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The transcriptomic datasets are visualized as tracks with coverage-related red histograms.
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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.
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).
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 datasets were then stitched together using the cross-correlation measure and are visualized together as shown in Figure 7b.
"Moreover if a dataset should represent you it is not only about the parts that are visualized but also about the uncomfortable parts of history that make a society.
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