Previous comparisons between microarrays and RNA-Seq have come to sometimes contradictory conclusions, which researchers from Princeton University suggest result from a lack of attention to the intensity-dependent nature of variation generated by the technologies. To examine this trend, they carried out a parallel nested experiment performed simultaneously on the two technologies that systematically split variation into four stages (treatment, biological variation, library preparation and chip/lane noise), allowing a separation and comparison of the sources of variation in a well-controlled cellular system, Saccharomyces cerevisiae.
Schematic of the experiment. The Condition and Biological Replicate steps were performed irrespective of technology and these materials were then utilized in a technology-specific manner (microarrays or RNA-seq) for the Preparation and Chip/Lane steps.
With this novel dataset, the researchers demonstrate that power and accuracy are more dependent on per-gene read depth in RNA-Seq than they are on fluorescence intensity in microarrays. However, they carried out quantitative PCR validations which indicate that microarrays may demonstrate greater systematic bias in low-intensity genes than in RNA-seq.
Percent of variance explained by each nested level of the experiment as computed by an ANOVA adjusted R2, and smoothed using LOESS across the intensity quantiles. Results from microarray data (MA) are shown in the solid lines and RNA-Seq data (RS) in the dashed lines. A 95% confidence interval is shown as the shaded region around each line.