# BayesKnockdown.diffExp: Posterior Probabilities for 2-class Data In BayesKnockdown: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data

## Description

Calculates posterior probabilities for each gene in a set of experiments is differentially expressed between two sets of experimental conditions. More generally, it calculates posterior probabilities that each measured variable is different between two classes, incorporating prior probabilities potentially unique to each variable.

## Usage

 `1` ```BayesKnockdown.diffExp(y1, y2, prior = 0.5, g = sqrt(ncol(y1) + ncol(y2))) ```

## Arguments

 `y1` Condition 1 outcome matrix: `p` (number of outcomes measured) by `n1` (number of samples for condition 1). `y2` Condition 2 outcome matrix: `p` (number of outcomes measured) by `n2` (number of samples for condition 2). `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 (combined across both conditions).

## Value

A vector of `p` posterior probabilities indicating the probability that each outcome variable is different between the two classes.

## Examples

 ```1 2 3 4 5 6 7 8``` ```n1 <- 15; n2 <- 20; p <- 10; y1 <- matrix(nrow=p, data=rnorm(n1*p)); y2 <- matrix(nrow=p, data=rnorm(n2*p)); y2[3,] <- y2[3,] + 0.5; BayesKnockdown.diffExp(y1, y2); ```

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