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.

Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("BayesKnockdown")
AuthorWilliam Chad Young
Bioconductor views Bayesian GeneExpression GeneTarget Network NetworkInference
Date of publicationNone
MaintainerWilliam Chad Young <wmchad@uw.edu>
LicenseGPL-3
Version1.2.0

View on Bioconductor

Files

DESCRIPTION
NAMESPACE
NEWS
R
R/BayesKnockdown.r R/assert.r
build
build/vignette.rds
data
data/lincs.kd.RData
inst
inst/doc
inst/doc/BayesKnockdown.R
inst/doc/BayesKnockdown.pdf
inst/doc/BayesKnockdown.rnw
man
man/BayesKnockdown.Rd man/BayesKnockdown.diffExp.Rd man/BayesKnockdown.es.Rd man/lincs.kd.Rd
vignettes
vignettes/BayesKnockdown.bib
vignettes/BayesKnockdown.rnw
vignettes/auto
vignettes/auto/BayesKnockdown.el
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Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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