CoChat
Sign Up
Cited by 159Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Phylogenetic Studies

Computational approaches and challenges in spatial transcriptomics

Open access
Fang, Shuangsang, Chen, Bichao, Zhang, Yong, Sun, Haixi, Liu, Longqi, Liu, Shiping, Li, Yuxiang, Xu, Xun·2023·Genomics, Proteomics & Bioinformatics
DOI 10.1016/j.gpb.2022.10.001PMC PMC10372921
Open this paper in CoChat — free accountOpen PDF

Abstract

The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.gpb.2022.10.001

Cites

22 of 22 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Rapid development of spatial transcriptomics has provided information on local tissue contexts and adjacent cellular interactions.

    “The rapid development of spatial transcriptomics has provided information on local tissue contexts and adjacent cellular interactions [122,123].”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [123]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 104

  1. [2]Chen K.H. Boettiger A.N. Moffitt J.R. Wang S. Zhuang X. Spatially resolved, highly multiplexed RNA profiling in single cells Science 348 2015 aaa6090 25858977 PMC4662681
  2. [3]Lubeck E. Coskun A.F. Zhiyentayev T. Ahmad M. Cai L. Single-cell in situ RNA profiling by sequential hybridization Nat Methods 11 2014 360 361 24681720 PMC4085791
  3. [4]Eng C.H.L. Lawson M. Zhu Q. Dries R. Koulena N. Takei Y. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+ Nature 568 2019 235 239 30911168 PMC6544023
  4. [5]Lee J.H. Daugharthy E.R. Scheiman J. Kalhor R. Ferrante T.C. Terry R. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues Nat Protoc 10 2015 442 458 25675209 PMC4327781
  5. [7]Rodriques S.G. Stickels R.R. Goeva A. Martin C.A. Murray E. Vanderburg C.R. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution Science 363 2019 1463 1467 30923225 PMC6927209
  6. [8]Vickovic S. Eraslan G. Salmen F. Klughammer J. Stenbeck L. Schapiro D. High-definition spatial transcriptomics for in situ tissue profiling Nat Methods 16 2019 987 990 31501547 PMC6765407
  7. [9]Liu Y. Yang M. Deng Y. Su G. Enninful A. Guo C.C. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue Cell 183 2020 1665 1681 33188776 PMC7736559
  8. [10]Toki M.I. Merritt C.R. Wong P.F. Smithy J.W. Kluger H.M. Syrigos K.N. High-plex predictive marker discovery for melanoma immunotherapy-treated patients using digital spatial profiling Clin Cancer Res 25 2019 5503 5512 31189645 PMC6744974
  9. [11]Cho C.S. Xi J. Si Y. Park S.R. Hsu J.E. Kim M. Microscopic examination of spatial transcriptome using Seq-Scope Cell 184 2021 3559 3572 34115981 PMC8238917
  10. [14]Lewis S.M. Asselin-Labat M.L. Nguyen Q. Berthelet J. Tan X. Wimmer V.C. Spatial omics and multiplexed imaging to explore cancer biology Nat Methods 18 2021 997 1012 34341583
  11. [15]Zhuang X. Spatially resolved single-cell genomics and transcriptomics by imaging Nat Methods 18 2021 18 22 33408406 PMC9805800
  12. [16]Maniatis S. Petrescu J. Phatnani H. Spatially resolved transcriptomics and its applications in cancer Curr Opin Genet Dev 66 2021 70 77 33434721 PMC7969406
  13. [17]Asp M. Bergenstrahle J. Lundeberg J. Spatially resolved transcriptomes—next generation tools for tissue exploration Bioessays 42 2020 e1900221 32363691
  14. [18]Crosse E.I. Gordon-Keylock S. Rybtsov S. Binagui-Casas A. Felchle H. Nnadi N.C. Multi-layered spatial transcriptomics identify secretory factors promoting human hematopoietic stem cell development Cell Stem Cell 27 2020 822 839 32946788 PMC7671940
