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Statistical significance among different experimental groups was determined by one-way analysis of variance (ANOVA) and Tukey's test was used to evaluate the differences among the means of different treatments.
ANOVA test was used to evaluate the differences among the three groups pre- and post-treatment, respectively, and LSD post hoc analyses were applied to test the differences in between.
Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (CA) allow us to investigate the correlations among the identified variables and the set of particles, to identify clusters of particles that can be differentiated on the bases of their chemical composition and morphology and to evaluate the differences among the sampling sites.
A direct comparison of their total gene expression patterns was performed to evaluate the differences among each category.
A direct comparison of their gene expression patterns, as quantified by the number and overlap of DEGs, was performed to evaluate the differences among each category.
*The Kruskal-Wallis test was used to evaluate the differences among the four groups.
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This mechanism uses the normalized distance to evaluate the difference among genes in a population.
Then, we evaluate the difference among them using the several functionality and complexity metrics.
For each trait evaluated, the pooled families within population variance provided an error term to evaluate the difference among populations.
To analyze the genetic alterations of pheochromocytomas and evaluate the difference among malignant, extra-adrenal, and benign pheochromocytomas.
For nonparametric analysis, the Mann-Whitney U test with post hoc analysis was used to evaluate the difference among three groups.
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