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Fig. 1 | BMC Genomics

Fig. 1

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

Fig. 1

Normalization of RNA-Seq data. (a) ROC analysis using the qRT-PCR validated data sets ABRF, SEQC and MAQC-II. ROC analysis for PrimePCR data sets at a qRT-PCR absolute log-ratio (logFC) threshold of 0.5, which results in 9871 true positives. TPR, true positive rate; FPR, false positive rate. A gene is considered to be not differentially regulated if its logFC in the PrimePCR data is less than 0.2, which results in 999 true negatives. Five normalization procedures are analyzed: qtotal, TMM, total, quantile and geometric. ROCs are based on ordinary log fold changes. Rroot mean square deviation (RMSD) correlation of external RNA control consortium (ERCC) for the (b) ABRF, and (c) SEQC data sets. RMSD is calculated from observed log2 fold change of RNA-Seq data and the expected expected fold change, which results from added RNA markers into samples that mixed into samples UHR and HBR at four ratios: 1/2, 2/3, 1 and 4

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