Package: BayesKnockdown 1.33.0

William Chad Young

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.

Authors:William Chad Young

BayesKnockdown_1.33.0.tar.gz
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BayesKnockdown_1.33.0.tgz(r-4.4-any)BayesKnockdown_1.33.0.tgz(r-4.3-any)
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BayesKnockdown.pdf |BayesKnockdown.html✨
BayesKnockdown/json (API)
NEWS

# Install 'BayesKnockdown' in R:
install.packages('BayesKnockdown', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • lincs.kd - LINCS L1000 Knockdown Example Dataset

On BioConductor:BayesKnockdown-1.31.0(bioc 3.20)BayesKnockdown-1.30.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

networkinferencegeneexpressiongenetargetnetworkbayesian

3.30 score 1 scripts 260 downloads 3 exports 2 dependencies

Last updated 23 days agofrom:ee329ba0ef. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-winOKOct 30 2024
R-4.5-linuxOKOct 30 2024
R-4.4-winOKOct 30 2024
R-4.4-macOKOct 30 2024
R-4.3-winOKOct 30 2024
R-4.3-macOKOct 30 2024

Exports:BayesKnockdownBayesKnockdown.diffExpBayesKnockdown.es

Dependencies:BiobaseBiocGenerics

networkBMA

Rendered fromBayesKnockdown.rnwusingutils::Sweaveon Oct 30 2024.

Last update: 2016-07-22
Started: 2016-07-22