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tille pushed a commit to branch master
in repository r-bioc-limma.
commit 3fc04a1ff915b4601de908ebfa54fd2c5e4eca74
Author: Andreas Tille <ti...@debian.org>
Date: Fri Jul 31 18:55:08 2015 +0200
Imported Upstream version 3.24.14+dfsg
---
DESCRIPTION | 6 +-
R/geneset-camera.R | 43 ++++++++---
R/geneset-roast.R | 4 +-
R/goana.R | 2 +-
R/lmfit.R | 4 +-
R/subsetting.R | 16 +++-
inst/doc/changelog.txt | 18 +++++
inst/doc/intro.pdf | Bin 46191 -> 46191 bytes
man/10GeneSetTests.Rd | 2 +-
man/TestResults.Rd | 5 +-
man/camera.Rd | 8 +-
man/roast.Rd | 2 +-
man/summary.Rd | 2 +-
tests/limma-Tests.Rout.save | 184 +++++++++++++-------------------------------
vignettes/intro.Rnw | 49 ------------
15 files changed, 136 insertions(+), 209 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index b7fe377..17e28c2 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: limma
-Version: 3.24.13
-Date: 2015-07-07
+Version: 3.24.14
+Date: 2015-07-20
Title: Linear Models for Microarray Data
Description: Data analysis, linear models and differential expression for
microarray data.
Author: Gordon Smyth [cre,aut], Matthew Ritchie [ctb], Jeremy Silver [ctb],
James Wettenhall [ctb], Natalie Thorne [ctb], Davis McCarthy [ctb], Di Wu
[ctb], Yifang Hu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Alicia Oshlack
[ctb], Carolyn de Graaf [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb],
Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb], Aaron Lun [ctb]
@@ -20,4 +20,4 @@ biocViews: ExonArray, GeneExpression, Transcription,
MultipleComparison, Normalization, Preprocessing,
QualityControl
NeedsCompilation: yes
-Packaged: 2015-07-09 01:26:56 UTC; biocbuild
+Packaged: 2015-07-21 01:24:42 UTC; biocbuild
diff --git a/R/geneset-camera.R b/R/geneset-camera.R
index 7cd0e7d..248d513 100644
--- a/R/geneset-camera.R
+++ b/R/geneset-camera.R
@@ -21,10 +21,10 @@ interGeneCorrelation <- function(y, design)
camera <- function(y,...) UseMethod("camera")
-camera.default <-
function(y,index,design=NULL,contrast=ncol(design),weights=NULL,use.ranks=FALSE,allow.neg.cor=TRUE,trend.var=FALSE,sort=TRUE,...)
+camera.default <-
function(y,index,design=NULL,contrast=ncol(design),weights=NULL,use.ranks=FALSE,allow.neg.cor=TRUE,inter.gene.cor=NULL,trend.var=FALSE,sort=TRUE,...)
# Competitive gene set test allowing for correlation between genes
# Gordon Smyth and Di Wu
-# Created 2007. Last modified 23 June 2015
+# Created 2007. Last modified 20 July 2015
{
# Issue warning if extra arguments found
dots <- names(list(...))
