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Inconsistencies between the two correlation structures are analyzed by means of diagnostic indices.
However, using the Fisher r-to-z transformation (Cohen et al., 2003), the significance of the difference between the two correlation coefficients was not statistically significant (p = 0.53, two tailed).
We also calculated the difference between the two correlation coefficients.
After careful observation and comparison we find partially complementary relationship between the two correlation matrices R(con)L×L and R(mut)L×L: the peaks and valleys are located in alternate places in Fig. 2 A and Fig. 3 A.
The figure demonstrates that although the Pearson's correlation coefficients themselves are small (| ρ|<0.10), there exist distinct differences between the two correlation structures.
We also performed statistical analysis on the data obtained for these genes from RNA-seq and qPCR and observed that there was very good correlation between the two (correlation coefficient 0.8) (Fig. 7b).
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Our results confirm these data, since correlations between either FRC or Crs and PaO2 are also significant and weak without a difference between the two correlations.
The difference between the three correlations was statistically significant (p < 0.001) and their relative rank was conserved in both the library intersection and relativity based approaches.
This analysis showed no overall significant correlation between the two variables (correlation: r = 0.244, P = 0.251).
There was poor correlation between the two techniques (correlation coefficient r = 0.145).
There were, however, significant correlations between the two values (correlation coefficient = 0.21 and 0.31 for control and CCFS subjects).
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