The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Complex heatmap visualization
Open accessAbstract
Heatmap is a widely used statistical visualization method on matrix‐like data to reveal similar patterns shared by subsets of rows and columns. In the R programming language, there are many packages that make heatmaps. Among them, the ComplexHeatmap package provides the richest toolset for constructing highly customizable heatmaps. ComplexHeatmap can easily establish connections between multisource information by automatically concatenating and adjusting a list of heatmaps as well as complex annotations, which makes it widely applied in data analysis in many fields, especially in bioinformatics, to find hidden structures in the data. In this article, we give a comprehensive introduction to the current state of ComplexHeatmap , including its modular design, its rich functionalities, and its broad applications.
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.
Integrity signals
Registry
Cites
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.
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
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447
Complex heatmaps reveal patterns and correlations in multidimensional genomic data
Gu, Z., Eils, R., & Schlesner, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw313 · Cited by 10,034
Self-citationVerhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368 · cited 1×
“The data are from the Roadmap project [35].”
Methodcirclize implements and enhances circular visualization in R
Gu, Z., Gu, L., Eils, R., Schlesner, M., & Brors, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu393 · Cited by 4,246
Self-citationdeepTools: a flexible platform for exploring deep-sequencing data
Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A., & Manke, T. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku365 · Cited by 3,738
UpSetR: an R package for the visualization of intersecting sets and their properties
Conway, J. R., Lex, A., & Gehlenborg, N. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx364 · Cited by 3,586
UpSet: Visualization of Intersecting Sets
Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R., & Pfister, H. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346248 · Cited by 2,323
Longitudinal analyses reveal immunological misfiring in severe COVID-19
Lucas, C., Wong, P., Klein, J., Castro, T. B. R., Silva, J., Sundaram, M., Ellingson, M. K., Mao, T., Oh, J. E., Israelow, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2588-y · Cited by 2,074
dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering
Galili, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv428 · Cited by 1,822
A flexible R package for nonnegative matrix factorization
Gaujoux, R., & Seoighe, C. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-367 · Cited by 1,547
Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution
Masuda, T., Sankowski, R., Staszewski, O., Böttcher, C., Amann, L., Sagar, Scheiwe, C., Nessler, S., Kunz, P., van Loo, G., et al. (2019) · Nature · doi:10.1038/s41586-019-0924-x · Cited by 1,310
The History of the Cluster Heat Map
Wilkinson, L., & Friendly, M. (2009) · The American Statistician · doi:10.1198/tas.2009.0033 · Cited by 932
Visualizing Genomic Data Using Gviz and Bioconductor
Hahne, F., & Ivanek, R. (2016) · Methods in Molecular Biology · doi:10.1007/978-1-4939-3578-9_16 · Cited by 885
Proteogenomic and metabolomic characterization of human glioblastoma
Wang, L.-B., Karpova, A., Gritsenko, M. A., Kyle, J. E., Cao, S., Li, Y., Rykunov, D., Colaprico, A., Rothstein, J. H., Hong, R., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.01.006 · Cited by 661
Wolff, J., Rabbani, L., Gilsbach, R., Richard, G., Manke, T., Backofen, R., & Grüning, B. A. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa220 · Cited by 441
Getting Things in Order: An Introduction to the R Package seriation
Hahsler, M., Hornik, K., & Buchta, C. (2008) · Journal of Statistical Software · doi:10.18637/jss.v025.i03 · Cited by 385
Make Interactive Complex Heatmaps in R
Gu, Z., & Hübschmann, D. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab806 · Cited by 378
