Abstract
BackgroundNext generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates.ResultsWhen the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth’s logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression.ConclusionsWe conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth’s logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.
Highlights
Generation sequencing provides a count of Ribonucleic Acid (RNA) molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements
These RNA sequencing (RNA-Seq) methods produce data that can be transformed into numerical values that are proportional to the abundance of RNA molecules and reflect the expression and turnover of those molecules
Large sample size and low dispersion generally yielded Type-I error rates that were close to the specified alpha levels as shown in Additional file 3: Figure S1
Summary
Generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. Generation sequencing (NGS) gene expression measurement methods simultaneously quantify tens of thousands of unique Ribonucleic Acid (RNA) molecules extracted from biological samples. These RNA sequencing (RNA-Seq) methods produce data that can be transformed into numerical values that are proportional to the abundance of RNA molecules and reflect the expression and turnover of those molecules. The Negative Binomial (NB) distribution appropriately models the biological dispersion of a gene, and NB regression has been used to analyze RNA-Seq data. When Y, a random variable, follows a NB distribution with mean (μ) and dispersion (φ), the parameterization of the probability mass function, expected value, and variance of Y are
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