Shinyheatmap: Ultra fast low memory heatmap web interface for big data genomics

PLoS ONE

Volumn 12, Issue 5, 2017, Pages

  Khomtchouk, Bohdan B ^a   Hennessy, James R ^b   Wahlestedt, Claes ^a  

^a UNIVERSITY OF MIAMI MILLER SCHOOL OF MEDICINE   (United States)

^b University of Miami   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER INTERFACE; GENOMICS; MEMORY; SKILL; VELOCITY; WEB BROWSER; ALGORITHM; GENE EXPRESSION; GENE EXPRESSION PROFILING; HUMAN; INTERNET; PROCEDURES; SOFTWARE;

ALGORITHMS; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENOMICS; HUMANS; INTERNET; SOFTWARE;

EID: 85018909188     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0176334     Document Type: Article

References (51)

1. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, et al.: TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003, 34(2): 374-378. PMID: 12613259
2. Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP: GenePattern 2.0. Nat Genet. 2006, 38(5): 500-501. https://doi.org/10.1038/ng0506-500 PMID: 16642009
3. Verhaak RGW, Sanders MA, Bijl MA, Delwel R, Horsman S, Moorhouse MJ, et al.: HeatMapper: powerful combined visualization of gene expression profile correlations, genotypes, phenotypes and sample characteristics. BMC Bioinformatics. 2006, 7:337. https://doi.org/10.1186/1471-2105-7-337 PMID: 16836741
4. Qlucore Omics Explorer: The D.I.Y Bioinformatics Software. http://www.qlucore.com.
5. Gould J: GENE-E software hosted at the Broad Institute. http://www.broadinstitute.org/cancer/software/GENE-E/.
6. Chu VT, Gottardo R, Raftery AE, Bumgarner RE, Yeung KY: MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis. Genome Biology. 2008, 9: R118. https://doi.org/10.1186/gb-2008-9-7-r118 PMID: 18652698
7. Howe EA, Sinha R, Schlauch D, Quackenbush J: RNA-Seq analysis in MeV. Bioinformatics. 2011, 27(22): 3209-3210. https://doi.org/10.1093/bioinformatics/btr490 PMID: 21976420
8. Khomtchouk BB, Van Booven DJ, Wahlestedt C: HeatmapGenerator: high performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline. Source Code for Biology and Medicine. 2014, 9(1): 1-6.
9. Kolde R: pheatmap: Pretty Heatmaps. 2015. R package version 1.0.8. https://CRAN.R-project.org/package=pheatmap.
10. Saldanha AJ: Java Treeview-extensive visualization of microarray data. Bioinformatics. 2004, 20(17): 3246-3248. https://doi.org/10.1093/bioinformatics/bth349 PMID: 15180930
11. Caraux G, Pinloche S: Permutmatrix: A Graphical Environment to Arrange Gene Expression rofiles in Optimal Linear Order. Bioinformatics. 2005, 21: 1280-1281. https://doi.org/10.1093/bioinformatics/bti141 PMID: 15546938
12. Kibbey C, Calvet A: Molecular Property eXplorer: a novel approach to visualizing SAR using tree-maps and heatmaps. J Chem Inf Model. 2005, 45(2): 523-532. https://doi.org/10.1021/ci0496954 PMID: 15807518
13. Wu HM, Tien YJ, Chen CH: GAP: A Graphical Environment for Matrix Visualization and Cluster Analysis. Computational Statistics and Data Analysis. 2010, 54: 767-778.
14. Perez-Llamas C, Lopez-Bigas N: Gitools: analysis and visualisation of genomic data using interactive heat-maps. PLoS One. 2011, 6: e19541. https://doi.org/10.1371/journal.pone.0019541 PMID: 21602921
15. Sikuta C, Bartnk P, Svozil D: InCHlib-interactive cluster heatmap for web applications Journal of Cheminformatics. 2014, 6(44): 1-9.
16. Turkay C, Lex A, Streit M, Pfister H, Hauser H: Characterizing cancer subtypes using dual analysis in Caleydo StratomeX. IEEE Comput Graph Appl. 2014, 34(2): 38-47. https://doi.org/10.1109/MCG. 2014.1 PMID: 24808198
17. Babicki S, Arndt D, Marcu A, Liang Y, Grant JR, Maciejewski A, et al.: Heatmapper: web-enabled heat mapping for all. Nucleic Acids Research 2016, pii: gkw419. [Epub ahead of print].
18. Khomtchouk BB, Hennessy JR, Wahlestedt C: MicroScope: ChIP-seq and RNA-seq software analysis suite for gene expression heatmaps. BMC Bioinformatics. 2016; 17(390). https://doi.org/10.1186/s12859-016-1260-x PMID: 27659774
19. Deu-Pons J, Schroeder MP, Lopez-Bigas N. jHeatmap: an interactive heatmap viewer for the web. Bioinformatics. 2014. https://doi.org/10.1093/bioinformatics/btu094 PMID: 24567544
20. Yachdav G, Hecht M, Pasmanik-Chor M, Yeheskel A, Rost B: HeatMapViewer: interactive display of 2D data in biology [v1; ref status: indexed, http://f1000r.es/2u6] F1000Research 2014, 3:48. https://doi.org/10.12688/f1000research.3-48.v1 PMID: 24860644
21. Pavlopoulos GA, Oulas A, Iacucci E, Sifrim A, Moreau Y, Schneider R, et al.: Unraveling genomic variation from next generation sequencing data. BioData Mining. 2013, 6:13. https://doi.org/10.1186/1756-0381-6-13 PMID: 23885890
