TargetScore-package: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information

Description

Infer the posterior distributions of microRNA targets by probabilistically modeling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variational Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features.

Arguments

Details

ll{ Package: TargetScore Type: Package Version: 1.1.5 Date: 2013-10-15 License: GPL-2 } The front-end main function targetScore should be used to obtain the probablistic score of miRNA target. The workhourse function is vbgmm, which implementates multivariate variational Bayesian Gaussian mixture model.

References

Lim, L. P., Lau, N. C., Garrett-Engele, P., Grimson, A., Schelter, J. M., Castle, J., Bartel, D. P., Linsley, P. S., and Johnson, J. M. (2005). Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature, 433(7027), 769-773.

Bartel, D. P. (2009). MicroRNAs: Target Recognition and Regulatory Functions. Cell, 136(2), 215-233.

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer, Information Science and Statistics. NY, USA. (p474-486)

Examples

Run this code

library(TargetScore)
ls("package:TargetScore")

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