Difference between revisions of "GGB-correlation-snippet.R"
From Organic Design wiki
(Fixing parameters (untested)) |
(Get correlation script working) |
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| − | p | + | # p (genes) by n (slides) matrix |
| − | n | + | # m=p keeping fdr notation |
| − | |||
| − | |||
| − | |||
| − | |||
| − | kcorrelation <- rgamma(2*m1 shape=k, rate=k) # p49 2001 NEWTON paper | + | m <- p <- 1000 |
| − | DEscales <- | + | nreps <- 5 |
| + | |||
| + | n <- nreps * 2 | ||
| + | pi0 <- 0.95 | ||
| + | |||
| + | m1 <- round(m * (1-pi0)) | ||
| + | m0 <- p - m1 | ||
| + | |||
| + | params <- c(2.74886, 1.36546, 4.12844) # "IPTG-a parameters" | ||
| + | k <- 100 | ||
| + | |||
| + | a.shape <- params[1] | ||
| + | a0.shape <- params[2] | ||
| + | scale <- params[3] | ||
| + | |||
| + | kcorrelation <- rgamma(2*m1, shape=k, rate=k) # p49 2001 NEWTON paper | ||
| + | DEscales <- rgamma(2*m1, shape=a0.shape, rate=scale) | ||
DEscales <- DEscales * kcorrelation | DEscales <- DEscales * kcorrelation | ||
| Line 13: | Line 25: | ||
EEscales <- rgamma(m0 , shape=a0.shape, rate=scale) | EEscales <- rgamma(m0 , shape=a0.shape, rate=scale) | ||
EEscales <- EEscales / kcorrelation | EEscales <- EEscales / kcorrelation | ||
| − | |||
| − | X <- rgamma( | + | scales <- c(rep(DEscales, each=nreps), rep(EEscales, each=2*nreps)) |
| − | dim(X) <- c(p | + | X <- rgamma(n* p, a.shape, rate=scales) |
| − | X <- t(X) | + | dim(X) <- c(n,p) |
| + | |||
| + | X <- t(X) | ||
| + | |||
| + | # Graphical check | ||
| + | pairs(log2(X)) | ||
Revision as of 23:43, 28 August 2006
- p (genes) by n (slides) matrix
- m=p keeping fdr notation
m <- p <- 1000 nreps <- 5
n <- nreps * 2 pi0 <- 0.95
m1 <- round(m * (1-pi0)) m0 <- p - m1
params <- c(2.74886, 1.36546, 4.12844) # "IPTG-a parameters" k <- 100
a.shape <- params[1] a0.shape <- params[2] scale <- params[3]
kcorrelation <- rgamma(2*m1, shape=k, rate=k) # p49 2001 NEWTON paper DEscales <- rgamma(2*m1, shape=a0.shape, rate=scale) DEscales <- DEscales * kcorrelation
kcorrelation <- rgamma(m0, shape=k, rate=k) EEscales <- rgamma(m0 , shape=a0.shape, rate=scale) EEscales <- EEscales / kcorrelation
scales <- c(rep(DEscales, each=nreps), rep(EEscales, each=2*nreps)) X <- rgamma(n* p, a.shape, rate=scales) dim(X) <- c(n,p)
X <- t(X)
- Graphical check
pairs(log2(X))
