Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.
BACKGROUND
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. 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.


RESULTS
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.


CONCLUSIONS
We 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.
Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, Massachusetts, USA.
Seung Hoan
SH
Choi
Department of Neurology, Boston University, 72 East Concord Street, Boston, Massachusetts, USA.
Adam T
AT
Labadorf
Department of Neurology, Boston University, 72 East Concord Street, Boston, Massachusetts, USA.
Richard H
RH
Myers
Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, Massachusetts, USA.
Kathryn L
KL
Lunetta
Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, Massachusetts, USA.
Josée
J
Dupuis
Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, Massachusetts, USA. adestef@bu.edu.|Department of Neurology, Boston University, 72 East Concord Street, Boston, Massachusetts, USA. adestef@bu.edu.
Anita L
AL
DeStefano
D001499:Bayes Theorem; D016022:Case-Control Studies; D019295:Computational Biology / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D006816:Huntington Disease / Q000175:diagnosis / Q000235:genetics*; D016015:Logistic Models; D008962:Models, Theoretical; D015203:Reproducibility of Results; D018401:Sample Size; D017423:Sequence Analysis, RNA / Q000379:methods*
Covariate effect; Firth’s logistic regression; Negative binomial regression; RNA-Sequencing analysis