@@ -51,14 +51,24 @@ camera.default <-
function(y,index,design=NULL,contrast=ncol(design),weights=NUL
if(nrow(design) != n) stop("row dimension of design matrix must match
column dimension of data")
ne <- nonEstimable(design)
if(!is.null(ne)) cat("Coefficients not estimable:",paste(ne,collapse="
"),"\n")
-
p <- ncol(design)
df.residual <- n-p
- df.camera <- min(df.residual,G-2)
# Check weights
if(is.null(weights)) weights <- y$weights
+# Check inter.gene.cor
+ fixed.cor <- !is.null(inter.gene.cor)
+
+# Set for camera tests
+ if(fixed.cor)
+ if(use.ranks)
+ df.camera <- Inf
+ else
+ df.camera <- G-2
+ else
+ df.camera <- min(df.residual,G-2)
+
# Reduce to numeric expression matrix
y <- y$exprs
@@ -124,8 +134,12 @@ camera.default <-
function(y,index,design=NULL,contrast=ncol(design),weights=NUL
if(trend.var) A <- rowMeans(y) else A <- NULL
sv <- squeezeVar(sigma2,df=df.residual,covariate=A)
modt <- unscaledt / sqrt(sv$var.post)
- df.total <- min(df.residual+sv$df.prior, G*df.residual)
- Stat <- zscoreT(modt, df=df.total)
+ if(use.ranks)
+ Stat <- modt
+ else {
+ df.total <- min(df.residual+sv$df.prior, G*df.residual)
+ Stat <- zscoreT(modt, df=df.total, approx=TRUE)
+ }
# Global statistics
meanStat <- mean(Stat)
@@ -139,13 +153,18 @@ camera.default <-
function(y,index,design=NULL,contrast=ncol(design),weights=NUL
StatInSet <- Stat[iset]
m <- length(StatInSet)
m2 <- G-m
- if(m>1) {
- Uset <- U[iset,,drop=FALSE]
- vif <- m * mean(colMeans(Uset)^2)
- correlation <- (vif-1)/(m-1)
+ if(fixed.cor) {
+ correlation <- inter.gene.cor
+ vif <- 1+(m-1)*correlation
} else {
- vif <- 1
- correlation <- NA
+ if(m>1) {
+ Uset <- U[iset,,drop=FALSE]
+ vif <- m * mean(colMeans(Uset)^2)
+ correlation <- (vif-1)/(m-1)
+ } else {
+ vif <- 1
+ correlation <- NA
+ }
}
tab[i,1] <- m
diff --git a/R/geneset-roast.R b/R/geneset-roast.R
index 5918a13..c56f8b0 100644
--- a/R/geneset-roast.R
+++ b/R/geneset-roast.R
@@ -16,7 +16,7 @@ roast <- function(y,...) UseMethod("roast")
roast.default <-
function(y,index=NULL,design=NULL,contrast=ncol(design),set.statistic="mean",gene.weights=NULL,array.weights=NULL,weights=NULL,block=NULL,correlation,var.prior=NULL,df.prior=NULL,trend.var=FALSE,nrot=999,approx.zscore=TRUE,...)
# Rotation gene set testing for linear models
# Gordon Smyth and Di Wu
-# Created 24 Apr 2008. Last modified 8 April 2015.
+# Created 24 Apr 2008. Last modified 16 July 2015.
{
# Issue warning if extra arguments found
dots <- names(list(...))
@@ -97,7 +97,9 @@ roast.default <-
function(y,index=NULL,design=NULL,contrast=ncol(design),set.sta
diag(cormatrix) <- 1
R <- chol(cormatrix)
y <- t(backsolve(R, t(y), transpose = TRUE))
+ dn <- dimnames(design)
design <- backsolve(R, design, transpose = TRUE)
+ dimnames(design) <- dn
}
# Check contrast
diff --git a/R/goana.R b/R/goana.R
index 9b935ac..3860dd0 100644
--- a/R/goana.R
+++ b/R/goana.R
@@ -200,7 +200,7 @@ goana.default <- function(de, universe = NULL, species =
"Hs", prior.prob = NULL
# Assemble output
g <- strsplit2(rownames(S),split="\\.")
- TERM <- AnnotationDbi::select(GO.db::GO.db,keys=g[,1],columns="TERM")
+ TERM <-
suppressMessages(AnnotationDbi::select(GO.db::GO.db,keys=g[,1],columns="TERM"))
Results <- data.frame(Term = TERM[[2]], Ont = g[,2], S, P,
stringsAsFactors=FALSE)
rownames(Results) <- g[,1]
diff --git a/R/lmfit.R b/R/lmfit.R
index 03a712e..82904f7 100644
--- a/R/lmfit.R
+++ b/R/lmfit.R
@@ -103,7 +103,7 @@ lm.series <-
function(M,design=NULL,ndups=1,spacing=1,weights=NULL)
# Check whether QR-decomposition is constant for all genes
# If so, fit all genes in one sweep
- NoProbeWts <- !any(is.na(M)) && (is.null(weights) ||
!is.null(attr(weights,"arrayweights")))
+ NoProbeWts <- all(is.finite(M)) && (is.null(weights) ||
!is.null(attr(weights,"arrayweights")))
if(NoProbeWts) {
if(is.null(weights))
fit <- lm.fit(design, t(M))
@@ -263,7 +263,7 @@ gls.series <-
function(M,design=NULL,ndups=2,spacing=1,block=NULL,correlation=NU
ngenes <- nrow(M)
stdev.unscaled <-
matrix(NA,ngenes,nbeta,dimnames=list(rownames(M),coef.names))
- NoProbeWts <- !any(is.na(M)) && (is.null(weights) ||
!is.null(attr(weights,"arrayweights")))
+ NoProbeWts <- all(is.finite(M)) && (is.null(weights) ||
!is.null(attr(weights,"arrayweights")))
if(NoProbeWts) {
V <- cormatrix
if(!is.null(weights)) {
diff --git a/R/subsetting.R b/R/subsetting.R
index 8495612..362ead0 100755
--- a/R/subsetting.R
+++ b/R/subsetting.R
@@ -41,8 +41,10 @@ assign("[.RGList",
function(object, i, j)
# Subsetting for RGList objects
# Gordon Smyth
-# 29 June 2003. Last modified 11 Dec 2013.
