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All Pearson correlations with r ≥0.7 were saved to a '.pearson' file and a correlation cut off of r = 0.8 was used to construct a graph containing 20,355 nodes (probesets) and 1,251,575 edges (correlations between nodes above the threshold).

The final layout and analysis of the data was performed using a Pearson correlation cut off of r = 0.9 and the resultant graph clustered using an MCL inflation value of 2.2.

Neither the pSF-12 PCS, the pUI nor the FES reached the intraclass correlation coefficient cut off of 0.7 that is generally considered acceptable agreement [ 24].

We also evaluated the consequences of using more rigorous cut off points (correlation coefficient between two determinants >0.4) or less rigorous cut off points (coefficient >0.8).

cut off.

The traditional method based on defining cut-off correlation values does not address this problem directly.

The correlation was significant and close or higher than the 0.50 cut-off correlation value considered the minimum for showing adequate construct validity.

We interrogated these data to identify transcripts most strongly correlated with HES6 and selected the top 1.5% according to a cut-off correlation coefficient of 0.9 (Supplementary Fig S10A).

For the time shift f, defined as f = i + k if i + k < N and f = i + k - N otherwise: For each time series we calculated the maximum positive R(f) among all possible phase shifts f and use tabulated 0.05 significance cut off values for correlation coefficient.

Using more (correlation coefficient > 0.4) or less (correlation coefficient > 0.8) rigorous cut off points for the maximal correlation between every pair of determinants hardly changed our results.

Using a strict correlation cut off (r > 0.85) leaves only three members of the rif1∆ est1∆ class; however, we can see in Figure 4F that the 20 most highly correlated genes display a distinctive MDP.

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