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D.F. analysed and simulated the data.
We compared the MLR results for each model "before MI" and "after MI" to assess the impact of simulating missing data on AORs.
Therefore, to distinguish between true splice junctions and false positives, we assessed the criteria based on simulating data on a set of randomly-constituted junctions.
Because of the complexity of the parameter space, we do not derive the null distribution analytically, and suggest assessing significance empirically by simulating data under the null hypothesis.
This allows us to assess the quality of our simulated data as accurately as possible.
Parameter estimates were assessed for the 6000 simulated data sets closest to the observed data using direct and logistic regressions.
Using the data simulated in model 1, we fitted Equation (3) to assess its type 1 error for single-locus M×O interaction.
We assess the performance of our model on simulated data and compare it with favorable results to related methods.
We assess the performance of our model on simulated data and compare it with favorable results of other unsupervised methods.
The simulated mutational catalogs were leveraged to assess the ability of our approach to decipher the mutational signatures with which the data were simulated.
SNP-calling performance was assessed further by the simulated PE data with 50 and 90 bp reads mentioned above.
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