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This method may provide a better overview when a large clustered dataset is visualized.

Two-moon dataset is visualized as moon-shaped clusters (see Figure 1).

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The nucleotide composition of our dataset was visualized by the tetrahedric plot function of the R package Compositions [ 113].

Consensus tree was generated from a set of the most parsimonious trees, and a single tree for each dataset was visualized in iTOL [ 61, 62].

The amount and distribution of missing data in each dataset was visualized with mare v. 0.1.2-rc ([ 49], http://mare.zfmk.de) (Additional file 1: Figures S1-S7).

The transcriptomic datasets are visualized as tracks with coverage-related red histograms.

The resulting datasets were visualized using Cytoscape with each node representing a different signature.

Relative expression levels of the top lncRNAs, in different subtypes, both in our dataset and in the Affymetrix validation datasets were visualized by box plots and tested for significant associations with the molecular subtypes using the t-test.

The resulting images for each channel were then aligned with imageJ software[ 48] and reconstructed using an iterative Algebraic Reconstruction Technique (i-ART) algorithm with TXM-Wizard.[ 49] 3 D datasets were visualized using the Avizo Fire software and further analysis was performed using MATLAB.

The transcriptomics dataset comparing obese, diabetic subjects with lean subjects is visualized in the network.

As with previous datasets, proportional breakdown and distribution of e-value comparisons is visualized in Fig.  3.

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