Data-based RNA-seq Simulations by Binomial Thinning

Abstract

With the explosion in the number of methods designed to analyze bulk and single-cell RNAseq data, there is a growing need for approaches that assess and compare these methods. The usual technique is to compare methods on data simulated according to some theoretical model. However, as real data often exhibit violations from theoretical models, this can result in unsubstantiated claims of a method's performance. Rather than generate data from a theoretical model, in this paper we develop methods to add signal to real RNA-seq datasets. Since the resulting simulated data are not generated from an unrealistic theoretical model, they exhibit realistic (annoying) attributes of real data. This lets RNA-seq methods developers assess their procedures in non-ideal (model-violating) scenarios. Our procedures may be applied to both single-cell and bulk RNA-seq. We show that our simulation method results in more realistic datasets and can alter the conclusions of a differential expression analysis study. We also demonstrate our approach by comparing various factor analysis techniques on RNA-seq datasets. Our tools are available in the seqgendiff R package on the Comprehensive R Archive Network: https://cran.r-project.org/package=seqgendiff.

Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis

“…In real data sets, the true value of is unknown. To side-step this problem, we used binomial thinning 49,50 of real data sets (independently proposed as molecular cross-validation 51 ) to produce training and validation data with the same true expression matrix . The key idea of this approach is that a Binomial sample of a Poisson-distributed count is also (marginally) Poisson-distributed, meaning it can produce such training/validation data without requiring knowledge of the true .…”