Difference between revisions of "LmFit-Weights.R"
(First principles on limma sigma hat calculation) |
m |
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| Line 16: | Line 16: | ||
RG <- new("RGList") | RG <- new("RGList") | ||
| − | RG$R <- matrix(rnorm(n*preps, | + | RG$R <- matrix(rnorm(n*preps,9,2), nc=preps) |
| − | RG$G <- matrix(rnorm(n*preps, | + | RG$G <- matrix(rnorm(n*preps,9,2), nc=preps) |
RG$printer <- structure(list(ngrid.c=1, ngrid.r=1, nspot.c=2, nspot.r=2), | RG$printer <- structure(list(ngrid.c=1, ngrid.r=1, nspot.c=2, nspot.r=2), | ||
class="printlayout") | class="printlayout") | ||
| Line 28: | Line 28: | ||
design <- rep(c(1,-1), preps/2) | design <- rep(c(1,-1), preps/2) | ||
| + | #design <- cbind(c(1,0,1,0,1,0,1,0,1,0),c(0,-1,0,-1,0,-1,0,-1,0,-1)) | ||
| + | #colnames(design) <- c("NHC1", "NHC1db") | ||
| + | #cont.matrix <- makeContrasts("NHC1 vs NHC1db" = NHC1 - NHC1db, levels=design) | ||
| + | |||
fit <- lmFit(MA, design=design, weights=NULL) | fit <- lmFit(MA, design=design, weights=NULL) | ||
| + | #fit2 <- contrasts.fit(fit, cont.matrix) | ||
fit <- eBayes(fit) | fit <- eBayes(fit) | ||
topTable(fit, adjust="none") | topTable(fit, adjust="none") | ||
| + | |||
| + | if( !is.null(dim(design)) ) { | ||
| + | design <- drop(design %*% c(1,1)) | ||
| + | } | ||
| Line 36: | Line 45: | ||
fit$sigma | fit$sigma | ||
apply(MA$M * outer(rep(1, n), design), 1, sd) | apply(MA$M * outer(rep(1, n), design), 1, sd) | ||
| − | + | apply(MA$M, 1, sd) | |
| + | plot(1:10) | ||
# Stderr component - se(x_bar) | # Stderr component - se(x_bar) | ||
drop(fit$stdev.unscaled) * fit$sigma | drop(fit$stdev.unscaled) * fit$sigma | ||
| − | apply(MA$M * outer(rep(1, n), design), 1, function(x){sd(x)/sqrt(length(x))}) | + | apply(MA$M * outer(rep(1, n), design), 1, function(x){sd(x)/sqrt(length(x))}) |
Revision as of 01:50, 2 August 2006
library(limma) options(digits=2) packageDescription("limma", field="Version")
- ========================================================================
- 0) cDNA example analysing M matrix in MAList
- ========================================================================
set.seed(2) n <- 50 preps <- 10
- ========================================================================
- 1) Generating a RGList for transformation into MAList
- ========================================================================
RG <- new("RGList") RG$R <- matrix(rnorm(n*preps,9,2), nc=preps) RG$G <- matrix(rnorm(n*preps,9,2), nc=preps) RG$printer <- structure(list(ngrid.c=1, ngrid.r=1, nspot.c=2, nspot.r=2),
class="printlayout")
- Ordering genes same ass topTable for convenience
- RG <- RG[c(4,2,1,3),]
MA <- MA.RG(RG) colnames(MA$M) <- paste("array", 1:preps, sep="") rownames(MA$M) <- paste("gene", 1:n, sep="")
design <- rep(c(1,-1), preps/2)
- design <- cbind(c(1,0,1,0,1,0,1,0,1,0),c(0,-1,0,-1,0,-1,0,-1,0,-1))
- colnames(design) <- c("NHC1", "NHC1db")
- cont.matrix <- makeContrasts("NHC1 vs NHC1db" = NHC1 - NHC1db, levels=design)
fit <- lmFit(MA, design=design, weights=NULL)
- fit2 <- contrasts.fit(fit, cont.matrix)
