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Recently, Murata[38] proposed a simplified test to measure the independence between two random signals.
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In ideal case, the cross-correlation function between two random noise signals would be nonzero only at the zero lag.
Of note, the few colocalizations between centromeric signals and DDR foci also demonstrates that our imaging technique is sensitive enough to discriminate coincidental overlap between two random foci within a cell nucleus.
But then, there are also measures of co-movement between two random variables.
Mutual information (MI) is a statistical measure of the codependency between two random variables.
Several algorithms exist to estimate the MI between two random variables.
Pearson correlation coefficient (PCC) is a standard measure of interdependence between two random variables.
Right panel: comparison between two random binding assays exhibited non-correlation between motifs M303 and M259.
Fifty thousand simulations of the correlation between two random vectors of 14 elements were performed.
Small (statistically zero) MI between two random variables indicates that variables are independent.
Thus, the linear dependency between two genes can be studied by using the correlation coefficient between two random variables.
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