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 language | English |
|---|---|
| Pages (from-to) | 356-382 |
| Number of pages | 27 |
| Journal | Annals of Applied Statistics |
| Volume | 4 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Mar 2010 |
| Externally published | Yes |
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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
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 journal › Article
}
TY - JOUR
T1 - A Bayesian measurement error model for two-channel cell-based RNAi data with replicates
AU - Chen, Chung Hsing
AU - Su, Wen Chi
AU - Chen, Chih Yu
AU - Huang, Jing Ying
AU - Tsai, Fang Yu
AU - Wang, Wen Chang
AU - Hsiung, Chao A.
AU - Jeng, King Song
AU - Chang, I. Shou
PY - 2010/3
Y1 - 2010/3
N2 - 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.
AB - 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.
KW - Bayesian hierarchical models
KW - HCV replication
KW - High-throughput screening
KW - Multiple hypothesis tests
KW - RNA interference
KW - Viral-host interactions
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UR - http://www.scopus.com/inward/citedby.url?scp=84870272220&partnerID=8YFLogxK
U2 - 10.1214/11-AOAS496
DO - 10.1214/11-AOAS496
M3 - Article
AN - SCOPUS:84870272220
VL - 4
SP - 356
EP - 382
JO - Annals of Applied Statistics
JF - Annals of Applied Statistics
SN - 1932-6157
IS - 1
ER -