The arrival of RNA-seq as a high-throughput method competitive to the established microarray technologies has necessarily driven a need for comparative evaluation. To date, cross-platform comparisons of these technologies have been relatively few in number of platforms analyzed and were typically gene name annotation oriented. Here, researchers from Washington University in Saint Louis School of Medicine present a more extensive and yet precise assessment to elucidate differences and similarities in performance of numerous aspects including dynamic range, fidelity of raw signal and fold-change with sample titration, and concordance with qRT-PCR (TaqMan). To ensure that these results were not confounded by incompatible comparisons, they introduce the concept of probe mapping directed “transcript pattern”. A transcript pattern identifies probe(set)s across platforms that target a common set of transcripts for a specific gene. Thus, three levels of data were examined: entire data sets, data derived from a subset of 15,442 RefSeq genes common across platforms, and data derived from the transcript pattern defined subset of 7,034 RefSeq genes.
In general, there were substantial core similarities between all 6 platforms evaluated; but, to varying degrees, the two RNA-seq protocols outperformed three of the four microarray platforms in most categories. Notably, a fourth microarray platform, Agilent with a modified protocol, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments, especially in regards to fold-change evaluation. Furthermore, these 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80 % fold-change concordance with the gold standard qRT-PCR (TaqMan).
Bar charts for platform comparisons on magnitude of differential expressions determined by average absolute fold-change. Average absolute fold-change was analyzed for each titration across all 6 platforms in entire data set (a) as well as in transcript pattern (TP) restricted 7,034 subset (b). To ascertain the magnitude of differential expression for a platform as a whole, the 4 average absolute fold-changes of the full titrations were averaged in both entire genes and detectable genes in the entire data set (c), as well as in TP non-restricted and restricted 7,034 RefSeq genes subsets (d). To gauge platform fidelity level in fold-change along sample titrations, percent of genes with a Pearson correlation > +0.5 was indicated in the panels (a) and (b). In addition, the fold-change enhancement was indicated with dotted lines in green in panels (c) and (d) that was determined as the difference in average absolute fold-change between the bar elements from left to right for each platform. Moreover, the statistics were placed in the panels (c) and (d) for the difference in average fold-change from AGLN to the other platforms for the entire set of data and for the TP-defined subset of data. When compared to AGLN, the average absolute fold-change was significantly lower in all platforms (p < 0.01–0.001) in the data set for entire genes, and such difference was statistically significant to 3 microarray platforms (p < 0.01) but not to the RNA-seq protocols (p > 0.05) in the TP restricted 7,034 subset.
This study suggests that microarrays can perform on nearly equal footing with RNA-seq, in certain key features, specifically when the dynamic range is comparable. Furthermore, the concept of a transcript pattern has been introduced that may minimize potential confounding factors of multi-platform comparison and may be useful for similar evaluations.