Fig. 3
From: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences
Comparison of methods using validated real data sets. a-c based on data from the MAQC study; d-e based on the ABRF data set. ROC analysis for (a) TaqMan and (b) PrimePCR data sets at a qRT-PCR absolute log-ratio (logFC) threshold of 0.5. TPR, true positive rate; FPR, false positive rate. ABSSeq performs better than other methods in detecting true differential expression. A gene was considered to be not differentially regulated if its logFC was less than 0.2. c Minimal fold changes under various ajusted p-value cutoffs for the MAQC II data set. d Number of false postives in comparisons of samples from same condition but different lab sites and (e) number of DE genes in comparison of samples from two conditons under additional filtering and confounding factor assessment approaches. Symbols in black show results from comparison of conditions from same laboratory and colored symbols those from comparison of conditions across laboratories. Genes are counted under 5 situations: orginal, without filtering (circle symbols); Foldchange, with a value greater than 1.5 (star symbols); AveExp, with average logCPM greater than 1 (square symbols); combination of Foldchange and AveExp (triangle symbols); and svaseq tested only for DESeq2 and Voom (pentacle symbols)