- Open Access
HeatMapper: powerful combined visualization of gene expression profile correlations, genotypes, phenotypes and sample characteristics
BMC Bioinformaticsvolume 7, Article number: 337 (2006)
Accurate interpretation of data obtained by unsupervised analysis of large scale expression profiling studies is currently frequently performed by visually combining sample-gene heatmaps and sample characteristics. This method is not optimal for comparing individual samples or groups of samples. Here, we describe an approach to visually integrate the results of unsupervised and supervised cluster analysis using a correlation plot and additional sample metadata.
We have developed a tool called the HeatMapper that provides such visualizations in a dynamic and flexible manner and is available from http://www.erasmusmc.nl/hematologie/heatmapper/.
The HeatMapper allows an accessible and comprehensive visualization of the results of gene expression profiling and cluster analysis.
Gene expression profiling by applying microarrays followed by cluster analyses is a powerful way to define pathobiologically relevant relations between the expression of sets of genes and disease classes. Unsupervised methods such as cluster analysis  and principal component analysis  are often applied to calculate and visualize these relations. Interpretation of results obtained by cluster analysis is frequently performed by visual inspection of a so-called heatmap; a matrix of genes versus samples in which gene expression levels or ratios are indicated using colors. Green often indicates low expression or down-regulation while red is frequently used to indicate high expression or up-regulation of genes [1, 3]. A dendrogram, which is typically produced by unsupervised cluster analysis, provides further insights into sample-to-sample or gene-to-gene relations . These visualizations are useful when small numbers of samples and genes are analyzed, but are insufficient when studying larger datasets. Similarities and differences between samples or genes are easily lost due to the large size of these visualizations. This shortcoming particularly affects patient-cohort studies, since these analyses include increasing numbers of samples to allow comprehensive analyses.
A second type of heatmap that is frequently used is a matrix of pair-wise sample correlations in which anti-correlation or correlation is indicated by a color-scale, e.g. blue to red [4–6]. Although details on individual gene expression measurements are lost, similarity between any pair of samples can easily be inspected.
To be able to correctly interpret both the sample versus gene expression heatmap and the sample versus sample correlation plot, data of the type of samples profiled, e.g. clinical parameters, karyotypes, mutations in particular genes, or gene expression data should be available. This information might then be included in a visual overview, as is frequently seen with sample versus gene heatmaps [7, 8]. Such presentation would be a useful addition to the sample-sample heatmaps, which are frequently shown without metadata. Here we developed a tool, called the HeatMapper, which can generate such combined visualizations. The tool is simple in use and allows dynamic and flexible display of a correlation plot in combination with sample characteristics.
The HeatMapper, written in JAVA (version 1.4.2), uses comma-separated or tab-delimited text-files as input. It requires two files: one file containing a matrix of sample-sample similarity, i.e. Pearson correlation, Spearman correlation or Euclidean distance, and one file with sample related data. In both files, similar sample ID's are used. Correlation files can be generated using tools such as Omniviz, GeneMaths and R/BioConductor, while sample data files can for instance be created in Microsoft Excel. Example files are available from the website. Alternatively, the tool can be adapted to communicate with a database. In our laboratory, the HeatMapper is connected to a MySQL database which further optimizes the workflow. This version is available on request.
Results & discussion
As the upper right part of a traditional sample versus sample heatmap is in fact a mirror image of the lower left part, it is redundant. Therefore, when data are loaded, the HeatMapper only displays a triangular heatmap (Figure 1). Sample-sample (dis-) similarity, i.e. Pearson correlation, Spearman correlation or Euclidean distance, is mapped to a color scale ranging from blue to red. Dark blue relates to the negative extreme value of the metric, i.e. -1 for Pearon correlation, where dark red refers to the positive extreme value, i.e. 1 for Pearson correlation. Sample related data, can be simply added via the menu and is subsequently plotted alongside the heatmap diagonal. Different entries in one sample characteristic are mapped to different colors, or, in the case of numeric data, shown as bars of which the size is proportional to the value. Several options are available to customize the resulting visualization, such as zoom functionality and options to change the colors used in histograms or bars to indicate phenotypic or genotypic differences. Further customization options include the possibility to change the sample order, allowing a user for instance to visualize the results of a different clustering algorithm, or to sort the data according to any user-defined order. This can be accomplished via selecting the 'Change sample order' menu-option, after which the order of the sample ids can be inserted by typing them or using copy-paste. Subsets of the original data can be created and viewed in any sequence. Importantly, high-resolution images of the produced figures can be exported using the Portable Network Graphics (PNG) format.
Our tool provides several advantages over more traditional means of presenting results obtained gene expression profiling and clustering analysis [7, 8]. The pair-wise display of samples clearly indicates similarity in expression profiles. By combined visualization of sample versus sample similarities and sample characteristics, subclasses of samples sharing a commonality, such as a mutation in a particular gene, and a high similarity in expression profile can be readily identified. Cluster assignments, made manually by the user, can then be added via the 'Add special values' menu option and displayed as sample characteristic.
