The recent advances in next generation sequencing technology have made the sequencing of RNA (i.e., RNA-Seq) an extemely popular approach for gene expression analysis. Identification of significant differential expression represents a crucial initial step in these analyses, on which most subsequent inferences of biological functions are built. Yet, for identification of these subsequently analysed genes, most studies use an additional minimal threshold of differential expression that is not captured by the applied statistical procedures.
Researchers from the University of Kiel have developed a new analysis approach, ABSSeq, which uses a negative binomal distribution to model absolute expression differences between conditions, taking into account variations across genes and samples as well as magnitude of differences. In comparison to alternative methods, ABSSeq shows higher performance on controling type I error rate and at least a similar ability to correctly identify differentially expressed genes.
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)
ABSSeq specifically considers the overall magnitude of expression differences, which enhances the power in detecting truly differentially expressed genes by reducing false positives at both very low and high expression level. In addition, ABSSeq offers to calculate shrinkage of fold change to facilitate gene ranking and effective outlier detection.