Difference between revisions of "LmFit-Weights.R"

From Organic Design wiki
(Mods to first 4 examples)
(Removing more complex Reference/Affy design)
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drop(fit$stdev.unscaled) * fit$sigma
 
drop(fit$stdev.unscaled) * fit$sigma
 
apply(MA$M  * designMat, 1, function(x,...){sd(x,...)/sqrt(length(x[!is.na(x)]))}, na.rm=TRUE)
 
apply(MA$M  * designMat, 1, function(x,...){sd(x,...)/sqrt(length(x[!is.na(x)]))}, na.rm=TRUE)
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# ========================================================================
 
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# Two groups common reference style analysis
 
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# ========================================================================
 
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RG <- new("RGList")
 
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RG$R <- matrix(rnorm(n*preps,8,2), nc=preps)
 
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RG$G <- matrix(rnorm(n*preps,8,2), nc=preps)
 
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RG$printer <-  structure(list(ngrid.c=1, ngrid.r=1, nspot.c=2, nspot.r=2),
 
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                        class="printlayout")
 
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# Ordering genes same ass topTable for convenience
 
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#RG <- RG[c(4,2,1,3),]
 
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MA <- MA.RG(RG)
 
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colnames(MA$M) <- paste("array", 1:preps, sep="")
 
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rownames(MA$M) <- paste("gene", 1:n, sep="")
 
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design <- cbind(1, rep(c(0,1), each=5))
 
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colnames(design) <- c("A","B-A")
 
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fit <- lmFit(MA, design=design, weights=NULL)
 
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fit <- eBayes(fit)
 
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topTable(fit, coef="B-A", adjust="none")
 
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# Sigma component - sd(x). The design matrix parametrisation is fitting a more complex
 
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# model, decreasing the rank by 1 which gives slightly modified estimates of sigma
 
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# The code in lm.series which calculates fit$sigma is;
 
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# fit$sigma <- sqrt(colMeans(fit$effects[(fit$rank + 1):narrays, , drop = FALSE]^2))
 
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fit$sigma
 
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apply(MA$M , 1, sd, na.rm=TRUE) # Not quite the same
 
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plot(fit$sigma, apply(MA$M , 1, sd, na.rm=TRUE))
 
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# Stderr component - se(x_bar)
 
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drop(fit$stdev.unscaled[,2]) * fit$sigma # Check differences here!
 
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apply(MA$M, 1, function(x,...){sd(x,...)/sqrt(length(x[!is.na(x)]))}, na.rm=TRUE)
 
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a <- drop(fit$stdev.unscaled[,2]) * fit$sigma # Check differences here!
 
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b <- apply(MA$M, 1, function(x,...){sd(x,...)/sqrt(length(x[!is.na(x)]))}, na.rm=TRUE)
 
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plot(a, b*2)
 
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abline(0,1)
 
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# ========================================================================
 
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# Two groups common reference style analysis (alternate parametrisation)
 
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# ========================================================================
 
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design <- cbind(rep(c(1,0), each=5), rep(c(0,1), each=5))
 
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colnames(design) <- c("A","B")
 
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cont.matrix <- makeContrasts("B-A" = B-A, levels=design)
 
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fit2 <- lmFit(MA, design=design, weights=NULL)
 
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fit2 <- contrasts.fit(fit2, cont.matrix)
 
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fit2 <- eBayes(fit2)
 
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topTable(fit2, adjust="none")
 
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fit$sigma # identical
 
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fit2$sigma
 
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# ========================================================================
 
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# Affy style analysis (using R, G channels here)
 
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# ========================================================================
 
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library(convert)
 
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RG <- new("RGList")
 
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RG$R <- matrix(rnorm(n*preps,8,2), nc=preps)
 
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RG$G <- matrix(rnorm(n*preps,8,2), nc=preps)
 
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RG$printer <-  structure(list(ngrid.c=1, ngrid.r=1, nspot.c=2, nspot.r=2),
 
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                        class="printlayout")
 
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exprSet <- as(RG, "exprSet")
 
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design <- cbind(rep(1, 20), rep(c(1,0), 10))
 
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colnames(design) <- c("A","B-A")
 
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fit <- lmFit(exprSet, design=design)
 
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fit <- eBayes(fit)
 
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topTable(fit, adjust="none", coef="B-A")
 
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fit$df.residual
 
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fit$sigma
 
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design <- cbind(rep(c(0,1), 10), rep(c(1,0), 10))
 
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colnames(design) <- c("A","B")
 
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cont.matrix <- makeContrasts("B-A" = B-A, levels=design)
 
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fit <- lmFit(exprSet, design=design)
 
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fit <- contrasts.fit(fit, cont.matrix)
 
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fit <- eBayes(fit)
 
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topTable(fit, adjust="none")
 
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fit$df.residual
 
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fit$sigma
 
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# Sigma component - sd(x)
 
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fit$sigma
 
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apply(exprs(exprSet), 1, sd)
 
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plot(fit$sigma, apply(exprs(exprSet), 1, sd))
 
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abline(0,1)
 
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#apply(exprs(exprSet) * outer(rep(1, n), (design[,2]*2)-1), 1, sd)
 
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#apply(exprs(exprSet) * outer(rep(1, n), design), 1, sd)
 
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# Stderr component - se(x_bar)
 
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drop(fit$stdev.unscaled) * fit$sigma
 
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apply(MA$M * outer(rep(1, n), design), 1, function(x){sd(x)/sqrt(length(x))})
 

Revision as of 23:45, 2 August 2006

  1. ========================================================================
  2. This script examines the effect of weights and NA's on sigma estimates
  3. ========================================================================

library(limma) options(digits=2) packageDescription("limma", field="Version")

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) colnames(MA$M) <- paste("array", 1:preps, sep="") rownames(MA$M) <- paste("gene", 1:n, sep="")

  1. ========================================================================
  2. 1) Analysis, no weights, no NA's
  3. ========================================================================

design <- rep(c(1,-1), preps/2) fit <- lmFit(MA, design=design, weights=NULL) fit <- eBayes(fit) topTable(fit, adjust="none")

  1. Sigma component - sd(x)

fit$sigma apply(MA$M * outer(rep(1, n), design), 1, sd)

  1. 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))})


  1. ========================================================================
  2. 2) Analysis, adding some weights to MAList
  3. ========================================================================

MA$weights <- matrix(1, n,preps) MA$weights[row(MA$weights)<col(MA$weights)] <- 0 MA$weights

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

  1. Zero weights should be identical to NA's

MA$M[MA$weights==0] <- NA designMat <- outer(rep(1, n), design)

  1. Sigma component - sd(x)

fit$sigma apply(MA$M * designMat, 1, sd, na.rm=TRUE)

  1. 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)


  1. ========================================================================
  2. 3) Analysis, adding NA's only to MAList
  3. ========================================================================

MA <- MA.RG(RG) MA$M[row(MA$M)<col(MA$M)] <- NA MA$M

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

  1. Sigma component - sd(x)

fit$sigma apply(MA$M * designMat, 1, sd, na.rm=TRUE)

  1. 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)

  1. ========================================================================
  2. 4) Added weights and NA's simultaneously at random for MAList
  3. ========================================================================

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

  1. Zero weights should be identical to NA's

MA$M[MA$weights==0] <- NA

  1. Sigma component - sd(x)

fit$sigma apply(MA$M * designMat, 1, sd, na.rm=TRUE)

  1. 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)

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