Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 620

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

Visualization of omics data for systems biology

Open access
Gehlenborg, Nils, O'Donoghue, Seán I, Baliga, Nitin S, Goesmann, Alexander, Hibbs, Matthew A, Kitano, Hiroaki, Kohlbacher, Oliver, Neuweger, Heiko, Schneider, Reinhard, Tenenbaum, Dan, Gavin, Anne-Claude·2010·Nature Methods
DOI 10.1038/nmeth.1436
Open this paper in CoChat — free account

Abstract

High-throughput studies of biological systems are rapidly accumulating a wealth of 'omics'-scale data. Visualization is a key aspect of both the analysis and understanding of these data, and users now have many visualization methods and tools to choose from. The challenge is to create clear, meaningful and integrated visualizations that give biological insight, without being overwhelmed by the intrinsic complexity of the data. In this review, we discuss how visualization tools are being used to help interpret protein interaction, gene expression and metabolic profile data, and we highlight emerging new directions.

Overview

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 · 10.1038/nmeth.1436

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Verification

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

Self-check

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

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

Citations

Cites

Research works printed but not linked: 121

  1. Michal, G. Biochemical Pathways: An Atlas of Biochemistry and Molecular Biology (Wiley, New York, 1998).
  2. Nishizuka, Y. The role of protein kinase C in cell surface signal transduction and tumour promotion. Nature 308, 693–698 (1984). · DOI · Paper page
  3. Levine, M. & Davidson, E.H. Gene regulatory networks for development. Proc. Natl. Acad. Sci. USA 102, 4936–4942 (2005). · DOI · Paper page
  4. Saraiya, P., North, C. & Duca, K. Visualizing biological pathways: requirements analysis, systems evaluation, and research agenda. Inf. Vis. 4, 191–205 (2005). This paper represents one of the first attempts to critically evaluate the requirements for visualization software used in biology. · DOI
  5. Suderman, M. & Hallett, M. Tools for visually exploring biological networks. Bioinformatics 23, 2651–2659 (2007). · DOI · Paper page
  6. Pavlopoulos, G.A.G., Wegener, A.L.A. & Schneider, R.R. A survey of visualization tools for biological network analysis. BioData Min. 1, 12 (2008). · DOI · Paper page
  7. Charbonnier, S., Gallego, O. & Gavin, A.C. The social network of a cell: recent advances in interactome mapping. Biotechnol. Annu. Rev. 14, 1–28 (2008). · DOI
  8. Gavin, A.C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006). · DOI · Paper page
  9. Mathivanan, S. et al. An evaluation of human protein-protein interaction data in the public domain. BMC Bioinformatics 7 (suppl. 5), S19 (2006). · DOI · Paper page
  10. Ma'ayan, A. Network integration and graph analysis in mammalian molecular systems biology. IET Syst. Biol. 2, 206–221 (2008). · DOI
  11. Salwinski, L. et al. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 32 (database issue), D449–D451 (2004). · DOI · Paper page
  12. Prasad, T.S., Kandasamy, K. & Pandey, A. Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology. Methods Mol. Biol. 577, 67–79 (2009). · DOI · Paper page
  13. von Mering, C. et al. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399–403 (2002). · DOI · Paper page
  14. Fruchterman, T.M.J. & Reingold, E.M. Graph drawing by force-directed placement. Software Pract. Exper. 21, 1129–1164 (1991). · DOI · Paper page
  15. Bader, G.D. & Hogue, C.W. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4, 2 (2003). · DOI · Paper page
  16. Kuhner, S. et al. Proteome organization in a genome-reduced bacterium. Science 326, 1235–1240 (2009). · DOI · Paper page
  17. Junker, B.H., Klukas, C. & Schreiber, F. VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics 7, 109 (2006). · DOI · Paper page
  18. Hu, Z. et al. VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res. 37 (web server issue), W115–W121 (2009). · DOI · Paper page
