BayesKnockdown: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data
Version 1.2.0

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

Package details

AuthorWilliam Chad Young
Bioconductor views Bayesian GeneExpression GeneTarget Network NetworkInference
MaintainerWilliam Chad Young <[email protected]>
Package repositoryView on Bioconductor
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BayesKnockdown documentation built on May 31, 2017, 12:55 p.m.