Power analysis for RNA-Seq differential expression studies.
BMC Bioinformatics
; 18(1): 234, 2017 May 03.
Article
em En
| MEDLINE
| ID: mdl-28468606
ABSTRACT
BACKGROUND:
Sample size calculation and power estimation are essential components of experimental designs in biomedical research. It is very challenging to estimate power for RNA-Seq differential expression under complex experimental designs. Moreover, the dependency among genes should be taken into account in order to obtain accurate results.RESULTS:
In this paper, we propose a simulation based procedure for power estimation using the negative binomial distribution and assuming a generalized linear model (at the gene level) that considers the dependence between gene expression level and its variance (dispersion) and also allows equal or unequal dispersion across conditions. We compared the performance of both Wald test and likelihood ratio test under different scenarios. The null distribution of the test statistics was simulated for the desired false positive control to avoid excess false positives with the usage of an asymptotic chi-square distribution. We applied this method to the TCGA breast cancer data set.CONCLUSIONS:
We provide a framework for power estimation of RNA-Seq data. The proposed procedure is able to properly control the false positive error rate at the nominal level.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Estatística como Assunto
/
Análise de Sequência de RNA
/
Perfilação da Expressão Gênica
Limite:
Humans
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article