The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
EnrichedHeatmap: an R/Bioconductor package for comprehensive visualization of genomic signal associations
Open accessAbstract
High-throughput sequencing data are dramatically increasing in volume. Thus, there is urgent need for efficient tools to perform fast and integrative analysis of multiple data types. Enriched heatmap is a specific form of heatmap that visualizes how genomic signals are enriched over specific target regions. It is commonly used and efficient at revealing enrichment patterns especially for high dimensional genomic and epigenomic datasets. We present a new R package named EnrichedHeatmap that efficiently visualizes genomic signal enrichment. It provides advanced solutions for normalizing genomic signals within target regions as well as offering highly customizable visualizations. The major advantage of EnrichedHeatmap is the ability to conveniently generate parallel heatmaps as well as complex annotations, which makes it easy to integrate and visualize comprehensive overviews of the patterns and associations within and between complex datasets. EnrichedHeatmap facilitates comprehensive understanding of high dimensional genomic and epigenomic data. The power of EnrichedHeatmap is demonstrated by visualization of the complex associations between DNA methylation, gene expression and various histone modifications.
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
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
deepTools: 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
Cited by
Gu, Z. (2022) · iMeta · doi:10.1002/imt2.43 · Cited by 1,885
Self-citationKMT2D Deficiency Impairs Super-Enhancers to Confer a Glycolytic Vulnerability in Lung Cancer
Alam, H., Tang, M., Maitituoheti, M., Dhar, S. S., Kumar, M., Han, C. Y., Ambati, C. R., Amin, S. B., Gu, B., Chen, T.-Y., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.03.005 · Cited by 243
Narita, T., Ito, S., Higashijima, Y., Chu, W. K., Neumann, K., Walter, J., Satpathy, S., Liebner, T., Hamilton, W. B., Maskey, E., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.03.008 · Cited by 228
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-citationMEF2 is a Key Regulator of Cognitive Potential and Confers Resilience to Neurodegeneration
Barker, S. J., Raju, R. M., Milman, N. E. P., Wang, J., Davila-Velderrain, J., Gunter-Rahman, F., Parro, C. C., Bozzelli, P. L., Abdurrob, F., Abdelaal, K., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abd7695 · Cited by 96
Metadata
- Authors
- Gu, Z., Eils, R., Schlesner, M., & Ishaque, N.
- Venue
- BMC Genomics
- Published
- 2018-04-04
- Volume
- 19
- Issue
- 1
- Pages
- 234-234
- ISSN
- 1471-2164 (ISSN-L)
- OpenAlex
- W2799739256
- Cited by
- 245
- References
- 2
- FWCI
- 5.34
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: EnrichedHeatmap: an R/Bioconductor package for comprehensive visualization of genomic signal associations. https://app.cochat.ai/papers/10.1186/s12864-018-4625-x
- The paper
- Gu, Z., Eils, R., Schlesner, M., & Ishaque, N. (2018). EnrichedHeatmap: an R/Bioconductor package for comprehensive visualization of genomic signal associations. BMC Genomics. https://doi.org/10.1186/s12864-018-4625-x
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.