  15. [19]Hildebrandt F. Andersson A. Saarenpää S. Larsson L. Hul N.V. Kanatani S. Spatial transcriptomics to define transcriptional patterns of zonation and structural components in the mouse liver Nat Commun 12 2021 7046 34857782 PMC8640072
  16. [20]Baccin C. Al-Sabah J. Velten L. Helbling P.M. Grunschlager F. Hernandez-Malmierca P. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization Nat Cell Biol 22 2020 38 48 31871321 PMC7610809
  17. [21]Berglund E. Maaskola J. Schultz N. Friedrich S. Marklund M. Bergenstrahle J. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity Nat Commun 9 2018 2419 29925878 PMC6010471
  18. [23]Dobin A. Davis C.A. Schlesinger F. Drenkow J. Zaleski C. Jha S. STAR: ultrafast universal RNA-seq aligner Bioinformatics 29 2013 15 21 23104886 PMC3530905
  19. [24]Srivastava A. Malik L. Smith T. Sudbery I. Patro R. Alevin efficiently estimates accurate gene abundances from dscRNA-seq data Genome Biol 20 2019 65 30917859 PMC6437997
  20. [25]Melsted P. Ntranos V. Pachter L. The barcode, UMI, set format and BUStools Bioinformatics 35 2019 4472 4473 31073610
  21. [26]Melsted P. Booeshaghi A.S. Liu L. Gao F. Lu L. Min K.H.J. Modular, efficient and constant-memory single-cell RNA-seq preprocessing Nat Biotechnol 39 2021 813 818 33795888
  22. [28]Srivastava A. Sarkar H. Gupta N. Patro R. RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes Bioinformatics 32 2016 i192 i200 27307617 PMC4908361
  23. [29]Wu D.C. Yao J. Ho K.S. Lambowitz A.M. Wilke C.O. Limitations of alignment-free tools in total RNA-seq quantification BMC Genomics 19 2018 510 29969991 PMC6042521
  24. [30]Navarro J.F. Lundeberg J. Stahl P.L. ST viewer: a tool for analysis and visualization of spatial transcriptomics datasets Bioinformatics 35 2019 1058 1060 30875427
  25. [31]Dries R. Zhu Q. Dong R. Eng C.H.L. Li H. Liu K. Giotto, a toolbox for integrative analysis and visualization of spatial expression data Genome Biol 22 2021 78 33685491 PMC7938609
  26. [32]Zhang Y. Patel K. Endrawis T. Bowers A. Sun Y. A FASTQ compressor based on integer-mapped k-mer indexing for biologist Gene 579 2016 75 81 26743127
  27. [33]Xing Y. Li G. Wang Z. Feng B. Song Z. Wu C. GTZ: a fast compression and cloud transmission tool optimized for FASTQ files BMC Bioinformatics 18 2017 549 29297296 PMC5751770
  28. [34]Fan Z. Chen R. Chen X. SpatialDB: a database for spatially resolved transcriptomes Nucleic Acids Res 48 2020 D233 D237 31713629 PMC7145543
  29. [35]Schiller H.B. Montoro D.T. Simon L.M. Rawlins E.L. Meyer K.B. Strunz M. The Human Lung Cell Atlas: a high-resolution reference map of the human lung in health and disease Am J Respir Cell Mol Biol 61 2019 31 41 30995076 PMC6604220
  30. [36]Ortiz C. Navarro J.F. Jurek A. Märtin A. Lundeberg J. Meletis K. Molecular atlas of the adult mouse brain Sci Adv 6 2020 eabb3446 32637622 PMC7319762
  31. [38]Asp M. Giacomello S. Larsson L. Wu C. Furth D. Qian X. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart Cell 179 2019 1647 1660 31835037
  32. [39]Franzen O. Gan L.M. Bjorkegren J.L.M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data Database 2019 2019 baz046 30951143 PMC6450036
  33. [40]Svensson V. da Veiga B.E. Pachter L. A curated database reveals trends in single-cell transcriptomics Database 2020 2020 baaa073 33247933 PMC7698659
