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

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)356-382
Number of pages27
JournalAnnals of Applied Statistics
Volume4
Issue number1
DOIs
Publication statusPublished - Mar 2010
Externally publishedYes

Fingerprint

Measurement Error Model
Measurement errors
RNA
Interference
Cell
Pathway
Viability
Cells
High-throughput Screening
Multiple Testing
Screening
Bayesian Methods
Throughput
False Positive
Messenger RNA
High Throughput
Replication
Experiment
Flexibility
Measurement error

Keywords

  • Bayesian hierarchical models
  • HCV replication
  • High-throughput screening
  • Multiple hypothesis tests
  • RNA interference
  • Viral-host interactions

ASJC Scopus subject areas

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

Cite this

Chen, C. H., Su, W. C., Chen, C. Y., Huang, J. Y., Tsai, F. Y., Wang, W. C., ... 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

A Bayesian measurement error model for two-channel cell-based RNAi data with replicates. / Chen, Chung Hsing; Su, Wen Chi; Chen, Chih Yu; Huang, Jing Ying; Tsai, Fang Yu; Wang, Wen Chang; Hsiung, Chao A.; Jeng, King Song; Chang, I. Shou.

In: Annals of Applied Statistics, Vol. 4, No. 1, 03.2010, p. 356-382.

Research output: Contribution to journalArticle

Chen, CH, Su, WC, Chen, CY, Huang, JY, Tsai, FY, Wang, WC, Hsiung, CA, Jeng, KS & Chang, IS 2010, 'A Bayesian measurement error model for two-channel cell-based RNAi data with replicates', Annals of Applied Statistics, vol. 4, no. 1, pp. 356-382. https://doi.org/10.1214/11-AOAS496
Chen, Chung Hsing ; Su, Wen Chi ; Chen, Chih Yu ; Huang, Jing Ying ; Tsai, Fang Yu ; Wang, Wen Chang ; Hsiung, Chao A. ; Jeng, King Song ; Chang, I. Shou. / A Bayesian measurement error model for two-channel cell-based RNAi data with replicates. In: Annals of Applied Statistics. 2010 ; Vol. 4, No. 1. pp. 356-382.
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