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.

AuthorWilliam Chad Young
Date of publicationNone
MaintainerWilliam Chad Young <wmchad@uw.edu>
LicenseGPL-3
Version1.0.0

View on Bioconductor

Files in this package

BayesKnockdown/DESCRIPTION
BayesKnockdown/NAMESPACE
BayesKnockdown/NEWS
BayesKnockdown/R
BayesKnockdown/R/BayesKnockdown.r
BayesKnockdown/R/assert.r
BayesKnockdown/build
BayesKnockdown/build/vignette.rds
BayesKnockdown/data
BayesKnockdown/data/lincs.kd.RData
BayesKnockdown/inst
BayesKnockdown/inst/doc
BayesKnockdown/inst/doc/BayesKnockdown.R
BayesKnockdown/inst/doc/BayesKnockdown.pdf
BayesKnockdown/inst/doc/BayesKnockdown.rnw
BayesKnockdown/man
BayesKnockdown/man/BayesKnockdown.Rd BayesKnockdown/man/BayesKnockdown.diffExp.Rd BayesKnockdown/man/BayesKnockdown.es.Rd BayesKnockdown/man/lincs.kd.Rd
BayesKnockdown/vignettes
BayesKnockdown/vignettes/BayesKnockdown.bib
BayesKnockdown/vignettes/BayesKnockdown.rnw
BayesKnockdown/vignettes/auto
BayesKnockdown/vignettes/auto/BayesKnockdown.el

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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