1932

Abstract

Since the first publication a decade ago describing the use of single-cell RNA sequencing (scRNA-seq) in the context of cancer, over 200 datasets and thousands of scRNA-seq studies have been published in cancer biology. scRNA-seq technologies have been applied across dozens of cancer types and a diverse array of study designs to improve our understanding of tumor biology, the tumor microenvironment, and therapeutic responses, and scRNA-seq is on the verge of being used to improve decision-making in the clinic. Computational methodologies and analytical pipelines are key in facilitating scRNA-seq research. Numerous computational methods utilizing the most advanced tools in data science have been developed to extract meaningful insights. Here, we review the advancements in cancer biology gained by scRNA-seq and discuss the computational challenges of the technology that are specific to cancer research.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-020722-091857
2023-08-10
2024-04-13
Loading full text...

Full text loading...

/deliver/fulltext/biodatasci/6/1/annurev-biodatasci-020722-091857.html?itemId=/content/journals/10.1146/annurev-biodatasci-020722-091857&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Grivennikov SI, Greten FR, Karin M 2010. Immunity, inflammation, and cancer. Cell 140:6883–99
    [Google Scholar]
  2. 2.
    Lizée G, Radvanyi LG, Overwijk WW, Hwu P. 2006. Improving antitumor immune responses by circumventing immunoregulatory cells and mechanisms. Clin. Cancer Res. 12:164794–803
    [Google Scholar]
  3. 3.
    DeNardo DG, Ruffell B. 2019. Macrophages as regulators of tumor immunity and immunotherapy. Nat. Rev. Immunol. 19:6369–82
    [Google Scholar]
  4. 4.
    Nishikawa H, Sakaguchi S. 2014. Regulatory T cells in cancer immunotherapy. Curr. Opin. Immunol. 27:1–7
    [Google Scholar]
  5. 5.
    Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC et al. 2012. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366:262443–54
    [Google Scholar]
  6. 6.
    Bainbridge MN, Warren RL, Hirst M, Romanuik T, Zeng T et al. 2006. Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach. BMC Genom. 7:246
    [Google Scholar]
  7. 7.
    Weinstein JN, Collisson EA, Mills GB, Shaw KM, Ozenberger BA et al. 2013. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45:101113–20
    [Google Scholar]
  8. 8.
    Aran D, Sirota M, Butte AJ. 2015. Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6:8971
    [Google Scholar]
  9. 9.
    Finotello F, Trajanoski Z. 2018. Quantifying tumor-infiltrating immune cells from transcriptomics data. Cancer Immunol. Immunother. 67:71031–40
    [Google Scholar]
  10. 10.
    Jin C, Chen M, Lin D-Y, Sun W. 2021. Cell-type-aware analysis of RNA-seq data. Nat. Comput. Sci. 1:4253–61
    [Google Scholar]
  11. 11.
    Tang F, Barbacioru C, Wang Y, Nordman E, Lee C et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6:5377–82
    [Google Scholar]
  12. 12.
    Hashimshony T, Wagner F, Sher N, Yanai I 2012. CEL-Seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep 2:3666–73
    [Google Scholar]
  13. 13.
    Ramsköld D, Luo S, Wang Y-C, Li R, Deng Q et al. 2012. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30:8777–82
    [Google Scholar]
  14. 14.
    Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM et al. 2014. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344:61901396–401
    [Google Scholar]
  15. 15.
    Hwang B, Lee JH, Bang D. 2018. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp. Mol. Med. 50:81–14
    [Google Scholar]
  16. 16.
    Zappia L, Phipson B, Oshlack A. 2018. Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database. PLOS Comput. Biol. 14:6e1006245
    [Google Scholar]
  17. 17.
    Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW et al. 2017. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8:14049
    [Google Scholar]
  18. 18.
    Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. 2018. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36:5411–20
    [Google Scholar]
  19. 19.
    McCarthy DJ, Campbell KR, Lun ATL, Wills QF. 2017. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33:81179–86
    [Google Scholar]
  20. 20.
    Satija R, Farrell JA, Gennert D, Schier AF, Regev A. 2015. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33:5495–502
    [Google Scholar]
  21. 21.
    Levine JH, Simonds EF, Bendall SC, Davis KL, Amir ED et al. 2015. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162:1184–97
    [Google Scholar]
  22. 22.