  34. [41]Cao Y. Zhu J. Jia P. Zhao Z. scRNASeqDB: a database for RNA-Seq based gene expression profiles in human single cells Genes (Basel) 8 2017 368 29206167 PMC5748686
  35. [42]Cao Z.J. Wei L. Lu S. Yang D.C. Gao G. Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST Nat Commun 11 2020 3458 32651388 PMC7351785
  36. [43]Srivastava D. Iyer A. Kumar V. Sengupta D. Cell AtlasSearch: a scalable search engine for single cells Nucleic Acids Res 46 2018 W141 W147 29788498 PMC6030823
  37. [44]Chen S. Luo Y. Gao H. Li F. Chen Y. Li J. hECA: the cell-centric assembly of a cell atlas iScience 25 2022 104318 35602947 PMC9114628
  38. [45]Li M. Zhang X. Ang K.S. Ling J. Sethi R. Lee N.Y.S. DISCO: a database of deeply integrated human single-cell omics data Nucleic Acids Res 50 2022 D596 D602 34791375 PMC8728243
  39. [46]Van den Berge K. Perraudeau F. Soneson C. Love M.I. Risso D. Vert J.P. Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications Genome Biol 19 2018 24 29478411 PMC6251479
  40. [47]Stuart T. Butler A. Hoffman P. Hafemeister C. Papalexi E. Mauck W.M. 3rd Comprehensive integration of single-cell data Cell 177 2019 1888 1902 31178118 PMC6687398
  41. [48]Pham D. Tan X. Xu J. Grice L.F. Lam P.Y. Raghubar A. stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues bioRxiv 2020 125658
  42. [51]Lytal N. Ran D. An L. Normalization methods on single-cell RNA-seq data: an empirical survey Front Genet 11 2020 41 32117453 PMC7019105
  43. [52]Risso D. Perraudeau F. Gribkova S. Dudoit S. Vert J.P. A general and flexible method for signal extraction from single-cell RNA-seq data Nat Commun 9 2018 284 29348443 PMC5773593
  44. [55]Cole M.B. Risso D. Wagner A. DeTomaso D. Ngai J. Purdom E. Performance assessment and selection of normalization procedures for single-cell RNA-seq Cell Syst 8 2019 315 328 31022373 PMC6544759
  45. [56]Welch J.D. Kozareva V. Ferreira A. Vanderburg C. Martin C. Macosko E.Z. Single-cell multi-omic integration compares and contrasts features of brain cell identity Cell 177 2019 1873 1887 31178122 PMC6716797
  46. [60]Shengquan C. Boheng Z. Xiaoyang C. Xuegong Z. Rui J. stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics Bioinformatics 37 2021 i299 i307 34252941 PMC8336594
  47. [61]Hou W. Ji Z. Ji H. Hicks S.C. A systematic evaluation of single-cell RNA-sequencing imputation methods Genome Biol 21 2020 218 32854757 PMC7450705
  48. [62]Zhao E. Stone M.R. Ren X. Guenthoer J. Smythe K.S. Pulliam T. Spatial transcriptomics at subspot resolution with BayesSpace Nat Biotechnol 39 2021 1375 1384 34083791 PMC8763026
  49. [63]Kleshchevnikov V. Shmatko A. Dann E. Aivazidis A. King H.W. Li T. Cell2location maps fine-grained cell types in spatial transcriptomics Nat Biotechnol 40 2022 661 671 35027729
  50. [64]Park J. Choi W. Tiesmeyer S. Long B. Borm L.E. Garren E. Cell segmentation-free inference of cell types from in situ transcriptomics data Nat Commun 12 2021 3545 34112806 PMC8192952
  51. [65]Qian X. Harris K.D. Hauling T. Nicoloutsopoulos D. Munoz-Manchado A.B. Skene N. Probabilistic cell typing enables fine mapping of closely related cell types in situ Nat Methods 17 2020 101 106 31740815 PMC6949128
  52. [66]Littman R. Hemminger Z. Foreman R. Arneson D. Zhang G. Gomez-Pinilla F. Joint cell segmentation and cell type annotation for spatial transcriptomics Mol Syst Biol 17 2021 e10108 34057817 PMC8166214