22. Pavlopoulos GA, Malliarakis D, Papanikolaou N, Theodosiou T, Enright AJ, Iliopoulos I: Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future. GigaScience 2015, 4:38. https://doi.org/10.1186/s13742-015-0077-2 PMID: 26309733
23. Metsalu T, Vilo J: ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Research. 2015, 43(W1): W566-570. https://doi.org/10.1093/nar/gkv468 PMID: 25969447
24. Kolde R: pheatmap: Pretty Heatmaps. 2015. Page 4:ftp://cran.r-project.org/pub/R/web/packages/pheatmap/pheatmap.pdf.
25. SO, 2011. How can I make a heatmap with a large matrix? http://stackoverflow.com/questions/5667107/how-can-i-make-A-heatmap-with-A-large-matrix.
26. SO, 2013. D3: How to show large dataset. http://stackoverflow.com/questions/18244995/d3-how-toshow-large-dataset.
27. SO, 2014. How to draw heatmap with huge data. http://stackoverflow.com/questions/23297616/howto-draw-heatmap-with-huge-data.
28. SO, 2014. clustering very large dataset in R. http://stackoverflow.com/questions/21984940/clusteringvery-large-dataset-in-r.
29. Google Groups, 2012. Heat map with 500,300 nodes. https://groups.google.com/forum/m/#!topic/d3-js/wVWvwa-YkFE.
30. Mango Information Systems, 2013. Pre-render d3.js charts at server side. https://mango-is.com/blog/engineering/pre-render-d3-js-charts-At-server-side/.
31. vida.io, 2014. BigQuery Big Data Visualization With D3.js. http://blog.vida.io/2014/07/06/bigquery-bigdata-visualization-with-d3-dot-js/.
32. Biostars 2014. How to plot the heatmap of gene expression for very large data set? https://www. biostars.org/p/104976/
33. Sood S, Szkop KJ, Nakhuda A, Gallagher IJ, Murie C, Brogan RJ, et al.: iGEMS: an integrated model for identification of alternative exon usage events Nucleic Acids Research. 2016, 44(11): e109. https://doi.org/10.1093/nar/gkw263 PMID: 27095197
34. Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al.: Massively parallel digital transcriptional profiling of single cells. Nature Communications. 2017, 8:14049. https://doi.org/10.1038/ncomms14049 PMID: 28091601
35. 10x Genomics Inc., 2017. Single cell datasets. https://support.10xgenomics.com/single-cell/datasets.
36. Galili T: heatmaply: Interactive Heat Maps Using'plotly'. 2016. R package version 0.6.0. https://CRAN. R-project.org/package=heatmaply.
37. Galili T: heatmaply: Interactive Heat Maps Using'plotly'. 2016. R package version 0.6.0. https://github. com/talgalili/heatmaply/issues/20.
38. Chang W, Cheng J, Allaire JJ, Xie Y, McPherson J, RStudio, et al.: shiny: Web Application Framework for R. 2015. R package version 0.12.2.
39. Dowle M, Srinivasan A, Short T, Lianoglou S, Saporta R, Antonyan E: data.table: Extension of Data. frame. 2015. R package version 1.9.6.
40. Warnes GR, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, et al.: gplots: Various R Programming Tools for Plotting Data. 2016. R package version 3.0.1. https://CRAN.R-project.org/package=gplots.
41. Sakai R, Winand R, Verbeiren T, Moere AV, Aerts J: dendsort: modular leaf ordering methods for dendrogram representations in R. [version 1; referees: 2 approved] F1000 Research. 2014, 3(177): 3246-3248.
42. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2016. https://rweb.stat.umn.edu/R/library/stats/html/hclust.html.
43. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2016. https://rweb.stat.umn.edu/R/library/stats/html/hclust.html.
44. Quackenbush J: Computational analysis of microarray data. Nature Reviews Genetics. 2001, 2(6): 418-27. https://doi.org/10.1038/35076576 PMID: 11389458
45. Tan P, Kumar V, Steinbach M: Introduction to data mining. Boston: Pearson Addison Wesley, 1st ed edition. 2005.
46. Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning. Springer Series Statistics. 2009.
47. Cheng J, Galili T, RStudio Inc, Bostock M, Palmer J: d3heatmap: Interactive Heat Maps Using 'htmlwidgets' and 'D3.js'. 2015. R package version 0.6.1.
48. Sievert C, Parmer C, Hocking T, Chamberlain S, Ram K, Corvellec M, et al.: plotly: Create Interactive Web Graphics via'plotly.js'. 2016. R package version 3.6.0. https://CRAN.R-project.org/package=plotly.
49. Plotly Technologies Inc.: Collaborative data science. Plotly Technologies Inc. Montreal, QC. 2015, https://plot.ly.
50. Khomtchouk BB: fastheatmap: high performance interactive heatmap software. 2016-2017. https://github.com/Bohdan-Khomtchouk/fastheatmap.
51. Love M, Anders S, Kim V, Huber W: RNA-seq workflow: gene-level exploratory analysis and differential expression. 2016, http://www.bioconductor.org/help/workflows/rnaseqGene/.