期刊
BRIEFINGS IN BIOINFORMATICS
卷 19, 期 1, 页码 65-76出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbw092
关键词
RNA-seq; differential analysis; benchmark data set
资金
- French Agence Nationale de la Recherche (project MixStatSeq) [ANR-13-JS01-0001-01]
- French Institut National de la Recherche Agronomique AIP bioressource project
- GIS Infrastructures en Biologie Sante et Agronomie AO Platform call
- LabEx Saclay Plant Sciences-SPS [ANR-10-LABX-0040-SPS]
- Agence Nationale de la Recherche (ANR) [ANR-13-JS01-0001] Funding Source: Agence Nationale de la Recherche (ANR)
Numerous statistical pipelines are now available for the differential analysis of gene expression measured with RNA-sequencing technology. Most of them are based on similar statistical frameworks after normalization, differing primarily in the choice of data distribution, mean and variance estimation strategy and data filtering. We propose an evaluation of the impact of these choices when few biological replicates are available through the use of synthetic data sets. This framework is based on real data sets and allows the exploration of various scenarios differing in the proportion of non-differentially expressed genes. Hence, it provides an evaluation of the key ingredients of the differential analysis, free of the biases associated with the simulation of data using parametric models. Our results show the relevance of a proper modeling of the mean by using linear or generalized linear modeling. Once the mean is properly modeled, the impact of the other parameters on the performance of the test is much less important. Finally, we propose to use the simple visualization of the raw P-value histogram as a practical evaluation criterion of the performance of differential analysis methods on real data sets.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据