    Wang J, Ma A, Chang Y, Gong J, Jiang Y et al. 2021. scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses. Nat. Commun. 12: 1882.
    [Google Scholar]
  23. 23.
    van der Maaten L, Hinton G. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:862579–605
    [Google Scholar]
  24. 24.
    McInnes L, Healy J, Saul N, Großberger L. 2018. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3:29861
    [Google Scholar]
  25. 25.
    Chari T, Pachter L 2022. The specious art of single-cell genomics. bioRxiv 2021.08.25.457696 https://doi.org/10.1101/2021.08.25.457696
    [Crossref]
  26. 26.
    Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN et al. 2020. Orchestrating single-cell analysis with Bioconductor. Nat. Methods 17:2137–45
    [Google Scholar]
  27. 27.
    Ding J, Adiconis X, Simmons SK, Kowalczyk MS, Hession CC et al. 2020. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. 38:6737–46
    [Google Scholar]
  28. 28.
    Zhang X, Lan Y, Xu J, Quan F, Zhao E et al. 2019. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res 47:D1D721–28
    [Google Scholar]
  29. 29.
    Franzén O, Gan L-M, Björkegren JLM. 2019. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019:baz046
    [Google Scholar]
  30. 30.
    Chen Z, Na H, Wu A 2021. ImmuCellDB: an indicative database of immune cell composition from different tissues and disease conditions in mouse and human. Front. Immunol. 12:670070
    [Google Scholar]
  31. 31.
    Shao X, Liao J, Lu X, Xue R, Ai N, Fan X 2020. scCATCH: automatic annotation on cell types of clusters from single-cell RNA sequencing data. iScience 23:3100882
    [Google Scholar]
  32. 32.
    Cao Y, Wang X, Peng G. 2020. SCSA: a cell type annotation tool for single-cell RNA-seq data. Front. Genet. 11:490
    [Google Scholar]
  33. 33.
    Zhang Z, Luo D, Zhong X, Choi JH, Ma Y et al. 2019. SCINA: a semi-supervised subtyping algorithm of single cells and bulk samples. Genes 10:7531
    [Google Scholar]
  34. 34.
    Zhang AW, O'Flanagan C, Chavez EA, Lim JLP, Ceglia N et al. 2019. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat. Methods 16:101007–15
    [Google Scholar]
  35. 35.
    Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL et al. 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102:4315545–50
    [Google Scholar]
  36. 36.
    Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H et al. 2017. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14:111083–86
    [Google Scholar]
  37. 37.
    Schubert M, Klinger B, Klünemann M, Sieber A, Uhlitz F et al. 2018. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat. Commun. 9:20
    [Google Scholar]
  38. 38.
    Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J. 2019. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res 29:81363–75
    [Google Scholar]
  39. 39.
    Aran D, Looney AP, Liu L, Wu E, Fong V et al. 2019. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20:2163–72
    [Google Scholar]
  40. 40.
    Kiselev VY, Yiu A, Hemberg M. 2018. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15:5359–62
    [Google Scholar]
  41. 41.
    Li H, Courtois ET, Sengupta D, Tan Y, Chen KH et al. 2017. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet. 49:5708–18
    [Google Scholar]
  42. 42.
    Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S et al. 2021. Integrated analysis of multimodal single-cell data. Cell 184:133573–87.e29
    [Google Scholar]
  43. 43.
    de Kanter JK, Lijnzaad P, Candelli T, Margaritis T, Holstege FCP. 2019. CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing. Nucleic Acids Res 47:16e95
    [Google Scholar]
  44. 44.
    Pliner HA, Shendure J, Trapnell C. 2019. Supervised classification enables rapid annotation of cell atlases. Nat. Methods 16:10983–86
    [Google Scholar]
  45. 45.
    Tan Y, Cahan P. 2019. SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species. Cell Syst 9:2207–13.e2
    [Google Scholar]
  46. 46.
    Wagner F, Yanai I. 2018. Moana: a robust and scalable cell type classification framework for single-cell RNA-Seq data. bioRxiv 456129. https://doi.org/10.1101/456129
  47. 47.
    Alquicira-Hernandez J, Sathe A, Ji HP, Nguyen Q, Powell JE. 2019. scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol 20:264
    [Google Scholar]
  48. 48.
    Lotfollahi M, Naghipourfar M, Luecken MD, Khajavi M, Büttner M et al. 2022. Mapping single-cell data to reference atlases by transfer learning. Nat. Biotechnol. 40:121–30
    [Google Scholar]
  49. 49.
    Yang F, Wang W, Wang F, Fang Y, Tang D et al. 2022. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat. Mach. Intell. 4:852–66
    [Google Scholar]
