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Figure 1 | Genome Medicine

Figure 1

From: Comparison of methods to identify aberrant expression patterns in individual patients: augmenting our toolkit for precision medicine

Figure 1

The outlying degree outperforms other methods in both high and low variability simulated datasets. (A) Expression data was simulated from two distributions (normal with mean of seven and standard deviation of one as well as a t-distribution with non-centrality parameter set to seven and the degrees of freedom equal to fifteen) that were at the extremes of what would be typically observed in microarray data with the distribution of hypothetical patient data situated somewhere in the middle. (B, C) The outlying degree (k = 9) significantly outperformed both the Zscore and Rscore method in terms of power and false discovery for all combinations of effect size and distribution type. However, all the methods were only effective when encountering high effect sizes (four to five) with low variability (normal distribution). The grey areas indicate 0.95 confidence intervals. Note that for the false discovery rate, the estimates were very stable and the grey area is not readily observable. OD, outlying degree.

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