A Bayesian measurement error model for two-channel cell-based RNAi data with replicates

Chung Hsing Chen, Wen Chi Su, Chih Yu Chen, Jing Ying Huang, Fang Yu Tsai, Wen Chang Wang, Chao A. Hsiung, King Song Jeng, I. Shou Chang

研究成果: 雜誌貢獻文章

1 引文 斯高帕斯(Scopus)

摘要

RNA interference (RNAi) is an endogenous cellular process in which small double-stranded RNAs lead to the destruction of mRNAs with complementary nucleoside sequence. With the production of RNAi libraries, large-scale RNAi screening in human cells can be conducted to identify unknown genes involved in a biological pathway. One challenge researchers face is how to deal with the multiple testing issue and the related false positive rate (FDR) and false negative rate (FNR). This paper proposes a Bayesian hierarchical measurement error model for the analysis of data from a two-channel RNAi high-throughput experiment with replicates, in which both the activity of a particular biological pathway and cell viability are monitored and the goal is to identify short hair-pin RNAs (shRNAs) that affect the pathway activity without affecting cell activity. Simulation studies demonstrate the flexibility and robustness of the Bayesian method and the benefits of having replicates in the experiment. This method is illustrated through analyzing the data from a RNAi high-throughput screening that searches for cellular factors affecting HCV replication without affecting cell viability; comparisons of the results from this HCV study and some of those reported in the literature are included.
原文英語
頁(從 - 到)356-382
頁數27
期刊Annals of Applied Statistics
4
發行號1
DOIs
出版狀態已發佈 - 三月 2010
對外發佈Yes

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Modelling and Simulation
  • Statistics and Probability

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    Chen, C. H., Su, W. C., Chen, C. Y., Huang, J. Y., Tsai, F. Y., Wang, W. C., Hsiung, C. A., Jeng, K. S., & Chang, I. S. (2010). A Bayesian measurement error model for two-channel cell-based RNAi data with replicates. Annals of Applied Statistics, 4(1), 356-382. https://doi.org/10.1214/11-AOAS496