  50. 50.
    Abdelaal T, Michielsen L, Cats D, Hoogduin D, Mei H et al. 2019. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol 20:194
    [Google Scholar]
  51. 51.
    Tabula Sapiens Consort 2022. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376:6594eabl4896
    [Google Scholar]
  52. 52.
    Nieto P, Elosua-Bayes M, Trincado JL, Marchese D, Massoni-Badosa R et al. 2021. A single-cell tumor immune atlas for precision oncology. Genome Res 31:101913–26
    [Google Scholar]
  53. 53.
    Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. 2011. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27:121739–40
    [Google Scholar]
  54. 54.
    Dohmen J, Baranovskii A, Ronen J, Uyar B, Franke V, Akalin A. 2022. Identifying tumor cells at the single-cell level using machine learning. Genome Biol 23:123
    [Google Scholar]
  55. 55.
    Pelka K, Hofree M, Chen JH, Sarkizova S, Pirl JD et al. 2021. Spatially organized multicellular immune hubs in human colorectal cancer. Cell 184:184734–52.e20
    [Google Scholar]
  56. 56.
    Tirosh I, Izar B, Prakadan SM, Wadsworth MH, Treacy D et al. 2016. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352:6282189–96
    [Google Scholar]
  57. 57.
    Yizhak K, Aguet F, Kim J, Hess JM, Kübler K et al. 2019. RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Science 364:6444eaaw0726
    [Google Scholar]
  58. 58.
    Vu TN, Nguyen H-N, Calza S, Kalari KR, Wang L, Pawitan Y 2019. Cell-level somatic mutation detection from single-cell RNA sequencing. Bioinformatics 35:224679–87
    [Google Scholar]
  59. 59.
    Peng J, Sun B-F, Chen C-Y, Zhou J-Y, Chen Y-S et al. 2019. Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res 29:9725–38
    [Google Scholar]
  60. 60.
    Johnson WE, Li C, Rabinovic A. 2007. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–27
    [Google Scholar]
  61. 61.
    Risso D, Ngai J, Speed TP, Dudoit S. 2014. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32:9896–902
    [Google Scholar]
  62. 62.
    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW et al. 2015. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:7e47
    [Google Scholar]
  63. 63.
    Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E et al. 2019. Comprehensive integration of single-cell data. Cell 177:71888–902.e21
    [Google Scholar]
  64. 64.
    Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F et al. 2019. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16:121289–96
    [Google Scholar]
  65. 65.
    Welch JD, Kozareva V, Ferreira A, Vanderburg C, Martin C, Macosko EZ. 2019. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177:71873–87.e17
    [Google Scholar]
  66. 66.
    Hie B, Bryson B, Berger B. 2019. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37:6685–91
    [Google Scholar]
  67. 67.
    Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. 2018. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15:121053–58
    [Google Scholar]
  68. 68.
    Stein-O'Brien GL, Clark BS, Sherman T, Zibetti C, Hu Q et al. 2019. Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species. Cell Syst 8:5395–411.e8
    [Google Scholar]
  69. 69.
    Luecken MD, Büttner M, Chaichoompu K, Danese A, Interlandi M et al. 2022. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19:41–50
    [Google Scholar]
  70. 70.
    Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. 2020. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat. Protoc. 15:41484–506
    [Google Scholar]
  71. 71.
    Pawson AJ, Sharman JL, Benson HE, Faccenda E, Alexander SPH et al. 2014. The IUPHAR/BPS Guide to Pharmacology: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res 42:D1098–106
    [Google Scholar]
  72. 72.
    Breuer K, Foroushani AK, Laird MR, Chen C, Sribnaia A et al. 2013. InnateDB: systems biology of innate immunity and beyond–recent updates and continuing curation. Nucleic Acids Res 41:D1228–33
    [Google Scholar]
  73. 73.
    Orchard S, Ammari M, Aranda B, Breuza L, Briganti L et al. 2014. The MIntAct project–IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42:D358–63
    [Google Scholar]
  74. 74.
    Browaeys R, Saelens W, Saeys Y. 2020. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17:2159–62
    [Google Scholar]
  75. 75.
    Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. 2017. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D1D353–61
    [Google Scholar]
  76. 76.
    Ramilowski JA, Goldberg T, Harshbarger J, Kloppmann E, Lizio M et al. 2015. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat. Commun. 6:17866
    [Google Scholar]
