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The approach builds on the metapopulation concept to produce a dispersal model based on repeated simulations of stochastic extinction-colonization dynamics across multiple landscapes of variable connectivity.
Optimisation (tuning) of the model is based on repeated simulations of a scenario, where probabilistic parameters (four for each type of obstacle) are set according to the comparison results of simulated and measured data.
As a result, optimization based on repeated simulations typically fails, and a simultaneous, equation-based approach must be applied.
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Based on repeated Monte Carlo simulations and assuming there are 1,000,000 total transcripts per cell, we calculated the mean and median copy numbers of detected transcripts (Figure 4) and the probability of detection in different expression level bins (Figure 5) at different sampling stages.
Under model (19) with j={3,4,5}, based on 1000 repeated simulations, the p-value for mouse No. 60 is estimated as 0.39.
Significance is based on repeated measurements.
An analysis procedure based on repeated cross-validation is suggested.
P-values based on Repeated Measures ANCOVA corrected for baseline.
Statistical analysis based on repeated-measures analysis of variance.
The optimisation process, as shown in Figure 5, is a process of model tuning (optimisation) based on running repeated narrowband simulations of a scenario (Figure 4) with probabilistic parameters of obstacles (materials) set before each simulation.
Simulation studies based on 1000 repeated re-samplings of (sample size: 1%, 5%, 10%and25%5%) the full data are used to asses and compare the methods discussed above.
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