BayesKnockdown: Posterior Probabilities for Knockdown Data In BayesKnockdown: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data

Description

Calculates posterior probabilities for edges from a knocked-down gene to each of a set of potential target genes. More generally, it calculates posterior probabilities between a single predictor variable and each of a set of response variables, incorporating prior probabilities potentially unique to each response variable.

Usage

 `1` ```BayesKnockdown(x, y, prior = 0.5, g = sqrt(length(x))) ```

Arguments

 `x` `n`-vector of predictor data. In knockdown experiments, this is a vector of the expression levels of the knocked-down gene across n experiments. `y` Outcome matrix: `p` (number of outcomes measured) by `n` (number of samples). In knockdown experiments, this is a matrix of all the gene measurements for genes that were not knocked down. `prior` Prior probabilities for the outcome variables. Defaults to 0.5 for all variables. `g` The value to use for Zellner's g-prior. Defaults to the square root of the number of observations.

Value

A vector of `p` posterior probabilities indicating the probability of a relationship between the predictor variable and each outcome variable.

Examples

 ```1 2 3 4 5 6 7``` ```n <- 100; p <- 10; x <- rnorm(n); y <- matrix(nrow=p, data=rnorm(n*p)); y[3,] <- y[3,] + 0.5*x; BayesKnockdown(x, y); ```

BayesKnockdown documentation built on Nov. 8, 2020, 5:48 p.m.