  77. 77.
    Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R et al. 2021. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12:1088
    [Google Scholar]
  78. 78.
    Zhang L, Li Z, Skrzypczynska KM, Fang Q, Zhang W et al. 2020. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell 181:2442–59.e29
    [Google Scholar]
  79. 79.
    Lee H-O, Hong Y, Etlioglu HE, Cho YB, Pomella V et al. 2020. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer. Nat. Genet. 52:6594–603
    [Google Scholar]
  80. 80.
    Kim N, Kim HK, Lee K, Hong Y, Cho JH et al. 2020. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat. Commun. 11:2285
    [Google Scholar]
  81. 81.
    Svensson V, da Veiga Beltrame E, Pachter L. 2020. A curated database reveals trends in single-cell transcriptomics. Database 2020:baaa073
    [Google Scholar]
  82. 82.
    Sun D, Wang J, Han Y, Dong X, Ge J et al. 2021. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res 49:D1420–30
    [Google Scholar]
  83. 83.
    Zheng L, Qin S, Si W, Wang A, Xing B et al. 2021. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374:6574abe6474
    [Google Scholar]
  84. 84.
    Cheng S, Li Z, Gao R, Xing B, Gao Y et al. 2021. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184:3792–809.e23
    [Google Scholar]
  85. 85.
    Baslan T, Kendall J, Volyanskyy K, McNamara K, Cox H et al. 2020. Novel insights into breast cancer copy number genetic heterogeneity revealed by single-cell genome sequencing. eLife 9:e51480
    [Google Scholar]
  86. 86.
    He D, Wang D, Lu P, Yang N, Xue Z et al. 2021. Single-cell RNA sequencing reveals heterogeneous tumor and immune cell populations in early-stage lung adenocarcinomas harboring EGFR mutations. Oncogene 40:2355–68
    [Google Scholar]
  87. 87.
    Bian S, Hou Y, Zhou X, Li X, Yong J et al. 2018. Single-cell multiomics sequencing and analyses of human colorectal cancer. Science 362:64181060–63
    [Google Scholar]
  88. 88.
    Ma L, Hernandez MO, Zhao Y, Mehta M, Tran B et al. 2019. Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer. Cancer Cell 36:4418–30.e6
    [Google Scholar]
  89. 89.
    Davis RT, Blake K, Ma D, Gabra MBI, Hernandez GA et al. 2020. Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing. Nat. Cell Biol. 22:3310–20
    [Google Scholar]
  90. 90.
    Pandiani C, Strub T, Nottet N, Cheli Y, Gambi G et al. 2021. Single-cell RNA sequencing reveals intratumoral heterogeneity in primary uveal melanomas and identifies HES6 as a driver of the metastatic disease. Cell Death Differ 28:61990–2000
    [Google Scholar]
  91. 91.
    Young MD, Mitchell TJ, Vieira Braga FA, Tran MG, Stewart BJ et al. 2018. Single cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361:6402594–99
    [Google Scholar]
  92. 92.
    Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C et al. 2018. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174:51293–308.e36
    [Google Scholar]
  93. 93.
    van der Leun AM, Schumacher TN. 2021. An atlas of intratumoral T cells. Science 374:65741446–47
    [Google Scholar]
  94. 94.
    Zilionis R, Engblom C, Pfirschke C, Savova V, Zemmour D et al. 2019. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity 50:51317–34.e10
    [Google Scholar]
  95. 95.
    Zhang Q, He Y, Luo N, Patel SJ, Han Y et al. 2019. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell 179:4829–45.e20
    [Google Scholar]
  96. 96.
    Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su M-J et al. 2018. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175:4984–97.e24
    [Google Scholar]
  97. 97.
    Sade-Feldman M, Yizhak K, Bjorgaard SL, Ray JP, de Boer CG et al. 2018. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175:4998–1013.e20
    [Google Scholar]
  98. 98.
    Oh DY, Kwek SS, Raju SS, Li T, McCarthy E et al. 2020. Intratumoral CD4+ T cells mediate anti-tumor cytotoxicity in human bladder cancer. Cell 181:71612–25.e13
    [Google Scholar]
  99. 99.
    Yost KE, Satpathy AT, Wells DK, Qi Y, Wang C et al. 2019. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25:81251–59
    [Google Scholar]
  100. 100.
    Deng Q, Han G, Puebla-Osorio N, Ma MCJ, Strati P et al. 2020. Characteristics of anti-CD19 CAR T-cell infusion products associated with efficacy and toxicity in patients with large B-cell lymphomas. Nat. Med. 26:121878–87
    [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-020722-091857
Loading
/content/journals/10.1146/annurev-biodatasci-020722-091857
Loading

Data & Media loading...

Supplemental Material

Supplementary Data

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error