Figure 4 | Scientific Reports

Figure 4

From: How to do quantile normalization correctly for gene expression data analyses

Figure 4

Performance of statistical feature selection (Precision: P, Recall: R and F-score: F) and batch effect correction (gPCA Delta: D) (A) statistical feature selection across the various quantile normalization strategies given increasing class effects (from 0 to 0.8). Data points shown here are the respective means across 100 simulations based on the RCC dataset. (B) Statistical feature selection across the various quantile normalization strategies given increasing class effects (from 0 to 0.8) and increasing batch effects (from 0 to 0.8). Data points shown here are the respective means across 100 simulations based on the D2.2 dataset. The “Adjust” scenario is not a quantile normalization strategy, it is the data after inserting class and/or batch effects, but no normalization.

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