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Statistical significant differences were determined between the samples using ANOVA (analysis of variance; f-test for multiple samples or two samples with α = 0.05); and Fishers least significance tests.
Keliadegraph was used to plot the distribution of intensities and to determine the significance of any variability between the samples using the Students t-test.
The statistical analyses were performed using Microsoft Excel Microsoftto to calculate the SD and to test for statistically significant differences between the samples using a Student t-test.
Indeed, the high degree of concordance between the samples using the Spearman Rank Correlation shows that for the most part, these two methods are comparable.
Given the sample size of this study and the alpha (.05), the test power (1- beta) for detecting significant group mean differences between the samples using a dependent t-statistic was.33 for a small (d =.20),.91 for a medium (d =.50) and.99 for a large (d = 80) effect.
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The multiple comparison method was applied to determine statistical differences between the mean of the samples using Fisher's lower significant difference (LSD) method.
The temperature dependence of these structural parameters is different between the samples used in the measurement.
This was done for the purpose of minimising the variation in the orientation of the collagen fibres between the samples used.
Human and chimpanzee samples were matched with respect to sex and relative age as closely as possible, with no sex or age bias shared between the samples used for mRNA and protein measurements (Table S2).
However, there was no overlap between the samples used on the array and for MeDIP-seq.
There is potentially some overlap between the samples used in the WTCCC study and the current analysis.
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