wmchad/BayesKnockdown: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data
A simple, fast Bayesian method for computing posterior probabilities for relationships between a single predictor variable and multiple potential outcome variables, incorporating prior probabilities of relationships. In the context of knockdown experiments, the predictor variable is the knocked-down gene, while the other genes are potential targets. Can also be used for differential expression/2-class data.
Getting started
Browse package contents
Package details |
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| Author | William Chad Young |
| Bioconductor views | Bayesian GeneExpression GeneTarget Network NetworkInference |
| Maintainer | William Chad Young <wmchad@uw.edu> |
| License | GPL-3 |
| Version | 0.99.4 |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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