119.041694
  43. Qiu P. Embracing the dropouts in single-cell RNA-seq analysis. Nat Commun. 2020;11:1169. https://doi.org/10.1038/s41467-020-14976-9
  44. Squair JW, Gautier M, Kathe C, Anderson MA, James ND, Hutson TH, et al. Confronting false discoveries in single-cell differential expression. Nat Commun. 2021;12:5692. https://doi.org/10.1038/s41467-021-25960-2
  45. Soneson C, Robinson MD. Bias, robustness and scalability in single-cell differential expression analysis. Nat Methods. 2018;15: 255-261. https://doi.org/10.1038/nmeth.4612
  46. Rauch A, Mandrup S. Transcriptional networks controlling stromal cell differentiation. Nat Rev Mol Cell Biol. 2021;22:465-482. https://doi.org/10.1038/s41580-021-00357-7
  47. Bertoli C, Skotheim JM, de Bruin RA. Control of cell cycle transcription during G1 and S phases. Nat Rev Mol Cell Biol. 2013;14:518-528. https://doi.org/10.1038/nrm3629
  48. Song D, Li JJ. PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data. Genome Biol. 2021;22:124. https://doi.org/10.1186/s13059-021-02341-y
  49. Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of single-cell trajectory inference methods. Nat Biotechnol. 2019; 37:547-554. https://doi.org/10.1038/s41587-019-0071-9
  50. Greenblatt MB, Ono N, Ayturk UM, Debnath S, Lalani S. The un-mixing problem: a guide to applying single-cell RNA sequencing to bone. J Bone Miner Res. 2019;34:1207-1219. https://doi.org/10.1002/jbmr.3802
  51. 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;32:381-386. https://doi.org/10.1038/nbt.2859
  52. Zhang Y, Tran D, Nguyen T, Dascalu SM, Harris FC Jr. A robust and accurate single-cell data trajectory inference method using ensemble pseudotime. BMC Bioinformatics. 2023;24:55. https://doi.org/10.1186/s12859-023-05179-2
  53. Van de Sande B, Lee JS, Mutasa-Gottgens E, Naughton B, Bacon W, Manning J, et al. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov. 2023;22:496-520. https://doi.org/10.1038/s41573-023-00688-4
  54. Duncavage EJ, Bagg A, Hasserjian RP, DiNardo CD, Godley LA, Iacobucci I, et al. Genomic profiling for clinical decision making in myeloid neoplasms and acute leukemia. Blood. 2022;140:2228-2247. https://doi.org/10.1182/blood.2022015853
  55. Kim N, Eum HH, Lee HO. Clinical perspectives of single-cell RNA sequencing. Biomolecules. 2021;11:1161. https://doi.org/10.3390/biom11081161
  56. Riess JW, Gandara DR, Frampton GM, Madison R, Peled N, Bufill JA, et al. Diverse EGFR exon 20 insertions and co-occurring molecular alterations identified by comprehensive genomic profiling of NSCLC. J Thorac Oncol. 2018;13:1560-1568. https://doi.org/10.1016/j.jtho.2018.06.019
  57. Vincent MD, Kuruvilla MS, Leighl NB, Kamel-Reid S. Biomarkers that currently affect clinical practice: EGFR, ALK, MET, KRAS. Curr Oncol. 2012;19(Suppl 1):S33-S44. https://doi.org/10.3747/co.19.1149
  58. Qian J, Olbrecht S, Boeckx B, Vos H, Laoui D, Etlioglu E, et al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. 2020;30:745-762. https://doi.org/10.1038/s41422-020-0355-0