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To capture 90% of this sample, we set c0 = 0.1 so that [see Equation (7)].

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For the least abundant phyla and the two least abundant proteobacterial classes, which included zero counts in the samples, we set the family argument to 'tweedie'64 with var.power = 1.5 and link.power = 0 (a compound Poisson gamma distribution).

Under these circumstances, after exhaustive empirical analysis (on 109 samples), we set the value of T to the square root of the center pixel in a local ternary pattern.

To reduce the impact of multiple presence within some grid cells due to more extensive sampling, we set MaxEnt to "remove duplicate presence records".

In order to estimate the lower limit of aCGH to detect copy number changes among samples, we set up an experiment in which we diluted DNA from an individual carrying a deletion of known size at 7q11.23 (typical Williams-Beuren Syndrome deletion) into a pool of normal DNA.

To evaluate the gene copy number in all samples, we set the cutoff line at 4 copies.

For all these methods applied to the different data sets with different numbers of samples, we set the quantile of outliers to 0.05 (5%).

As shown in Table 3, we consider the following simulation settings (a) number of genes: 20, 500 and 1000; (b) sample size: we set sample size equal to 50 in each study; and (c) regression coefficients.

To allow for sample attrition, we set a sample size target of n = 650 per group.

If sets of Ei correspond to the untreated sample, we get sets of Pi (0) of scaled gene profiles; for samples treated with compound R, we obtain Pi(R).

Given the explorative purpose of the present study and our sample size we set the factors' loading threshold at >0.25 [ 28– 33].

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