Which regression model is right for your RNA-Seq analysis?

Next 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. Boston University researchers 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. They evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, they incorporate the data adaptive method in order to compare false positive rates.

When 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.

Empirical power of covariate models


Empirical power of covariate models from balanced design with N D=1  = 10 and μ D=0  = 1000. The power of Negative Binomial with true dispersion (NB), and Firth’s Logistic (FL) regressions at significance level 0.05 and 0.01 is shown in the figure. Black dotted horizontal lines represent 95 and 90% power. The odds ratios between covariates and case–control status (CovOR = 1.2 and 5) are partitioned by vertical black dotted lines. The number covariates (0, 1, 2, 3, 5 (, and 10)) in the model are positioned within each CovOR. Dotted lines within each symbol represent the 95% confidence interval. a Balanced design from N D=1   = 10, μ D=0  = 1000, dispersion = 0.01, and log2fc = 0.3. b Balanced design of N D=1   = 25, μ D=0  = 1000, dispersion = 1, and log2fc = 2

The researchers 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.

Choi SH, Labadorf AT, Myers RH, Lunetta KL, Dupuis J, DeStefano AL. (2017) Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis. BMC Bioinformatics 18(1):91. [article]

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