Hi, I have isoform-level deep RNA-seq data from stringtie on about 800 people. I have created gene-level and isoform-level data using tximport. I have just looked at the voom mean-variance trend plots. At the gene level, the plot looks as usual (decreases at low expression levels and then levels off). For the isoform data, the plot looks different, the fit curve continues to decrease all along the x-axis without ever leveling off (so no variance stabilization). I have done this both with loose and quite stringent filtering (requiring at least 15 reads in 75% of individuals for the stringent filtering), and in both cases the plot looks the same. We do not need variance stabilization since we use the voom weights in subsequent analyses, but is this type of plot of any concern? Has this been seen for other isoform data? As expected the isoform data points cluster strongly on the left side of the graph, while the gene-level data cluster more in the middle. Thank you for any comments.

Ina,

Not an answer to you question, but a side note, it would be interesting to see if uncertainty on the isoform-level quantification is playing a role. With Salmon this is possible by adding arguments such as --numGibbsSamples 30 --thinningFactor 100 or alternatively --numBootstraps 30. Then tximport will import the inferential replicates in addition to the counts (or with varReduce=TRUE it will compute inferential variance matrices for you, as a new matrix in the txi list). I know you have StringTie quantification here, but maybe it would be possible to experiment running transcript quantification with Salmon on a few samples to investigate how it relates to the pattern you see.

From my work in isoform quantification, I can provide some intuition about what may be happening: even if the count is high for the gene/isoform, there can still be substantial uncertainty on the assignment of read counts to isoforms. Then across samples you may see high variance due to the stochastic assignment of many reads across those isoforms with a lot of shared exonic sequence.

Yes, problems with the voom mean-variance trend is totally to be expected with isoform-level data and it is a concern. It will cause voom to be less much less powerful than it usually would be. The problem is caused by variance inflation due to overlap (and hence ambiguity) between isoforms. This is why voom has only ever been recommended for gene-level data.

As mentioned by Michael Love, the variance inflation can be estimated by bootstrapping if you use Salmon (although I have different recommendations for how to feed it into voom). If you use Stringtie then there's no much you can do about it.