Forum:Is low N in-vivo research a blind spot for RNA-seq?
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5 months ago
telroyjatter ▴ 210

My guess is that 70-80% (maybe higher) of RNA-seq experiments are with in vitro samples, as opposed to in vivo samples.

From what I've heard/read, it is very common for in vivo RNA-seq experiments with sample sizes of 5-20 per group to yield less than 20 (sometimes 0) differentially expressed genes after FDR-correction. In the field, it is common for papers doing experiments like this to use nominal p-values for this reason.

Some references:

https://www.nature.com/articles/srep40005

https://assets.researchsquare.com/files/rs-1551886/v1/b353c369-9ec2-410a-915d-6f61edf7d604.pdf?c=1657521872

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202276/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5511210/

https://www.nature.com/articles/mp2013170

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/art.41516

Now, when doing in vitro RNA-seq experiments, using sample sizes of 5-20 often does yield DEGs after FDR-correction. I wonder why this is. My two ideas are:

  1. Standardization is much easier in vitro than in vivo, and this reduces variability and enables effects to reach statistical significance. The variability you find in vivo can often prevent FDR-corrected DEGs from being identified.

  2. Experimental perturbations are more effective in vitro than in vivo. Cell cultures are more open systems that can more easily be perturbed than animals' bodies which are constantly fighting to reach homeostasis and resist experimental interventions.

Would love to know what you all think!

power metascience statistics sample size • 596 views
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I do not see the point discussing anecdotal evidence. Please provide some references for your claims, especially reporting nominal p-values and absence of DEGs. I cannot confirm any of that by personal experience.

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Thank you. I made an edit and added references.

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You picked tricky ones there, most seem to be in vivo but in humans, in a disease setting so inherently low n is underpowered. COmpared to mice or other model organisms in general humans are confounded by all kinds of effects, be it sex, eating habits, drug consumption, environmental cues, dietary status etc. For such studies low n is obviously not the way to get reliable results. It also depends on the effect size. If you compare n=5 cancer samples to n=5 normals you will likely get hundreds of DEGs. The references you give are not representative, it all depends on the interplay between sample size, sample similarity, confounders and most importantly effect size and underlying biology.

Towards 2, well yeah I think it's obvious that cells in a dish are more easy to manipulate than an organism. Lets just take CRISPR, you can easily bath a million single cells in a dish in viral soup to deliver the Cas9 cDNA and gRNA templates, e.g. into a pancreas cell line. But getting the same into a living mouse pancreas is hard, immune system will obviously do its part fighting these components even if you manage to somehow deliver it to the pancreas. I think that's an obvious consensus.

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5 months ago

My thoughts on this have very much not changed since you asked a similar question two years ago: Am I crazy, or are most published RNA-seq studies vastly underpowered?

Both your proposed explanations are in play - but I would say probably that the first is more important. An example of this you don't really mention is that if you do a sample of 5 humans, you have 5 different organisms: they have have different genetics, have had different life experiences, different things for breakfast this morning, etc. Where as 5 replicates of a cell line are (or at least started off) genetically identical. They all share the same life history etc. Its much better to regard 5 replicates of a cell line as being more like 5 samples from the same person as samples from 5 different people.

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