Data-based RNA-seq simulations by binomial thinning.

ABSTRACT:

Background

With the explosion in the number of methods designed to analyze bulk and single-cell RNA-seq 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.

Results

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 (

SUBMITTER: Gerard D 

PROVIDER: S-EPMC7245910 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature