A Sequence Based Validation of Gene Expression Microarray Data
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Published On: 19 January 2012
Copyright: © 2020 Gerhard G. Thallinger, Eva Obermayr, Pornpimol Charoentong, Dan Tong, Zlatko Trajanoski and Robert Zeillinger. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: Quantitative Reverse Transcription PCR (RT-qPCR) is often used to validate microarray data. Previous studies show different levels of correlation, without further investigation of influencing factors. Approach: We compared expression levels of 381 genes obtained from microarray hybridizations and from TaqMan based RT-qPCR assays. Correlation of expression levels was determined by comparing: (i) single genes across samples, (ii) all genes within a sample and (iii) the expression ratios of all genes in a sample using another sample as the reference. The influence of several parameters on the correlation was analyzed: (i) variation in transcript set targeted by the microarray probe and the PCR assay, (ii) variation in amplicon probe position relative to 3' end of transcript, (iii) variation in efficiency of the PCR reaction and (iv) normalization of the PCR data. Results: The 381 genes covered by RT-qPCR had 494 matching probes on the microarray. 397 probes with a matching transcript set were identified via a rigid sequence-based validation. Correlation was significantly higher among matching transcript sets and probes closer to the 3' end. Adjustments for different amplification efficiencies had either no influence or decreased correlation. Normalization of qPCR data consistently reduced correlation for all analysis approaches. Conclusion: Current clinical research uses microarrays to select genes of interest and evaluates these genes using qPCR. Therefore, it is important that expression levels measured by both techniques be highly correlated. High correlation can be achieved if the targeted transcript sets match, whereas normalization and efficiency correction can have a negative influence.
- DNA microarrays
- TaqMan quantitative reverse-transcription PCR