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Fig. 6 | BMC Genomics

Fig. 6

From: aFold – using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data

Fig. 6

Comparison on simulated data. a-b Area under the curve (AUC) for aFold and two alternative methods under two simulation settings: (a) Negative Binomial (NB) distribution and (b) NB distribution with random outliers (R). Each boxplot summarizes the AUCs across 10 independently simulated data sets. Asterisk indicates a statistically significant difference in AUC between aFold and any of the other methods. n indicates the number of considered RNA-Seq replicates, from (2, 5, 10). Under all conditions, aFold is highly effective in correctly identifying differentially expressed genes. c-d Sensitivity and FDR analysis of the tested methods. The sensitivity is defined as the fraction of genes under adjusted p-value < 0.1 among true DEs. The FDR is the fraction of false DEs among genes under adjusted p-value < 0.1. ROTS is unable to handle small sample size (n = 2) and thus is excluded at n = 2

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