As an example, Figure 1 shows the results of a cluster analysis of 285 acute myeloid leukemia (AML) samples. Clusters are recognized as red triangles near the plot diagonal. Sample related data are presented in the adjacent bars, where the same color indicates the same characteristic. The last bar indicates the expression levels of CD34, in which the level of expression is proportional to the length of the bar. By visual inspection of this plot, one can immediately conclude that (1)AML samples can be separated into several subtypes, such as cases with a t(8;21), based on expression profiling , (2) several clusters are related to a single distinguished abnormality (for instance nucleophosmin (NPM1) mutations), indicated in red in the fifth column and (3) mRNA levels of CD34 are low in samples with NPM1 mutations.
In our laboratory the HeatMapper code has been coupled to a database containing gene expression profiling results, from which gene expression levels can dynamically be obtained. This allows the quick and accurate visual inspection of the distribution of expression levels in different clusters, and making the tool even more powerful. The database implementation, is available on request.
With the increase of the number of samples profiled, particularly in patient-cohort studies, specialized visualization methods for microarray studies are indispensable. Our tool allows the accurate inspection of combinations of dataset characteristics, i.e. correlations and clustering results and sample related characteristics, i.e. survival time and gene expression levels. Summarizing, the HeatMapper tool results in powerful visualization tool that allows the accurate and rapid interpretation of the data obtained by large scale gene expression profiling. The HeatMapper tool has already proven to be very useful in several studies [6, 9, 10, 11, 12].
Availability & requirements
Project name: HeatMapper
Project homepage: http://www.erasmusmc.nl/hematologie/heatmapper/
Operating system: Platform independent
Programming language: JAVA
Other requirements: JAVA 1.4.2 or higher.
License: The tool is available free of charge. Source code is available upon request.
Any restrictions to use by non-academics: None
Acute Myeloid Leukemia
Portable Network Graphics
Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 1998, 95(25):14863–14868. 10.1073/pnas.95.25.14863
Raychaudhuri S, Stuart JM, Altman RB: Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac Symp Biocomput 2000, 455–466.
Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998, 9(12):3273–3297.
Dudoit S, Fridlyand J, Speed TP: Comparison of discrimination methods for the classification of tumors using gene expression data. In Journal of the American Statistical Association. Volume 97. Berkeley, University of California; 2002:77–87. 10.1198/016214502753479248
Ross ME, Mahfouz R, Onciu M, Liu HC, Zhou X, Song G, Shurtleff SA, Pounds S, Cheng C, Ma J, Ribeiro RC, Rubnitz JE, Girtman K, Williams WK, Raimondi SC, Liang DC, Shih LY, Pui CH, Downing JR: Gene expression profiling of pediatric acute myelogenous leukemia. Blood 2004, 104(12):3679–3687. 10.1182/blood-2004-03-1154
Valk PJ, Verhaak RG, Beijen MA, Erpelinck CA, Barjesteh van Waalwijk van Doorn-Khosrovani S, Boer JM, Beverloo HB, Moorhouse MJ, van der Spek PJ, Lowenberg B, Delwel R: Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med 2004, 350(16):1617–1628. 10.1056/NEJMoa040465
van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415(6871):530–536. 10.1038/415530a
Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, Jatkoe T, Berns EM, Atkins D, Foekens JA: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005, 365(9460):671–679.
Bullinger L, Valk PJ: Gene expression profiling in acute myeloid leukemia. J Clin Oncol 2005, 23(26):6296–6305. 10.1200/JCO.2005.05.020
Valk PJ, Delwel R, Lowenberg B: Gene expression profiling in acute myeloid leukemia. Curr Opin Hematol 2005, 12(1):76–81. 10.1097/01.moh.0000149610.14438.9a
van den Akker E, Vankan-Berkhoudt Y, Valk PJ, Lowenberg B, Delwel R: The common viral insertion site Evi12 is located in the 5'-noncoding region of Gnn, a novel gene with enhanced expression in two subclasses of human acute myeloid leukemia. J Virol 2005, 79(9):5249–5258. 10.1128/JVI.79.9.5249-5258.2005
Verhaak RG, Goudswaard CS, van Putten W, Bijl MA, Sanders MA, Hugens W, Uitterlinden AG, Erpelinck CA, Delwel R, Lowenberg B, Valk PJ: Mutations in nucleophosmin (NPM1) in acute myeloid leukemia (AML): association with other gene abnormalities and previously established gene expression signatures and their favorable prognostic significance. Blood 2005, 106(12):3747–3754. 10.1182/blood-2005-05-2168
Supported by grants from the Erasmus University Medical Center (Revolving Fund) and the Dutch Cancer Society "Koningin Wilhelmina Fonds".
RGWV designed the software, participated in all phases of research and wrote the manuscript; MAS wrote the majority of the JAVA code; MAB contributed to software design and earlier code; RD gave intellectual contributions and revised the manuscript; SB contributed to the software code; MJM and PJS were involved in an earlier implementation of the software; BL gave intellectual contributions; PJV initiated the idea, gave intellectual contributions and revised the manuscript.
Authors’ original submitted files for images
Below are the links to the authors’ original submitted files for images.