  19. McGuffin, M.J. & Jurisica, I. Interaction techniques for selecting and manipulating subgraphs in network visualizations. IEEE Trans. Vis. Comput. Graph. 15, 937–944 (2009). · doi:10.1109/tvcg.2009.151
  20. Prinz, S. et al. Control of yeast filamentous-form growth by modules in an integrated molecular network. Genome Res. 14, 380–390 (2004). · DOI
  21. Hu, Z. et al. Towards zoomable multidimensional maps of the cell. Nat. Biotechnol. 25, 547–554 (2007). · DOI
  22. Barsky, A., Munzner, T., Gardy, J. & Kincaid, R. Cerebral: visualizing multiple experimental conditions on a graph with biological context. IEEE Trans. Vis. Comput. Graph. 14, 1253–1260 (2008). · DOI
  23. Genc, B. and Dogrusoz, U. A layout algorithm for signaling pathways. Inf. Sci. 176, 135–149 (2006) · doi:10.1016/j.ins.2004.11.006
  24. de Lichtenberg, U., Jensen, L.J., Brunak, S. & Bork, P. Dynamic complex formation during the yeast cell cycle. Science 307, 724–727 (2005). · DOI · Paper page
  25. Kanehisa, M. et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36 (database issue), D480–D484 (2008).
  26. Kuhn, M. et al. STITCH 2: an interaction network database for small molecules and proteins. Nucleic Acids Res. 38 (database issue), D552–D556 (2010). · DOI · Paper page
  27. Quackenbush, J. Computational analysis of microarray data. Nat. Rev. Genet. 2, 418–427 (2001). · DOI · Paper page
  28. Bantscheff, M., Schirle, M., Sweetman, G., Rick, J. & Kuster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017–1031 (2007). · DOI · Paper page
  29. Gstaiger, M. & Aebersold, R. Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat. Rev. Genet. 10, 617–627 (2009). · DOI · Paper page
  30. Westenberg, M.A., van Hijum, S.A.F.T., Kuipers, O.P. & Roerdink, J.B.T.M. Visualizing genome expression and regulatory network dynamics in genomic and metabolic context. Comput. Graph. Forum 27, 887–894 (2008). · DOI
  31. Freeman, T.C. et al. Construction, visualisation, and clustering of transcription networks from microarray expression data. PLOS Comput. Biol. 3, e206 (2007). · DOI · Paper page
  32. Saraiya, P., Lee, P. & North, C. in IEEE Symp. Information Visualization (InfoVis 2005) 225–232 (2005).
  33. Tufte, E.R. The Visual Display of Quantitative Information 2nd edn. (Graphics Press, Cheshire, Connecticut, USA, 2001).
  34. Salomonis, N. et al. GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics 8, 217 (2007). · DOI · Paper page
  35. Neuweger, H. et al. Visualizing post genomics data-sets on customized pathway maps by ProMeTra – aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example. BMC Syst. Biol. 3, 82 (2009). · DOI
  36. Kincaid, R., Kuchinsky, A. & Creech, M. VistaClara: an expression browser plug-in for Cytoscape. Bioinformatics 24, 2112–2114 (2008). · doi:10.1093/bioinformatics/btn368
  37. Lee, H.K., Hsu, A.K., Sajdak, J., Qin, J. & Pavlidis, P. Coexpression analysis of human genes across many microarray data sets. Genome Res. 14, 1085–1094 (2004). · DOI · Paper page
  38. Dunn, W.B. & Ellis, D.I. Metabolomics: current analytical platforms and methodologies. TrAC Trends Anal. Chem. 24, 285–294 (2005). · DOI · Paper page
  39. Scholz, M. & Fiehn, O. Setup X – a public study design database for metabolomic projects. Pac. Symp. Biocomput. 169–180, doi: 10.1142/9789812772435_0017 (2007). · DOI
  40. Thimm, O. et al. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 37, 914–939 (2004). · DOI · Paper page
  41. Paley, S.M. & Karp, P.D. The Pathway Tools cellular overview diagram and omics viewer. Nucleic Acids Res. 34, 3771–3778 (2006). · DOI · Paper page
  42. Tokimatsu, T. et al. KaPPA-view: a web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps. Plant Physiol. 138, 1289–1300 (2005). · DOI · Paper page
  43. Mlecnik, B. et al. PathwayExplorer: web service for visualizing high-throughput expression data on biological pathways. Nucleic Acids Res. 33 (web server issue), W633–W637 (2005) · DOI
  44. Funahashi, A. et al. CellDesigner 3.5: a versatile modeling tool for biochemical networks. Proc. IEEE 96, 1254–1265 (2008). · DOI · Paper page
  45. Sauro, H.M. et al. Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration. OMICS 7, 355–372 (2003). · DOI · Paper page