+# 29 June 2003. Last modified 20 July 2015.
{
+ if(nargs() != 3) stop("Two subscripts required",call.=FALSE)
+
# Recognized components
IJ <- c("R","G","Rb","Gb","weights")
IX <- c("genes")
@@ -60,8 +62,10 @@ assign("[.MAList",
function(object, i, j)
# Subsetting for MAList objects
# Gordon Smyth
-# 29 June 2003. Last modified 22 Dec 2005.
+# 29 June 2003. Last modified 20 July 2015.
{
+ if(nargs() != 3) stop("Two subscripts required",call.=FALSE)
+
# Recognized components
IJ <- c("M","A","weights")
IX <- c("genes")
@@ -81,8 +85,10 @@ assign("[.EList",
function(object, i, j)
# Subsetting for EList objects
# Gordon Smyth
-# 23 February 2009. Last modified 11 Dec 2013.
+# 23 February 2009. Last modified 20 July 2015.
{
+ if(nargs() != 3) stop("Two subscripts required",call.=FALSE)
+
# Recognized components
IJ <- c("E","Eb","weights")
IX <- c("genes")
@@ -102,8 +108,10 @@ assign("[.MArrayLM",
function(object, i, j)
# Subsetting for MArrayLM objects
# Gordon Smyth
-# 26 April 2005. Last modified 14 May 2014.
+# 26 April 2005. Last modified 20 July 2015.
{
+ if(nargs() != 3) stop("Two subscripts required",call.=FALSE)
+
# Recognized components
IJ <- c("coefficients","stdev.unscaled","t","p.value","lods","weights")
IX <- "genes"
diff --git a/inst/doc/changelog.txt b/inst/doc/changelog.txt
index 9ff73af..8dcb976 100755
--- a/inst/doc/changelog.txt
+++ b/inst/doc/changelog.txt
@@ -1,3 +1,21 @@
+20 July 2015: limma 3.24.14
+
+- camera() has a new argument inter.gene.cor. This allows a preset
+ inter gene correlation to be set for each test test, resulting in
+ potentially less conservative tests.
+
+- All data classes (EListRaw, EList, RGList, MAList, MArrayLM) now
+ require two arguments for subsetting. This restores the behavior
+ in limma version 3.19.10 and earlier. In limma 3.19.11 through
+ 3.25.13, subsetting by one argument y[i] was equivalent to y[i,].
+ This will now give an error message.
+
+- The contrast argument of roast() can now optionally be a character
+ string giving the name of a column of the design matrix.
+
+- lmFit() now treats infinite expression values as NA. In the past,
+ this was done somewhat inconsistently.
+
7 July 2015: limma 3.24.13
- New 'tolerance' argument for read.idat() to allow manifest and idat
diff --git a/inst/doc/intro.pdf b/inst/doc/intro.pdf
index 4bd9f8e..b94678c 100644
Binary files a/inst/doc/intro.pdf and b/inst/doc/intro.pdf differ
diff --git a/man/10GeneSetTests.Rd b/man/10GeneSetTests.Rd
index 33e793f..6670298 100644
--- a/man/10GeneSetTests.Rd
+++ b/man/10GeneSetTests.Rd
@@ -39,7 +39,7 @@ This page gives an overview of the LIMMA functions for gene
set testing and path
\item{ \code{\link{kegga}} and \code{\link{topKEGG}}}{
KEGG pathway over-representation analysis of gene lists using Entrez
Gene IDs.
