TargetScore-package {TargetScore}R Documentation

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.

Details

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.

Author(s)

Yue Li <yueli@cs.toronto.edu>

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)

See Also

targetScore

Examples

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

[Package TargetScore version 1.22.0 Index]