  46. Demir, E. et al. PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways. Bioinformatics 18, 996–1003 (2002). · DOI · Paper page
  47. Schreiber, F., Dwyer, T., Marriott, K. & Wybrow, M. A generic algorithm for layout of biological networks. BMC Bioinformatics 10, 375 (2009). · DOI
  48. Thomas, J.J. & Cook, K.A. Illuminating the Path: The Research and Development Agenda for Visual Analytics. (National Visual Analytics Center & IEEE, Richland, Washington, USA, 2005).
  49. Saraiya, P., North, C. & Duca, K. An Insight-based methodology for evaluating bioinformatics visualizations. IEEE Trans. Vis. Comput. Graph. 11, 443–456 (2005). · DOI · Paper page
  50. Dwyer, T., Koren, Y. & Marriott, K. IPSEP-COLA: an incremental procedure for separation constraint layout of graphs. IEEE Trans. Vis. Comput. Graph. 12, 821–828 (2006). · doi:10.1109/tvcg.2006.156
  51. Dwyer, T. et al. Exploration of networks using overview+detail with constraint-based cooperative layout. IEEE Trans. Vis. Comput. Graph. 14, 1293–1300 (2008). · doi:10.1109/tvcg.2008.130
  52. Viégas, F.B., Wattenberg, M., van Ham, F., Kriss, J. & McKeon, M. ManyEyes: a site for visualization at internet scale. IEEE Trans. Vis. Comput. Graph. 13, 1121–1128 (2007). · DOI · Paper page
  53. Heer, J., Viégas, F.B. & Wattenberg, M. Voyagers and voyeurs: supporting asynchronous collaborative information visualization. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'07) 1029–1038 (ACM, New York, 2007). · DOI · Paper page
  54. Heer, J. & Agrawala, M. Design considerations for collaborative visual analytics. Inf. Vis. 7, 49–62 (2008). · DOI
  55. Pavlopoulos, G.A. et al. Arena3D: visualization of biological networks in 3D. BMC Syst. Biol. 2, 104 (2008). · doi:10.1186/1752-0509-2-104
  56. Ball, R. & North, C. Realizing embodied interaction for visual analytics through large displays. Comput. Graph. 31, 380–400 (2007). · DOI
  57. Hermjakob, H. et al. The HUPO PSI's molecular interaction format–a community standard for the representation of protein interaction data. Nat. Biotechnol. 22, 177–183 (2004). · DOI · Paper page
  58. Hucka, M. et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003). · DOI · Paper page
  59. Walter, T. et al. Visualization of image data from cells to organisms. Nat. Methods 7, S26–S40 (2010). · DOI · Paper page
  60. O'Donoghue, S.I. et al. Visualization of macromolecular structures. Nat. Methods 7, S42–S55 (2010). · DOI
  61. Nielsen, C.B., Cantor, M., Dubchak, I., Gordon, D. & Wang, T. Visualizing genomes: techniques and challenges. Nat. Methods 7, S5–S15 (2010). · DOI · Paper page
  62. Procter, J.B. et al. Visualization of multiple alignments, phylogenies and gene family evolution. Nat. Methods 7, S16–S25 (2010). · DOI
  63. Burrage, K., Hood, L. & Ragan, M.A. Advanced computing for systems biology. Brief. Bioinform. 7, 390–398 (2006). · DOI
  64. Awad, I.A., Rees, C.A., Hernandez-Boussard, T., Ball, C.A. & Sherlock, G. Caryoscope: an open source Java application for viewing microarray data in a genomic context. BMC Bioinformatics 5, 151 (2004). · DOI
  65. Lau, C. et al. Exploration and visualization of gene expression with neuroanatomy in the adult mouse brain. BMC Bioinformatics 9, 153 (2008). · DOI
  66. Weber, G.H. et al. Visual exploration of three-dimensional gene expression using physical views and linked abstract views. IEEE/ACM Trans. Comput. Biol. Bioinform. 6, 296–309 (2009). · DOI
  67. Mookherjee, N. et al. Modulation of the TLR-mediated inflammatory response by the endogenous human host defense peptide LL-37. J. Immunol. 176, 2455–2464 (2006). · DOI · Paper page
  68. Spellman, P.T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998). · DOI · Paper page
  69. Kuntzer, J. et al. BNDB – the Biochemical Network Database. BMC Bioinformatics 8, 367 (2007). · DOI
  70. Baitaluk, M., Sedova, M., Ray, A. & Gupta, A. BiologicalNetworks: visualization and analysis tool for systems biology. Nucleic Acids Res. 34 (web server issue), W466–W471 (2006). · doi:10.1093/nar/gkl308
  71. Cline, M.S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366–2382 (2007). · DOI · Paper page
