To simulate the sampling of molecules of mRNA for each technical replicate, 30 million random integers between 1 and Z were sampled.
Simulation studies typically involve the generation of a disease model (i.e. a penetrance function) that is subsequently used to simulate the samples of a dataset.
We use the bestfit model in COSI to simulate the samples with a calibrated human demography for different populations, proposed by Schaffner et al. [ 5].
These conditions allow us to simulate the sampling intensity and biodiversity found within a typical 16S rRNA gene targeted sequencing experiment (Schloss, 2008).
Further, Mo et al. [ 13] developed a MC code to simulate the sampling volume of a ball-lens fiber probe depending on diameter and refractive index of the ball lens and probe-tissue distance.
In addition, in order to simulate the sampling after 16 h, the Chrastil approach for modeling enzymatic reactions was used to fit the experimental data for glucose release [ 61].
Finally, we simulated the sample number that is required to verify our scoring system.
Using p1 and the difference and variability observed in stage one, we simulated the sample size for stage two needed for an overall power of 80%.
We simulated the sample size needed to verify our results, as shown in Table 4, and suggest that the statistical power should be sufficient to validate with this small number of samples.
First, we considered a PFT to perform our study, simulating the sampling process.
