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As the distribution of the gene expression values was not normally distributed Kolmogorov-Smirnovv test), we normalized the data distribution by using log10 to carry out the statistical analysis.
We employed Kohonen's self organizing map (SOM) technique [25] for visualizing the data distribution by nonlinear projection of this high-dimensional sequence space [26].
The significance of phytohormone and TPI activity induced by different HAEs was assessed by parametric or non-parametric tests after normality testing of the data distribution by the Shapiro test.
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Comparing the data distributions by Mann–Whitney U-test supported the significance of these qualitative observations (P-values for NLCQ-1, RB6145 and tirapazamine vs saline-treated controls were <0.00001, <0.001 and 0.007).
Data symmetry was tested by use of visual inspection of the data distribution plotted by histograms.
An exploration by Dean Radin, looking at the shape of the data distribution represented by the skew statistic may be seen on Dean's blog.
This method utilizes unlabeled instances to reduce the data distribution gap by incorporating multiple speed-up spectral regression kernel discriminant analysis (SRKDA) into the original supervised method.
The modification of the procedure introduced here sets the acceptance levels at the peak and LEHM of the data distribution given by the smoothing algorithm (with the smoothing coefficient of 15 mm, Figure 5B).
However, we also found a notable difference between the data distribution predicted by available models of sequence evolution and those observed in empirical data.
The normality of the data distribution was evaluated by Kolmogorov- Smirnov test10.
The data distribution was evaluated by visual inspection of histograms.
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