The Annals of Applied Statistics

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, and I-Shou Chang

Full-text: Open access


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.

Article information

Ann. Appl. Stat., Volume 6, Number 1 (2012), 356-382.

First available in Project Euclid: 6 March 2012

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Bayesian hierarchical models HCV replication high-throughput screening multiple hypothesis tests RNA interference viral-host interactions


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. Ann. Appl. Stat. 6 (2012), no. 1, 356--382. doi:10.1214/11-AOAS496.

Export citation


  • Birmingham, A., Selfors, L. M., Forster, T., Wrobel, D., Kennedy, C. J., Shanks, E., Santoyo-Lopez, J., Dunican, D. J., Long, A., Kelleher, D., Smith, Q., Beijersbergen, R. L., Ghazal, P. and Shamu, C. E. (2009). Statistical methods for analysis of high-throughput RNA interference screens. Nat. Methods 6 569–575.
  • Börner, K., Hermle, J., Sommer, C., Brown, N. P., Knapp, B., Glass, B., Kunkel, J., Torralba, G., Reymann, J., Beil, N., Beneke, J., Pepperkok, R., Schneider, R., Ludwig, T., Hausmann, M., Hamprecht, F., Erfle, H., Kaderali, L., Kräusslich, H.-G. and Lehmann, M. J. (2010). From experimental setup to bioinformatics: An RNAi screening platform to identify host factors involved in HIV-1 replication. Biotechnol. J. 5 39–49.
  • Boutros, M. and Ahringer, J. (2008). The art and design of genetic screens: RNA interference. Nat. Rev. Genet. 9 554–566.
  • Boutros, M., Bras, L. P. and Huber, W. (2006). Analysis of cell-based RNAi screens. Genome Biol. 7 R66.
  • Box, G. E. P. and Cox, D. R. (1964). An analysis of transformations. J. R. Stat. Soc. Ser. B Stat. Methodol. 26 211–252.
  • Chapman, E. J. and Carrington, J. C. (2007). Specialization and evolution of endogenous small RNA pathways. Nat. Rev. Genet. 8 884–896.
  • Chen, C. H., Su, W. C., Chen, C. Y., Huang, J. Y., Tsai, F. Y., Wang, W. C., Hsiung, C. A., Jeng, K. S. and Chang, I. S. (2011). Supplement to “A Bayesian measurement error model for two-channel cell-based RNAi data with replicates.” DOI:10.1214/11-AOAS496SUPP.
  • Cherry, S. (2009). What have RNAi screens taught us about viral-host interactions? Curr. Opin. Microbiol. 12 446–452.
  • Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. J. Amer. Statist. Assoc. 74 829–836.
  • Echeverri, C. J., Beachy, P. A., Baum, B., Boutros, M., Buchholz, F., Chanda, S. K., Downward, J., Ellenberg, J., Fraser, A. G., Hacohen, N., Hahn, W. C., Jackson, A. L., Kiger, A., Linsley, P. S., Lum, L., Ma, Y., Mathey-Prévôt, B., Root, D. E., Sabatini, D. M., Taipale, J., Perrimon, N. and Bernards, R. (2006). Minimizing the risk of reporting false positives in large-scale RNAi screens. Nat. Methods 3 777–779.
  • Elbashir, S. M., Harborth, J., Lendeckel, W., Yalcin, A., Weber, K. and Tuschl, T. (2001). Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells. Nature 411 494–498.
  • Fire, A., Xu, S., Montgomery, M. K., Kostas, S. A., Driver, S. E. and Mello, C. C. (1998). Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391 806–811.
  • Gelman, A., Meng, X.-L. and Stern, H. (1996). Posterior predictive assessment of model fitness via realized discrepancies. Statist. Sinica 6 733–807.
  • Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (2003). Bayesian Data Analysis, 2nd ed. Chapman and Hall/CRC, Boca Raton, FL.
  • Gottardo, R. and Raftery, A. (2009). Bayesian robust transformation and variable selection: A unified approach. Canad. J. Statist. 37 361–380.
  • Gottardo, R., Raftery, A. E., Yeung, K. Y. and Bumgarner, R. E. (2006). Bayesian robust inference for differential gene expression in microarrays with multiple samples. Biometrics 62 10–18, 313.
  • Hannon, G. J. and Zamore, P. D. (2003). Small RNAs, Big Biology: Biochemical Studies of RNA Interference. A Guide to Gene Silencing. Cold Spring Harbor Laboratory Press, New York.
  • Lewin, A., Richardson, S., Marshall, C., Glazier, A. and Aitman, T. (2006). Bayesian modeling of differential gene expression. Biometrics 62 1–9, 313.
  • Lo, K. and Gottardo, R. (2007). Flexible empirical Bayes models for differential gene expression. Bioinformatics 23 328–335.
  • Malo, N., Hanley, J. A., Cerquozzi, S., Pelletier, J. and Nadon, R. (2006). Statistical practice in high-throughput screening data analysis. Nat. Biotechnol. 24 167–175.
  • Moffat, J., Grueneberg, D. A., Yang, X., Kim, S. Y., Kloepfer, A. M., Hinkle, G., Piqani, B., Eisenhaure, T. M., Luo, B., Grenier, J. K., Carpenter, A. E., Foo, S. Y., Stewart, S. A., Stockwell, B. R., Hacohen, N., Hahn, W. C., Lander, E. S., Sabatini, D. M. and Root, D. E. (2006). A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124 1283–1298.
  • Newton, M. A., Noueiry, A., Sarkar, D. and Ahlquist, P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5 155–176.
  • Robert, C. P. and Casella, G. (2004). Monte Carlo Statistical Methods, 2nd ed. Springer, New York.
  • Ryan, K. J. and Ray, C. G. (2004). Sherris Medical Microbiology: An Introduction to Infectious Diseases. McGraw-Hill, New York.
  • Scott, J. G. and Berger, J. O. (2006). An exploration of aspects of Bayesian multiple testing. J. Statist. Plann. Inference 136 2144–2162.
  • Supekova, L., Supek, F., Lee, J., Chen, S., Gray, N., Pezacki, J. P., Schlapbach, A. and Schultz, P. G. (2008). Identification of human kinases involved in hepatitis C virus replication by small interference RNA library screening. J. Biol. Chem. 283 29–36.
  • Tai, A. W., Benita, Y., Peng, L. F., Kim, S.-S., Sakamoto, N., Xavier, R. J. and Chung, R. T. (2009). A functional genomic screen identifies cellular cofactors of hepatitis C virus replication. Cell Host Microbe 5 298–307.
  • Zhang, X. D., Yang, X. C., Chung, N., Gates, A., Stec, E. M., Kunapuli, P., Holder, D. J., Ferrer, M. and Espeseth, A. S. (2006). Robust statistical methods for hit selection in RNA interference high-throughput screening experiments. Pharmacogenomics 7 299–309.
  • Zhang, X. D., Kuan, P. F., Ferrer, M., Shu, X., Liu, Y. C., Gates, A. T., Kunapuli, P., Stec, E. M., Xu, M., Marine, S. D., Holder, D. J., Strulovici, B., Heyse, J. F. and Espeseth, A. S. (2008). Hit selection with false discovery rate control in genome-scale RNAi screens. Nucleic Acids Res. 36 4667–4679.

Supplemental materials

  • Supplementary material: A Computer algorithm for analyzing data from two-channel cell-based RNAi experiments with replicates. This note provides the hybrid MCMC algorithm for sampling the posterior distribution used in Chen et al. (2011) and several observations used in designing this algorithm so as to make it more efficient.