My 5 cents
Found some SRA data related to roots infected with a fungus. Got personal interest in analyzing these data. SRA data did not contain replicates
In the meantime, the authors of the SRA data published a paper. However, neither the DE genes nor the GO enrichment published had sense to me, as no defense genes appeared in the list of the DE genes. I unknown the reasons for that
However, we analyze the data through edgeR following their particular instructions for samples containing not replicates. And I must say I felt very confident with these results, as a set of defense DE genes appeared after 7 days related to the biotrophic fungus we initially infected the roots, that were replaced by another set of defense genes typical of necrotrophic organisms after 15 days: The list of DE genes were not merely including defense genes. They contain a numerous collection of other known genes related to infections. This process is very well described in many occasions, and it serves as a model
We then got interested in analyzing the metatranscriptomic of these sample to try to explain these results. And it turn that after 7 days, a myriad of new opportunistic organisms emerged that included necrotrophic fungus and bacteria that were taking advantages of the initial fungal infection. The results have been recently published in BMC
So I cannot be convinced of any other idea that data with no replicates are not useful. I have seen this has not been my case, and that after a careful management and analysis of the data, you can get very useful results
I can only shake my head when reading studies like this. First of all they use simulated data, not a single confirmation experiment was done to back up their strategy. Second, they benchmark (among others) against DESeq (deprecated since 2014 upon DESeq2 release) and NOI-seq (now deprecated in the now Bioc version). And third, why would you make a study to develop methods to process inherently underpowered (n=1) studies? If you do not have replicates then you cannot make any claims, simply as that. No way possible you can separate technical noise from biological effects. They should have generated some data themselves (or download experiments with replicates) and then compare their method with standard approaches that use replicates. False discovery rate would probably skyrocket.
Hi- I think I know what you mean and I agree but I think sometimes this statement is exaggerated. There are circumstances where even n=1 can be informative, even if not conclusive, at least to formulate further hypotheses. For example, if you expect low variability to start with (e.g. cell cultures), the effect of the treatment is large and you have some expectation of what genes should change and what should stay the same, even n=1 is something useful. Besides, in some cases n=1 may be all you have so either you do the experiment with that one or you don't do it at all (which may be wiser sometimes but not always, it depends...).
On the other hand, in some cases n=2 or 3 is only marginally better than n=1 and it may give a false sense of security. For example, if you assign people completely blindly to treatment and control, with very small n you have good chances that confounders like sex or age are completely associated to the condition and you get very significant changes which in fact are misleading.
Sorry - it's only that sometimes I have the impression that n=1 is taken as "useless" while n=2 as "ok, now everything's fine".
I agree that replicates are necessary, but I am not sure you cannot make any claims at all. If you have geneA with 10 counts in both conditions and geneB that goes from 10 to 1000 counts, would you say that the probabilities that those genes are differentially expressed are completely equal?
The difference in counts between A and B can be convincing but without randomization of replicates you can't be sure that the difference is due to the treatment applied. The difference may be due to something other than the treatment (e.g. sex, age, handling of samples etc), unless you have some prior belief that tells you that that gene is unlikely to be different for reasons other than the treatment applied (basically I just rephrased my previous comment)
I agree. My main point was that I don't think that the values are completely random in a single-replicate experiment. As you pointed out in the earlier comment, having 2 replicates does not automatically transform complete noise into perfect signal.
Sure, this suggests that this single gene is DEG, but this is nothing you can base a claim on. With claim I mean biological messages like alterations of pathways, groups of genes reacting upon treatment, drug response etc. You are right though that n=1 is not completely useless but for a systematic analysis it is not reliable and I guess most people do a RNA-seq since they want a global picture. For single gene studies one can and should do qPCR or similar low-throughput methods.