  53. [67]Stringer C. Wang T. Michaelos M. Pachitariu M. Cellpose: a generalist algorithm for cellular segmentation Nat Methods 18 2021 100 106 33318659
  54. [68]Sommer C. Straehle C. Kothe U. Hamprecht F.A. Ilastik: interactive learning and segmentation toolkit IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2011 2011 230 233
  55. [69]Lamprecht M.R. Sabatini D.M. Carpenter A.E. Cell Profiler: free, versatile software for automated biological image analysis Biotechniques 42 2007 71 75 17269487
  56. [71]Bao F. Deng Y. Wan S. Shen S.Q. Wang B. Dai Q. Integrative spatial analysis of cell morphologies and transcriptional states with MUSE Nat Biotechnol 40 2022 1200 1209 35347329
  57. [72]Singhal V. Chou N. Lee J. Liu J. Chock W.K. Lin L. BANKSY: a spatial omics algorithm that unifies cell type clustering and tissue domain segmentation bioRxiv 2022 488259
  58. [74]Fu H. Xu H. Chong K. Li M. Ang K.S. Lee H.K. Unsupervised spatially embedded deep representation of spatial transcriptomics bioRxiv 2021 448542 PMC10790257 38217035
  59. [75]Cable D.M. Murray E. Zou L.S. Goeva A. Macosko E.Z. Chen F. Robust decomposition of cell type mixtures in spatial transcriptomics Nat Biotechnol 40 2021 517 526 33603203 PMC8606190
  60. [76]Elosua-Bayes M. Nieto P. Mereu E. Gut I. Heyn H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes Nucleic Acids Res 49 2021 e50 33544846 PMC8136778
  61. [77]Dong R. Yuan G.C. SpatialDWLS: accurate deconvolution of spatial transcriptomic data Genome Biol 22 2021 145 33971932 PMC8108367
  62. [78]Petukhov V. Soldatov R.A. Khodosevich K. Kharchenko P.V. Bayesian segmentation of spatially resolved transcriptomics data bioRxiv 2020 326777
  63. [79]Wei R. He S. Bai S. Sei E. Hu M. Thompson A. Spatial charting of single-cell transcriptomes in tissues Nat Biotechnol 40 2022 1190 1199 35314812 PMC9673606
  64. [80]Chang Y. He F. Wang J. Chen S. Li J. Liu J. Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning Comput Struct Biotechnol J 20 2022 4600 4617 36090815 PMC9440291
  65. [81]Edsgard D. Johnsson P. Sandberg R. Identification of spatial expression trends in single-cell gene expression data Nat Methods 15 2018 339 342 29553578 PMC6314435
  66. [83]Sun S. Zhu J. Zhou X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies Nat Methods 17 2020 193 200 31988518 PMC7233129
  67. [84]Kats I. Vento-Tormo R. Stegle O. SpatialDE2: fast and localized variance component analysis of spatial transcriptomics bioRxiv 2021 466045
  68. [85]Wu Y. Hu Q. Wang S. Liu C. Shan Y. Guo W. Highly Regional Genes: graph-based gene selection for single-cell RNA-seq data J Genet Genomics 49 2022 891 899 35144027
  69. [86]Francis K. Palsson B.O. Effective intercellular communication distances are determined by the relative time constants for cyto/chemokine secretion and diffusion Proc Natl Acad Sci U S A 94 1997 12258 12262 9356436 PMC24899
  70. [88]Cang Z. Nie Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data Nat Commun 11 2020 2084 32350282 PMC7190659
  71. [89]Yuan Y. Bar-Joseph Z. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data Genome Biol 21 2020 300 33303016 PMC7726911
  72. [90]Efremova M. Vento-Tormo M. Teichmann S.A. Vento-Tormo R. Cell PhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand-receptor complexes Nat Protoc 15 2020 1484 1506 32103204
  73. [91]Jin S. Guerrero-Juarez C.F. Zhang L. Chang I. Ramos R. Kuan C.H. Inference and analysis of cell–cell communication using Cell Chat Nat Commun 12 2021 1088 33597522 PMC7889871
