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For a linear regression, the coefficient of correlation (r) can be determined using Eq. 2, and is checked for significance by comparing the critical values as recommended in the Pearson product moment correlation table.
Linear regression was drawn for time points 1 to 6 and visually checked for significance, using the 95% CI interval of the baseline measurements and the corresponding 95% CI intervals of the linear regression.
All values were checked for significance by the Chi-Square test with Yate's corrections.
Statistical correlations of data were checked for significance using the student t test.
Potential interactions were checked for significance.
Models were also checked for significance of interaction terms involving main effects.
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GAG was assessed for normality and a one-way ANOVA with a Fisher's PLSD was done in order to check for significance between all media treatment groups (with P < 0.05 significant).
Gene set enrichment analysis (GSEA) (version 2) [ http://www.broadinstitute.org/gsea/] with 1000 permutations was used to check for significance of the gene lists in separating the clusters.
We counted how often singletons and duplicates occurred in operons or not, and used the Fisher's exact test to check for significance.
Each variable was then fitted back into the model individually to check for significance, and finally the group variable that remained forced into the model throughout was examined for significance (P = 0.05).
Here we describe a novel asymptotic upper bound on the calculation of the LSA statistic's p-value, resulting in an exponentially converging calculation to bound and check for significance of computed LSA statistics without a computationally intensive permutation test.
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