One of the more vexing and difficult issues in molecular biology is the use of PCR as a quantitative assay. It is easy to find a wide range of approaches to this, and even easier to find papers that refer to “semi-quantitative” PCR. As one might expect, there is a range of quality in the literature when it comes to these assays. Worse, though, is the paucity of information that is often provided – things like the numbers of replicates, statistical tools used to analyze the data, and the like. THis makes it hard to follow many studies, and to replicate the work of others. (Needless to say, reviewing these papers is a bear.)
There has been a mini-debate of sorts in The Plant Cell over the past few years, and a recent issue has two additions to the discussion. Follow below the fold for more.
A recent editorial (provided in its entirety – until TPC tells me to delete it):
Guidelines for Quantitative RT-PCR
Editor in Chief
Something I notice often in supervising new graduate students is their frustration with the irreproducibility of their biological data. For example, I am asked frequently “how many biological replicates do I need for qRT-PCR”? The answer, of course, lies first with the students themselves. They need as many replicates as will persuade them of the validity of their observations. However, for many—especially younger students who lack experience of the degrees of variability that data can show—such general guidelines are inadequate. In this issue, Ivo Rieu and Stephen Powers attempt to answer such questions and provide guidelines for the experimental design and statistical analysis of qRT-PCR data from the statistician’s perspective. From The Plant Cell perspective, these are guidelines; any attempt to impose such analysis as standard while we are still struggling to persuade authors of the deficiencies of “semiquantitative” RT-PCR would be a difficult, if not impossible, task. However, I hope that readers of The Plant Cell find this advice useful in designing experiments and analyzing qRT-PCR data. Of course, even where these guidelines are adopted, we will remain heavily dependent on authors to distinguish the data of true biological significance from those of only statistical significance.
Some items from Rieu and Powers:
Two recent letters to the editor of The Plant Cell (Gutierrez et al., 2008; Udvardi et al., 2008) highlighted the importance of following correct experimental protocol in quantitative RT-PCR (qRT-PCR). In these letters, the authors outlined measures to allow precise estimation of gene expression by ensuring the quality of material, refining laboratory practice, and using a normalization of relative quantities of transcripts of genes of interest (GOI; also called target genes) where multiple reference genes have been analyzed appropriately. In this letter, we build on the issues raised by considering the statistical design of qRT-PCR experiments, the calculation of normalized gene expression, and the statistical analysis of the subsequent data. This letter comprises advice for taking account of, in particular, the first and the last of these three vital issues. We concentrate on the situation of comparing transcript levels in different sample types (treatments) using relative quantification, but many of the concerns, particularly those with respect to design, are equally applicable to absolute quantification.
The conclusion of the letter:
To make a valid comparison of treatments in qRT-PCR experiments, it is essential to begin with a statistical design that incorporates the concepts of randomization, blocking, and adequate (biological) replication. Subsequently, data analysis will benefit from appropriate data transformation and proper accounting of sources of variation due to the experimental design prior to making a statistical assessment of differences between treatments. Finally, it should be kept in mind that the strategy of relative quantification, as dealt with here, is only suitable for comparing results from a given primer pair between treatments and not for comparing results obtained with different primer pairs to each other.
Rieu I, Powers SJ. 2009. Real-Time Quantitative RT-PCR: Design, Calculations, and Statistics. The Plant Cell 21:1023.