  74. [92]Browaeys R. Saelens W. Saeys Y. NicheNet: modeling intercellular communication by linking ligands to target genes Nat Methods 17 2020 159 162 31819264
  75. [93]Lummertz da Rocha E. Malleshaiah M. Trajectory algorithms to infer stem cell fate decisions Methods Mol Biol 1975 2019 193 209 31062311
  76. [94]Mantri M. Scuderi G.J. Abedini-Nassab R. Wang M.F.Z. McKellar D. Shi H. Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis Nat Commun 12 2021 1771 33741943 PMC7979764
  77. [95]Saelens W. Cannoodt R. Todorov H. Saeys Y. A comparison of single-cell trajectory inference methods Nat Biotechnol 37 2019 547 554 30936559
  78. [97]Wolf F.A. Hamey F.K. Plass M. Solana J. Dahlin J.S. Gottgens B. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells Genome Biol 20 2019 59 30890159 PMC6425583
  79. [98]Street K. Risso D. Fletcher R.B. Das D. Ngai J. Yosef N. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics BMC Genomics 19 2018 477 29914354 PMC6007078
  80. [99]Cannoodt R. Saelens W. Sichien D. Tavernier S. Janssens S. Guilliams M. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development bioRxiv 2016 079509
  81. [100]Wang L. Zhang Q. Qin Q. Trasanidis N. Vinyard M. Chen H. Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate Curr Opin Syst Biol 26 2021 1 11 33997529 PMC8117397
  82. [102]Li Y. Xu Q. Wu D. Chen G. Exploring additional valuable information from single-cell RNA-seq data Front Cell Dev Biol 8 2020 593007 33335900 PMC7736616
  83. [103]Abdelaal T, Lelieveldt BPF, Reinders MJT, Mahfouz A. SIRV: spatial inference of RNA velocity at the single-cell resolution. bioRxiv 2021;453774. PMC11302586 39108639
  84. [104]La Manno G. Soldatov R. Zeisel A. Braun E. Hochgerner H. Petukhov V. RNA velocity of single cells Nature 560 2018 494 498 30089906 PMC6130801
  85. [105]Bergen V. Lange M. Peidli S. Wolf F.A. Theis F.J. Generalizing RNA velocity to transient cell states through dynamical modeling Nat Biotechnol 38 2020 1408 1414 32747759
  86. [106]Qiu X. Zhang Y. Martin-Rufino J.D. Weng C. Hosseinzadeh S. Yang D. Mapping transcriptomic vector fields of single cells Cell 185 2022 690 711 35108499 PMC9332140
  87. [107]Wang Q. Ding S.L. Li Y. Royall J. Feng D. Lesnar P. The Allen Mouse Brain Common Coordinate Framework: a 3D reference atlas Cell 181 2020 936 953 32386544 PMC8152789
  88. [108]Shen E.H. Overly C.C. Jones A.R. The Allen Human Brain Atlas: comprehensive gene expression mapping of the human brain Trends Neurosci 35 2012 711 714 23041053
  89. [109]de Bakker B.S. de Jong K.H. Hagoort J. de Bree K. Besselink C.T. de Kanter F.E.C. An interactive three-dimensional digital atlas and quantitative database of human development Science 354 2016 aag0053 27884980
  90. [110]Markov N.T. Ercsey-Ravasz M.M. Ribeiro Gomes A.R. Lamy C. Magrou L. Vezoli J. A weighted and directed interareal connectivity matrix for macaque cerebral cortex Cereb Cortex 24 2014 17 36 23010748 PMC3862262
  91. [111]Zeira R. Land M. Strzalkowski A. Raphael B.J. Alignment and integration of spatial transcriptomics data Nat Methods 19 2022 567 575 35577957 PMC9334025
  92. [112]Bergenstrahle J. Larsson L. Lundeberg J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows BMC Genomics 21 2020 482 32664861 PMC7386244
