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The analysis included three main computational steps: (1) calculate a profile for each domain comprised of numbers of the domain in the tested genomes (Supplementary Table 6S), (2) calculate Pearson correlations for each pair of the domain profiles, and (3) find disconnected networks by clustering the correlated profiles according to their pair-wise correlations using a threshold.
This section describes the computation of the cross correlations using a more complex setting with multiple sources, including scattering in the environment.
We calculated descriptive statistics for the variables: DBH, H, Age (I), BEF, R and C and analyzed their correlations using a simple correlation matrix.
The two-condition-based GWAS was followed by integration with network-derived metabolite transcript correlations using a time-course stress experiment.
This paper examines both conceptual and practical issues related to such correlations, using a case study from the eastern part of the former Solent River system near Southampton, England.
Reliability also was examined by calculating Pearson correlations using a test/retest approach.
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It could be a better way for correlation forecasting, if one describes the correlation using a mean-reverting stochastic process.
In this algorithm, CP correlation using a sliding window is performed over a number of OFDM symbols.
However, the Fluent solver allows for a direct specification of the three missing correlation using a user-defined function (UDF) written and compiled in C language.
In addition, the video sensors are able to exploit temporal correlation using a motion compensated lifted wavelet transform [19] at the encoder.
Next, the units were joined at the point of maximal correlation using a cross-fade technique [33] which further helped remove the phase discontinuities.
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