  72. Hooper, S.D. & Bork, P. Medusa: a simple tool for interaction graph analysis. Bioinformatics 21, 4432–4433 (2005). · DOI · Paper page
  73. Kao, H.L. & Gunsalus, K.C. Browsing multidimensional molecular networks with the generic network browser (N-Browse). Curr. Protoc. Bioinformatics 9, 9.11.1–9.11.21 (2008).
  74. Brown, K.R. et al. NAViGaTOR: Network Analysis, Visualization and Graphing Toronto. Bioinformatics 25, 3327–3329 (2009). · DOI · Paper page
  75. Kohler, J. et al. Graph-based analysis and visualization of experimental results with ONDEX. Bioinformatics 22, 1383–1390 (2006). · DOI · Paper page
  76. Breitkreutz, B.J., Stark, C. & Tyers, M. Osprey: a network visualization system. Genome Biol. 4, R22 (2003). · DOI · Paper page
  77. Batagelj, V. & Mrvar, A. Pajek – Program for large network analysis. Connections 21, 47–57 (1998).
  78. Forman, J.J., Clemons, P.A., Schreiber, S.L. & Haggarty, S.J. SpectralNET–an application for spectral graph analysis and visualization. BMC Bioinformatics 6, 260 (2005). · DOI
  79. Auber, D. A huge graph visualization framework. in Graph Drawing Software (eds. Mutzel, P. & Jünger, M.) 105–126 (Springer, Heidelberg, Germany, 2004). · doi:10.1007/978-3-642-18638-7_5
  80. Zinovyev, A., Viara, E., Calzone, L. & Barillot, E. BiNoM: a Cytoscape plugin for manipulating and analyzing biological networks. Bioinformatics 24, 876–877 (2008). · DOI
  81. Huttenhower, C., Mehmood, S.O. & Troyanskaya, O.G. Graphle: interactive exploration of large, dense graphs. BMC Bioinformatics 10, 417 (2009). · doi:10.1186/1471-2105-10-417
  82. Longabaugh, W.J., Davidson, E.H. & Bolouri, H. Visualization, documentation, analysis, and communication of large-scale gene regulatory networks. Biochim. Biophys. Acta 1789, 363–374 (2009). · DOI
  83. Streit, M., Lex, A., Kalkusch, M., Zatloukal, K. & Schmalstieg, D. Caleydo: connecting pathways and gene expression. Bioinformatics 25, 2760–2761 (2009). · DOI
  84. Okuda, S. et al. KEGG atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res. 36 (web server issue), W423–W426 (2008). · DOI · Paper page
  85. van Iersel, M.P. et al. Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics 9, 399 (2008). · DOI · Paper page
  86. Holford, M., Li, N., Nadkarni, P. & Zhao, H. VitaPad: visualization tools for the analysis of pathway data. Bioinformatics 21, 1596–1602 (2005). · DOI
  87. Chung, H. J., Kim, M., Park, C. H., Kim, J., and Kim, J. H. ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using scalable vector graphics. Nucleic Acids Res. 32 (web server issue), W460–W464 (2004). · DOI
  88. Weniger, M., Engelmann, J.C. & Schultz, J. Genome Expression Pathway Analysis Tool–analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context. BMC Bioinformatics 8, 179 (2007). · doi:10.1186/1471-2105-8-179
  89. Letunic, I., Yamada, T., Kanehisa, M. & Bork, P. iPath: interactive exploration of biochemical pathways and networks. Trends Biochem. Sci. 33, 101–103 (2008). · DOI · Paper page
  90. Dogrusoz, U. et al. PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization. Bioinformatics 22, 374–375 (2006). · doi:10.1093/bioinformatics/bti776
  91. Santamaria, R., Theron, R. & Quintales, L. BicOverlapper: a tool for bicluster visualization. Bioinformatics 24, 1212–1213 (2008). · doi:10.1093/bioinformatics/btn076
  92. Goncalves, J.P., Madeira, S.C. & Oliveira, A.L. BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data. BMC Res Notes 2, 124 (2009). · DOI
  93. Shamir, R. et al. EXPANDER–an integrative program suite for microarray data analysis. BMC Bioinformatics 6, 232 (2005). · DOI · Paper page
  94. Sturn, A., Quackenbush, J. & Trajanoski, Z. Genesis: cluster analysis of microarray data. Bioinformatics 18, 207–208 (2002). · DOI · Paper page
  95. Hibbs, M.A., Dirksen, N.C., Li, K. & Troyanskaya, O.G. Visualization methods for statistical analysis of microarray clusters. BMC Bioinformatics 6, 115 (2005). · DOI
  96. Seo, J. & Shneiderman, B. Interactively exploring hierarchical clustering results. Computer 35, 80–86 (2002).
  97. Saldanha, A.J. Java Treeview–extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004). · DOI · Paper page