  93. [113]Argelaguet R. Cuomo A.S.E. Stegle O. Marioni J.C. Computational principles and challenges in single-cell data integration Nat Biotechnol 39 2021 1202 1215 33941931
  94. [114]Korsunsky I. Millard N. Fan J. Slowikowski K. Zhang F. Wei K. Fast, sensitive and accurate integration of single-cell data with Harmony Nat Methods 16 2019 1289 1296 31740819 PMC6884693
  95. [115]Gao C. Liu J. Kriebel A.R. Preissl S. Luo C. Castanon R. Iterative single-cell multi-omic integration using online learning Nature Biotechnol 39 2021 1000 1007 33875866 PMC8355612
  96. [116]Argelaguet R. Arnol D. Bredikhin D. Deloro Y. Velten B. Marioni J.C. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data Genome Biol 21 2020 111 32393329 PMC7212577
  97. [117]Welch J.D. Hartemink A.J. Prins J.F. MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics Genome Biol 18 2017 138 28738873 PMC5525279
  98. [118]Liu J. Huang Y. Singh R. Vert J.P. Noble W.S. Jointly embedding multiple single-cell omics measurements Algorithms Bioinform 143 2019 10 34632462 PMC8496402
  99. [120]Schachter J. Ribas A. Long G.V. Arance A. Grob J.J. Mortier L. Pembrolizumab versus ipilimumab for advanced melanoma: final overall survival results of a multicentre, randomised, open-label phase 3 study (KEYNOTE-006) Lancet 390 2017 1853 1862 28822576
  100. [121]Robert C. Schachter J. Long G.V. Arance A. Grob J.J. Mortier L. Pembrolizumab versus ipilimumab in advanced melanoma N Engl J Med 372 2015 2521 2532 25891173
  101. [122]Robert C. Ribas A. Schachter J. Arance A. Grob J.J. Mortier L. Pembrolizumab versus ipilimumab in advanced melanoma (KEYNOTE-006): post-hoc 5-year results from an open-label, multicentre, randomised, controlled, phase 3 study Lancet Oncol 20 2019 1239 1251 31345627
  102. [123]Hodi F.S. O’Day S.J. McDermott D.F. Weber R.W. Sosman J.A. Haanen J.B. Improved survival with ipilimumab in patients with metastatic melanoma N Engl J Med 363 2010 711 723 20525992 PMC3549297
  103. [124]Zugazagoitia J. Gupta S. Liu Y. Fuhrman K. Gettinger S. Herbst R.S. Biomarkers associated with beneficial PD-1 checkpoint blockade in non-small cell lung cancer (NSCLC) identified using high-plex digital spatial profiling Clin Cancer Res 26 2020 4360 4368 32253229 PMC7442721
  104. [125]Taube J.M. Galon J. Sholl L.M. Rodig S.J. Cottrell T.R. Giraldo N.A. Implications of the tumor immune microenvironment for staging and therapeutics Mod Pathol 31 2018 214 234 29192647 PMC6132263

Cited by

Sorted by citations

Authors
Fang, S., Chen, B., Zhang, Y., Sun, H., Liu, L., Liu, S., Li, Y., & Xu, X.
Affiliations
  • BGI Group (China) (CN)
  • BGI-Shenzhen , Shenzhen, 518083 , China
Corresponding author
  • Li, Yuxiang
  • Xu, Xun
ORCID
Venue
Genomics, Proteomics & Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2022-10-14
Volume
21
Issue
1
Pages
24-47
ISSN
  • 1672-0229 (ISSN-L)
  • 2210-3244
DOI
10.1016/j.gpb.2022.10.001
PMC
PMC10372921
OpenAlex
W4306194402
Cited by
159
References
22
Citations by year
2026: 50 · 2025: 52 · 2024: 42 · 2023: 14 · 2022: 1
FWCI
2.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Genomics Proteomics & Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
spatial transcriptomics, computational methods, spatial gene expression, batch effect correction, data quality, multi-omics integration, single-cell analysis, algorithm development
MeSH terms
Multiomics, Algorithms, Gene Expression Profiling, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review