fit <- eBayes(fit) topTable(fit, adjust="none")
if( !is.null(dim(design)) ) {
design <- drop(design %*% c(1,1))
}
- Sigma component - sd(x)
fit$sigma apply(MA$M * outer(rep(1, n), design), 1, sd) apply(MA$M, 1, sd) plot(1:10)
- Stderr component - se(x_bar)
drop(fit$stdev.unscaled) * fit$sigma apply(MA$M * outer(rep(1, n), design), 1, function(x){sd(x)/sqrt(length(x))})
- ========================================================================
- 2) Adding some weights for MAList
- ========================================================================
RG$weights <- matrix(1, n,preps) RG$weights[row(RG$weights)<col(RG$weights)] <- 0 RG$weights
MA <- MA.RG(RG) colnames(MA$M) <- paste("array", 1:preps, sep="") rownames(MA$M) <- paste("gene", 1:n, sep="")
design <- rep(c(1,-1), preps/2)
fit <- lmFit(MA, design=design, weights=MA$weights)
fit <- eBayes(fit)
topTable(fit, adjust="none")
fit$df.residual
- Zero weights should be identical to NA's
MA$M[MA$weights==0] <- NA designMat <- outer(rep(1, n), design)
- Sigma component - sd(x)
fit$sigma apply(MA$M * designMat, 1, sd, na.rm=TRUE)
- Stderr component - se(x_bar)
drop(fit$stdev.unscaled) * fit$sigma apply(MA$M * designMat, 1, function(x,...){sd(x,...)/sqrt(length(x[!is.na(x)]))}, na.rm=TRUE)
- ========================================================================
- 3) Added some NA's for MAList (several lines above)
- ========================================================================
design <- rep(c(1,-1), preps/2) fit <- lmFit(MA, design=design, weights=MA$weights) fit <- eBayes(fit) topTable(fit, adjust="none") fit$df.residual
- Sigma component - sd(x)
fit$sigma apply(MA$M * designMat, 1, sd, na.rm=TRUE)
- Stderr component - se(x_bar)
drop(fit$stdev.unscaled) * fit$sigma apply(MA$M * designMat, 1, function(x,...){sd(x,...)/sqrt(length(x[!is.na(x)]))}, na.rm=TRUE)
- ========================================================================
- 3) Added weights and NA's simultaneously at random for MAList
- ========================================================================
set.seed(2) n <- 50 preps <- 10
RG <- new("RGList") RG$R <- matrix(rnorm(n*preps,8,2), nc=preps) RG$G <- matrix(rnorm(n*preps,8,2), nc=preps) RG$printer <- structure(list(ngrid.c=1, ngrid.r=1, nspot.c=2, nspot.r=2),
class="printlayout")
MA <- MA.RG(RG)
ncoords <- sample(n,round(n/2)) pcoords <- sample(preps,round(n/2), replace=TRUE)
MA$M[cbind(ncoords, pcoords)] <- NA
ncoords <- sample(n,round(n/2)) pcoords <- sample(preps,round(n/2), replace=TRUE)
MA$weights <- matrix(1, nc=preps, nr=n) MA$weights[cbind(ncoords, pcoords)] <- 0
design <- rep(c(1,-1), preps/2) fit <- lmFit(MA, design=design, weights=MA$weights) fit$coeff fit <- eBayes(fit) topTable(fit, adjust="none") fit$df.residual
- Zero weights should be identical to NA's
MA$M[MA$weights==0] <- NA
- Sigma component - sd(x)
fit$sigma apply(MA$M * designMat, 1, sd, na.rm=TRUE)
- Stderr component - se(x_bar)
drop(fit$stdev.unscaled) * fit$sigma apply(MA$M * designMat, 1, function(x,...){sd(x,...)/sqrt(length(x[!is.na(x)]))}, na.rm=TRUE)
