Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing

Abstract

BackgroundRNA sequencing (RNA-Seq) has emerged as a powerful approach for the detection of differential gene expression with both high-throughput and high resolution capabilities possible depending upon the experimental design chosen. Multiplex experimental designs are now readily available, these can be utilised to increase the numbers of samples or replicates profiled at the cost of decreased sequencing depth generated per sample. These strategies impact on the power of the approach to accurately identify differential expression. This study presents a detailed analysis of the power to detect differential expression in a range of scenarios including simulated null and differential expression distributions with varying numbers of biological or technical replicates, sequencing depths and analysis methods.ResultsDifferential and non-differential expression datasets were simulated using a combination of negative binomial and exponential distributions derived from real RNA-Seq data. These datasets were used to evaluate the performance of three commonly used differential expression analysis algorithms and to quantify the changes in power with respect to true and false positive rates when simulating variations in sequencing depth, biological replication and multiplex experimental design choices.ConclusionsThis work quantitatively explores comparisons between contemporary analysis tools and experimental design choices for the detection of differential expression using RNA-Seq. We found that the DESeq algorithm performs more conservatively than edgeR and NBPSeq. With regard to testing of various experimental designs, this work strongly suggests that greater power is gained through the use of biological replicates relative to library (technical) replicates and sequencing depth. Strikingly, sequencing depth could be reduced as low as 15% without substantial impacts on false positive or true positive rates.

Systematic integration of RNA-Seq statistical algorithms for accurate detection of differential gene expression patterns

“…(17). The synthetic data sets were created using a previously described methodology (5,23) based on real publicly available RNA-Seq count data for five organisms, namely, Homo sapiens (Human), Pan troglodytes (Chimpanzee), Mus musculus (Mouse), Drosophila melanogaster (Fruitfly) and Arabidopsis thaliana (Arabidopsis). We used a limited set of simulations and real data, which are sufficient to prove the added value of PANDORA.…”

MultiRankSeq: Multiperspective Approach for RNAseq Differential Expression Analysis and Quality Control

“…Combining multiple methods of RNAseq data analysis has been previously suggested. For example, Robles et al suggested that using a combination of multiple packages may overcome the possible bias susceptibility of a given package to a particular dataset of interest [20]. In another study by Soneson et al, the authors suggested the use of transformation-based approaches (the variance stabilizing transformation provided in the DESeq R package and the voom transformation from the limma R package) combined with LIMMA [25], which performed well under many conditions.…”

lncRNA expression in the auditory forebrain during postnatal development

“…These three methods were chosen based on results of several previous studies in which multiple RNAseq differential analysis methods were compared for accuracy and sensitivity of read count-based data (Dillies et al, 2013; Guo et al, 2013a; Kvam et al, 2012; Robles et al, 2012; Soneson and Delorenzi, 2013). In analyses of the same dataset, the methods typically differ in numbers of differentially expressed genes identified in a comparison of any two samples and also in the direction of expression (up- or down-regulation).…”