Not every gene is special: Modelling scale controls the false discovery rate when analysing high-throughput sequencing data

The useful part

Most tools employ normalisations to attempt to correct for technical variation in the count data. Previously, we demonstrated that these normalisations are often inappropriate due to incorrect assumptions regarding the overall scale (i.e., size) of the biological system in question. This enabled calculation of the false discovery rate (FDR) and sensitivity across the iterations.

How it works

• Our simulations showed that scale misspecification results in poor control of the FDR by several commonly used tools and that, counterintuitively, FDRs increased as the modelled difference between groups...
• This phenomenon was consistently observed in disparate types of HTS data and was remarkably consistent.
• Critically, we leveraged a 'real-world', non-permuted analysis of an RNA-seq dataset to demonstrate that the latter effect is not a result of our thinning/permutation approach.
• Overall, our work highlights the potentially unwitting choice between sensitivity and FDR control that all researchers are making when analysing sequencing data and provides guidance on choosing a...
• In this study, we used a combination of binomial thinning and permutation of sample groupings to produce 100 analysis iterations of 11 RNA-seq and other HTS datasets in which ~5% of all features are...

What to take from it

We established that increasing scale uncertainty also increased the minimum difference between groups required for a feature to be reported as differentially expressed.