Self-citationGu, Z., & Hübschmann, D. (2023) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.04.008 · Cited by 307
Self-citationHeer, J., Kong, N., & Agrawala, M. (2009) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518897 · Cited by 288
Gu, Z., Eils, R., Schlesner, M., & Ishaque, N. (2018) · BMC Genomics · doi:10.1186/s12864-018-4625-x · Cited by 245
Self-citationSuperheat: An R Package for Creating Beautiful and Extendable Heatmaps for Visualizing Complex Data
Barter, R. L., & Yu, B. (2018) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2018.1473780 · Cited by 220
Immunogenomic Landscape of Hematological Malignancies
Dufva, O., Pölönen, P., Brück, O., Keränen, M. A. I., Klievink, J., Mehtonen, J., Huuhtanen, J., Kumar, A., Malani, D., Siitonen, S., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.06.002 · Cited by 190
Heatmap3: an improved heatmap package with more powerful and convenient features
Zhao, S., Guo, Y., Sheng, Q., & Shyr, Y. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s10-p16 · Cited by 183
Green microalgae in marine coastal waters: The Ocean Sampling Day (OSD) dataset
Tragin, M., & Vaulot, D. (2018) · Scientific Reports · doi:10.1038/s41598-018-32338-w · Cited by 129
Glioblastoma epigenome profiling identifies SOX10 as a master regulator of molecular tumour subtype
Wu, Y., Fletcher, M., Gu, Z., Wang, Q., Costa, B., Bertoni, A., Man, K.-H., Schlotter, M., Felsberg, J., Mangei, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-20225-w · Cited by 101
Self-citationSCOPE: A Normalization and Copy-Number Estimation Method for Single-Cell DNA Sequencing
Wang, R., Lin, D. Y., & Jiang, Y. (2020) · Cell Systems · doi:10.1016/j.cels.2020.03.005 · Cited by 97
dendsort: modular leaf ordering methods for dendrogram representations in R
Sakai, R., Winand, R., Verbeiren, T., Moere, A. V., & Aerts, J. A. (2014) · F1000Research · doi:10.12688/f1000research.4784.1 · Cited by 73
A Toolbox for Bicluster Analysis in R
Kaiser, S., & Leisch, F. (2008) · Open access LMU (Ludwid Maxmilian's Universitat Munchen) · doi:10.5282/ubm/epub.3293 · Cited by 68
cola: an R/Bioconductor package for consensus partitioning through a general framework
Gu, Z., Schlesner, M., & Hübschmann, D. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1146 · Cited by 60
Self-citationHu, A., Li, S., Zhang, L., Wang, H., Yang, J., Luo, Z., Rashid, A., Chen, S., WeiXiong, H., & Yu, C. (2018) · Environmental Pollution · doi:10.1016/j.envpol.2018.07.097 · Cited by 47
HilbertCurve: an R/Bioconductor package for high-resolution visualization of genomic data
Gu, Z., Eils, R., & Schlesner, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw161 · Cited by 32
Self-citation
Cited by
Human microglial state dynamics in Alzheimer’s disease progression
Sun, N., Victor, M. B., Park, Y. P., Xiong, X., Scannail, A. N., Leary, N., Prosper, S., Viswanathan, S., Luna, X., Boix, C. A., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.037 · Cited by 462
Multiomic spatial landscape of innate immune cells at human central nervous system borders
Sankowski, R., Süß, P., Benkendorff, A., Böttcher, C., Fernandez-Zapata, C., Chhatbar, C., Cahueau, J., Monaco, G., Gasull, A. D., Khavaran, A., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02673-1 · Cited by 156
The best practice for microbiome analysis using R
Wen, T., Niu, G., Chen, T., Shen, Q., Yuan, J., & Liu, Y.-X. (2023) · Protein & Cell · doi:10.1093/procel/pwad024 · Cited by 139
Kirsher, D. Y., Chand, S., Phong, A., Nguyen, B., Szoke, B. G., & Ahadi, S. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01665-1 · Cited by 111
Guo, X., Nie, H., Zhang, W., Li, J., Ge, J., Xie, B., Hu, W., Zhu, Y., Zhong, N., Zhang, X., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.01.001 · Cited by 100
Metadata
- Authors
- Gu, Z.
- Venue
- iMeta
- Publisher
- Wiley
- Published
- 2022-08
- Volume
- 1
- Issue
- 3
- Pages
- e43-e43
- ISSN
- 2770-596X (ISSN-L)
- OpenAlex
- W4289527519
- Cited by
- 1,885
- References
- 33
- FWCI
- 107.73
- Type
- journal article
- Language
- en
- Open access
- gold
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.