SimSeq: a nonparametric approach to simulation of RNA-sequence datasets

Bioinformatics
Sam Benidt, Dan Nettleton

Abstract

RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. We conduct simulation experiments based on the negative binomial distribution and our proposed nonparametric simulation algorithm. We compare performance between the two simulation experiments over a small subset of statistical methods for RNA-seq analysis available in the literature. We use as a benchmark the ability of a method to control the false discovery rate. Not surprisingly, methods based on parametric modeling assumptions seem to perform better with respect to false discovery rate control when data are simulated from parametric models rather than using our more realistic nonparametric simulation strategy. ...Continue Reading

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Dec 15, 2015·Statistical Applications in Genetics and Molecular Biology·Thomas Thorne
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Feb 13, 2021·NAR Genomics and Bioinformatics·Stijn HawinkelOlivier Thas

Related Concepts

Literature
RNA
Sequence Analysis, RNA
Simulation
Statistical Technique
R Programming Language

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