- \code{goana} can work directly on a fitted model object or on one or
more lists of genes.}
+ \code{kegga} can work directly on a fitted model object or on one or
more lists of genes.}
}
}
diff --git a/man/TestResults.Rd b/man/TestResults.Rd
index c1b385c..93c4755 100755
--- a/man/TestResults.Rd
+++ b/man/TestResults.Rd
@@ -7,7 +7,7 @@
\description{
A matrix-based class for storing the results of simultanous tests.
-\code{TestResults} objects are normally created by
\code{\link{classifyTestsF}}, \code{\link{classifyTestsT}} or
\code{\link{classifyTestsP}}.
+\code{TestResults} objects are normally created by \code{\link{decideTests}},
\code{\link{classifyTestsF}}, \code{\link{classifyTestsT}} or
\code{\link{classifyTestsP}}.
}
\usage{
@@ -20,6 +20,9 @@ A matrix-based class for storing the results of simultanous
tests.
}
\section{Slots/List Components}{
+A \code{TestResults} object is essentially a numeric matrix with elements
equal to \code{0}, \code{1} or \code{-1}.
+Zero represents acceptance of the null hypothesis, \code{1} indicates
rejection in favor of the right tail alternative and \code{-1} indicates
rejection in favor of the left tail alternative.
+
\code{TestResults} objects can be created by \code{new("TestResults",results)}
where \code{results} is a matrix.
Objects of this class contain no slots (other than \code{.Data}), although the
attributes \code{dim} and \code{dimnames} may be treated as slots.
}
diff --git a/man/camera.Rd b/man/camera.Rd
index 5a41a08..f60e23d 100644
--- a/man/camera.Rd
+++ b/man/camera.Rd
@@ -8,7 +8,8 @@ Test whether a set of genes is highly ranked relative to other
genes in terms of
}
\usage{
\S3method{camera}{default}(y, index, design, contrast=ncol(design),
weights=NULL,
- use.ranks=FALSE, allow.neg.cor=TRUE, trend.var=FALSE, sort=TRUE, \dots)
+ use.ranks=FALSE, allow.neg.cor=TRUE, inter.gene.cor=NULL,
trend.var=FALSE,
+ sort=TRUE, \dots)
interGeneCorrelation(y, design)
}
\arguments{
@@ -22,6 +23,7 @@ interGeneCorrelation(y, design)
\item{weights}{can be a numeric matrix of individual weights, of same size
as \code{y}, or a numeric vector of array weights with length equal to
\code{ncol(y)}, or a numeric vector of gene weights with length equal to
\code{nrow(y)}.}
\item{use.ranks}{do a rank-based test (\code{TRUE}) or a parametric test
(\code{FALSE})?}
\item{allow.neg.cor}{should reduced variance inflation factors be allowed
for negative correlations?}
+ \item{inter.gene.cor}{numeric, optional preset value for the inter gene
correlation within tested sets. If not \code{NULL}, then this value will
over-ride estimation of the inter gene correlations.}
\item{trend.var}{logical, should an empirical Bayes trend be estimated? See
\code{\link{eBayes}} for details.}
\item{sort}{logical, should the results be sorted by p-value?}
\item{\dots}{other arguments are not currently used}
@@ -41,7 +43,9 @@ This allows users to focus on differential expression for any
coefficient or con
\code{camera} estimates p-values after adjusting the variance of test
statistics by an estimated variance inflation factor.
The inflation factor depends on estimated genewise correlation and the number
of genes in the gene set.
-\code{interGeneCorrelation} estimates the mean pair-wise correlation between a
set of genes.
+By default, \code{camera} uses \code{interGeneCorrelation} to estimate the
mean pair-wise correlation within each set of genes.
+\code{camera} can be used with a small preset correlation value, say
\code{inter.gene.cor=0.05}.
+This produces a less conservative test.
}
\value{
diff --git a/man/roast.Rd b/man/roast.Rd
index c4c64d3..6fbb90d 100644
--- a/man/roast.Rd
+++ b/man/roast.Rd
@@ -33,7 +33,7 @@ Rotation gene set testing for linear models.
\item{index}{index vector specifying which rows (probes) of \code{y} are in
the test set. This can be a vector of indices, or a logical vector of the same
length as \code{statistics}, or any vector such as \code{y[index,]} contains
the values for the gene set to be tested.
