The results obtained by RNA-Seq and microarrays are highly reproducible

RNA sequencing (RNA-Seq) and microarray are two of the most commonly used high-throughput technologies for transcriptome profiling; however, they both have their own inherent strengths and limitations. Researchers from Fudan University aimed to analyze the correlation between microarrays and RNA-Seq detection of transcripts in the same tissue sample to explore the reproducibility between the techniques.

Using data of RNA-Seq v2 and three different microarrays provided by The Cancer Genome Atlas, 11,120 genes of 111 lung squamous cell carcinoma samples were simultaneously detected by the four methods. Then the researchers analyzed the Pearson correlation between microarrays and RNA-Seq. Finally, in the six comparison results, 9984 (89.8%) genes, irrespective of which two methods were used, simultaneously showed the existence of correlation, whereas only 83 (0.1%) genes proved to have no significant correlation in either comparison. In addition, the comparisons between 3266 (29.3%) genes showed high correlation (R≥0.8) in all six comparisons, only for 1643 (14.8%) genes correlation were not as high in either comparison. Meanwhile, transcripts with extreme high or low expression levels were more highly discrepant across the methods. In conclusion, they found that, for most transcripts, the results obtained by RNA-Seq and microarrays were highly reproducible.

Overlap of correlated transcripts in the six different
comparative analyses between any two techniques

rna-seq

A and C. The number of transcripts in different classifications according to whether the transcript was significantly correlated (p < 0.05)/highly correlated (R ≥ 0.8) or not in each of the six comparative analyses. B and D. The number of transcripts with significant/high correlation in 0 to 6 comparative analyses, respectively. For example, 228 (5 of 6 analyses) means 228 transcripts which represented significant correlation in 5 of 6 comparisons.

Chen L, Sun F, Yang X, Jin Y, Shi M, Wang L, Shi Y, Zhan C, Wang Q. (2017) Correlation between RNA-Seq and microarrays results using TCGA data. Gene [Epub ahead of print]. [abstract]

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