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However, the "heatmap" function lacks certain functionalities and customizability, preventing it from generating advanced heat maps and dendrograms.

The "heatmap3" package allows users to produce highly customizable state of the art heat maps and dendrograms.

The "heatmap3" package also provides an option which allows the generation of multiple heat maps and dendrograms based on the threshold criteria selected by the user.

Figure 2a shows the heat maps and dendrograms for expression of the 14-3-3ζ 14-3-3ζ 14-3-3ζ from thesignaturestudies (panel I Wangenesal., panel II van't Veer et al., panel III Sorlie et al).

The "heatmap3" package is designed with advanced options and is completely backward compatible with the original "heatmap" function in R. Users with limited R skill can generate sophisticated heat maps and dendrograms with ease.

To evaluate how each model relates to the others based on the presented results so far, heat maps and dendrograms were created for the four models based on metabolic gene content or phenotype predictions (see Methods).

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Overall, we evaluated by quantitative RT-PCR 50 genes (see Table 1, Additional file 2) and compared their expression levels in ITGA6 siRNA versus control siRNA treated TEC. Figure 3 shows the heat map and dendrograms representing the bi-cluster analysis of these experiments (n = 5).

The heat map and dendrogram are plotted in the same fashion as the original "heatmap" function.

These clusters are then visualized as a heat map and dendrogram to highlight the similarity within clusters.

The heat map and dendrogram indicated the close clustering of the MTB strains and their separation from the NTM strains.

Genes of cluster C had a distinctly different temporal pattern of change compared to clusters A and B, as was observed in Figure  1B heat map and dendrogram.

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