  98. Dietzsch, J., Gehlenborg, N. & Nieselt, K. Mayday – a microarray data analysis workbench. Bioinformatics 22, 1010–1012 (2006). · DOI
  99. Gehlenborg, N., Dietzsch, J. & Nieselt, K. A framework for visualization of microarray data and integrated meta information. Inf. Vis. 4, 164–175 (2005). · doi:10.1057/palgrave.ivs.9500094
  100. Hochheiser, H., Baehrecke, E.H., Mount, S.M. & Shneiderman, B. Dynamic querying for pattern identification in microarray and genomic data. Proc. IEEE Multimedia and Expo Int. Conf. 3, 453–456 (2003).
  101. Kapushesky, M. et al. Expression Profiler: next generation–an online platform for analysis of microarray data. Nucleic Acids Res. 32 (web server issue), W465–W470 (2004). · DOI
  102. Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006). · DOI · Paper page
  103. Brettschneider, J. . Collin, F., Bolstad, B.M. & Speed, T.P. Quality assessment for short oligonucleotide microarray data. Technometrics 50, 241–264 (2008). · DOI
  104. Morgan, M. et al. ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25, 2607–2608 (2009). · DOI · Paper page
  105. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). · DOI · Paper page
  106. Smyth, G.K., Yang, Y.H. & Speed, T. Statistical issues in cDNA microarray data analysis. Methods Mol. Biol. 224, 111–136 (2003).
  107. Nesvizhskii, A.I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4, 787–797 (2007). · DOI · Paper page
  108. Perkins, D.N., Pappin, D.J., Creasy, D.M. & Cottrell, J.S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999). · DOI · Paper page
  109. Li, X.J. et al. A tool to visualize and evaluate data obtained by liquid chromatography-electrospray ionization-mass spectrometry. Anal. Chem. 76, 3856–3860 (2004). · DOI
  110. Sturm, M. & Kohlbacher, O. TOPPView: an open-source viewer for mass spectrometry data. J. Proteome Res. 8, 3760–3763 (2009). · DOI
  111. Kopka, J. Current challenges and developments in GC-MS based metabolite profiling technology. J. Biotechnol. 124, 312–322 (2006). · DOI · Paper page
  112. Broeckling, C.D. et al. Metabolic profiling of Medicago truncatula cell cultures reveals the effects of biotic and abiotic elicitors on metabolism. J. Exp. Bot. 56, 323–336 (2005). · DOI · Paper page
  113. Beckonert, O. et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 2692–2703 (2007). · DOI · Paper page
  114. Lindon, J.C. & Nicholson, J.K. Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics. Annu. Rev. Anal. Chem. 1, 45–69 (2008). · DOI · Paper page
  115. Xia, J., Bjorndahl, T.C., Tang, P. & Wishart, D.S. MetaboMiner–semi-automated identification of metabolites from 2D NMR spectra of complex biofluids. BMC Bioinformatics 9, 507 (2008). · DOI · Paper page
  116. Hotelling, H. Analysis of complex statistical variables into principal components. J. Educ. Psychol. 24, 417–441 (1933). · DOI · Paper page
  117. Kruskal, J. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29, 1–26 (1964). · DOI · Paper page
  118. Venna, J. & Kaski, S. Comparison of visualization methods for an atlas of gene expression data sets. Inf. Vis. 6, 139–154 (2007). · DOI
  119. Inselberg, A. The plane with parallel coordinates. Vis. Comput. 1, 69–91 (1985). · DOI · Paper page
  120. Wilkinson, L. & Friendly, M. The history of the cluster heat map. Am. Stat. 63, 179–184 (2009). · DOI · Paper page
  121. Weinstein, J.N. Biochemistry. a postgenomic visual icon. Science 319, 1772–1773 (2008). · doi:10.1126/science.1151888

Cited by

See all 620

Metadata

Authors
Gehlenborg, N., O'Donoghue, S. I., Baliga, N. S., Goesmann, A., Hibbs, M. A., Kitano, H., Kohlbacher, O., Neuweger, H., Schneider, R., Tenenbaum, D., & Gavin, A.-C.
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2010-03
Volume
7
Issue
S3
Pages
S56-S68
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
DOI
10.1038/nmeth.1436
OpenAlex
W2023098599
Cited by
620
References
17
FWCI
26.94
Type
journal article
Language
en
Open access
green
Licence
www.springer.com/tdm (tdm)

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

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.