Confounding factors when comparing effects of expression between RNA-seq samples and qPCR samples?

I'm trying to design an experiment comparing the effects of various expression levels of Gene X between Sample A and Sample B. If measurements in Gene X expression are from RNA-seq in Sample A and qPCR in Sample B, what assumptions must be made in comparing the effects of expression on a trait between these two samples?

Long version:

Expression levels of Gene X in Sample A can only be measured by RNA-seq (standard Illumina high-throughput). Expression levels of Gene X in Sample B can only be measured by qPCR – a standard qPCR protocol in which RNA is extracted and used to produce single-strand cDNA, from which the target gene is amplified and measured with a real-time quantitative PCR machine. In either case, with qPCR or RNA-seq, expression of Gene X is standardized against expression of the same reference gene.

The reason for the methods for each is that:
- There are too many SNPs in Sample A to design qPCR primers that would work for enough samples
- RNA-seq data is already available for Sample A, so cost not a factor
- RNA-seq of Sample B would be too costly and is unnecessary since qPCR primers will work for all samples

I wish to build linear models showing the effects of expression on the trait for each sample. In considering whether this is a valid approach, there is concern regarding possible bias from the two different methods of measurement. Where can bias be introduced in either qPCR or RNA-seq? How can either method be more or less accurate in measuring gene expression levels?

rna-seq qrtpcr qpcr transcriptomics expression

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updated 7.3 years ago by Devon Ryan

• written 7.3 years ago by michael.nagle

1 answer

I'm trying to design an experiment

No you're not, you're trying to salvage a poorly constructed experimental design that could have been avoided.

If you had at least attempted to quantify expression using both methods in a few samples you'd have a decent shot at coming up with a fairly accurate conversion between the two, but barring that your options are limited. You might get lucky and the following will work:

Choose reference samples in each group that should be reasonably similar it terms of expression
Normalize within each group to said samples
Hope that either you don't have particularly astute reviewers or that you have sufficient independent evidence to validate your findings, since whatever you do is unlikely to pass reviewer muster

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written 7.3 years ago by Devon Ryan

I'm working on a proposal for possible future research and trying to make the most of available resources and a limited budget.

There's no reason we can't perform both qPCR and RNA-seq for some number of samples in Sample A. It is possible for us to find enough samples in Sample A without SNPs that would prevent use of the same good qPCR primers. We could then compare reference-normalized expression levels between qPCR and RNA-seq to calculate a conversion factor. What is unclear to me, however, is why that conversion factor would be anything other than 1. Furthermore, I don't know if/why the conversion factor would change over a range of expression levels.

I am trying to ascertain specifically where bias can come from in order to reach an evidence-based conclusion on whether this approach can be valid. Maybe it will be best to refute this idea and instead RNA-seq everything with baits to select the gene of interest and standards, but the rejection of the cheaper and faster approach will need to be justified.

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written 7.3 years ago by michael.nagle