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In summary, these approaches allow preserving statistical properties of the experimental RNA-seq and cell proportion datasets while removing biological relationships.
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For the in silico studies described above, sensitivity was measured as the proportion of datasets out of 100 simulated datasets for each scenario where the best performing neural network model contained the two functional SNPs, with no other SNPs in the model, i.e. a perfect match [ 46].
A first method (termed hereafter the direct approach) is to take as an estimate of the posterior probability of a scenario the proportion of datasets obtained with this scenario in the nδ closest datasets (Miller et al., 2005; Pascual et al., 2007).
The familywise error rate was estimated by the proportion of datasets, in which at least one true partial hypothesis was falsely rejected.
The skew and the proportion of datasets are plotted together.
The proportion of datasets returning a FP (FPX).
SN, the proportion of datasets returning a TP.
Only a small proportion of datasets (n = 10, 18.9%) included imputed SNP analysis.
This ability is determined by the proportion of datasets within which the correct underlying model was identified.
Once each pattern had been rated, performance metrics were calculated for relevant sets of data: SN, the proportion of datasets returning a TP.
Then the power of each method is estimated by the proportion of datasets in which the gene set is found to be enriched.
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