Corrections: Variance Component Testing in the Longitudinal Mixed Effects Model

Biometrics ◽  
1995 ◽  
Vol 51 (3) ◽  
pp. 1196 ◽  
Keyword(s):  
Variance Component ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Component Testing ◽  
Variance Component Testing

scholarly journals Variance component testing for identifying differentially expressed genes in RNA-seq data

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3797 ◽  
Author(s):  
Sheng Yang ◽  
Fang Shao ◽  
Weiwei Duan ◽  
Yang Zhao ◽  
Feng Chen
Keyword(s):  
Variance Component ◽  
Negative Binomial ◽  
Null Distribution ◽  
Real Data ◽  
Differentially Expressed ◽  
Expression Data ◽  
Rna Seq ◽  
Component Testing ◽  
Optimal Power ◽  
Variance Component Testing

RNA sequencing (RNA-Seq) enables the measurement and comparison of gene expression with isoform-level quantification. Differences in the effect of each isoform may make traditional methods, which aggregate isoforms, ineffective. Here, we introduce a variance component-based test that can jointly test multiple isoforms of one gene to identify differentially expressed (DE) genes, especially those with isoforms that have differential effects. We model isoform-level expression data from RNA-Seq using a negative binomial distribution and consider the baseline abundance of isoforms and their effects as two random terms. Our approach tests the global null hypothesis of no difference in any of the isoforms. The null distribution of the derived score statistic is investigated using empirical and theoretical methods. The results of simulations suggest that the performance of the proposed set test is superior to that of traditional algorithms and almost reaches optimal power when the variance of covariates is large. This method is also applied to analyze real data. Our algorithm, as a supplement to traditional algorithms, is superior at selecting DE genes with sparse or opposite effects for isoforms.

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