Figure 2

The weighted outlying degree can attenuate the effect of sample-specific technical variability. (A) An example of a simulated dataset from the normal distribution with a technical factor affecting 2,500 of the 10,000 genes of sample one, making it divergent. The size of the effect is a two-unit decrease. (B, C) display power and false discovery rate estimates for the methods based on similar simulations to (A), where either 2,500 or 7,500 genes of one or three samples were affected. The effect size was kept at five units. The WODb method outperforms the others at least for the case where the number of divergent samples was equal to three. 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 method; WODa, weighted outlying degree with weighting performed after nearest neighbor computations; WODb, weighted outlying degree with weighting performed before nearest neighbor computations.