For \code{mroast}, \code{index} is a list of index vectors. The list can be
made using \link{ids2indices}.}
\item{design}{design matrix}
- \item{contrast}{contrast for which the test is required. Can be an integer
specifying a column of \code{design}, or else a contrast vector of length equal
to the number of columns of \code{design}.}
+ \item{contrast}{contrast for which the test is required. Can be an integer
specifying a column of \code{design}, or the name of a column of \code{design},
or a numeric contrast vector of length equal to the number of columns of
\code{design}.}
\item{set.statistic}{summary set statistic. Possibilities are
\code{"mean"},\code{"floormean"},\code{"mean50"} or \code{"msq"}.}
\item{gene.weights}{optional numeric vector of weights for genes in the set.
Can be positive or negative. For \code{mroast} this vector must have length
equal to \code{nrow(y)}. For \code{roast}, can be of length \code{nrow(y)} or
of length equal to the number of genes in the test set.}
\item{array.weights}{optional numeric vector of array weights.}
diff --git a/man/summary.Rd b/man/summary.Rd
index 52b5477..4e43515 100755
--- a/man/summary.Rd
+++ b/man/summary.Rd
@@ -12,7 +12,7 @@ Briefly summarize microarray data objects.
\method{summary}{RGList}(object, \dots)
}
\arguments{
- \item{object}{an object of class \code{RGList}, \code{MAList} or
\code{MArrayLM}}
+ \item{object}{an object of class \code{RGList}, \code{MAList},
\code{EListRaw}, \code{EList} or \code{MArrayLM}}
\item{\dots}{other arguments are not used}
}
\details{
diff --git a/tests/limma-Tests.Rout.save b/tests/limma-Tests.Rout.save
index bbc91d6..f1b47e5 100755
--- a/tests/limma-Tests.Rout.save
+++ b/tests/limma-Tests.Rout.save
@@ -1,5 +1,5 @@
-R version 3.2.0 (2015-04-16) -- "Full of Ingredients"
+R version 3.2.1 (2015-06-18) -- "World-Famous Astronaut"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
@@ -1122,135 +1122,7 @@ attr(,"df1")
attr(,"df2")
[1] Inf
> classifyTestsT(tstat)
-TestResults matrix
- [,1] [,2] [,3]
-[1,] 0 1 0
-[2,] 0 0 0
-[3,] 0 0 0
-[4,] 0 0 0
-> classifyTestsP(tstat)
-TestResults matrix
- [,1] [,2] [,3]
-[1,] 0 1 0
-[2,] 0 1 0
-[3,] 0 0 0
-[4,] 0 0 0
->
-> ### avereps
->
-> x <- matrix(rnorm(8*3),8,3)
-> colnames(x) <- c("S1","S2","S3")
-> rownames(x) <- c("b","a","a","c","c","b","b","b")
-> avereps(x)
- S1 S2 S3
-b -0.2353018 0.5220094 0.2302895
-a -0.4347701 0.6453498 -0.6758914
-c 0.3482980 -0.4820695 -0.3841313
->
-> ### roast
->
-> y <- matrix(rnorm(100*4),100,4)
-> sigma <- sqrt(2/rchisq(100,df=7))
-> y <- y*sigma
-> design <- cbind(Intercept=1,Group=c(0,0,1,1))
-> iset1 <- 1:5
-> y[iset1,3:4] <- y[iset1,3:4]+3
-> iset2 <- 6:10
-> roast(y=y,iset1,design,contrast=2)
- Active.Prop P.Value
-Down 0 0.996498249
-Up 1 0.004002001
-UpOrDown 1 0.008000000
-Mixed 1 0.008000000
-> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
- Active.Prop P.Value
-Down 0 0.99899950
-Up 1 0.00150075
-UpOrDown 1 0.00300000
-Mixed 1 0.00300000
-> w <- matrix(runif(100*4),100,4)
-> roast(y=y,iset1,design,contrast=2,weights=w)
- Active.Prop P.Value
-Down 0 0.9994997
-Up 1 0.0010005
-UpOrDown 1 0.0020000
-Mixed 1 0.0020000
->
mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
- NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
-set1 5 0 1 Up 0.008 0.015 0.008 0.015
-set2 5 0 0 Up 0.959 0.959 0.687 0.687
->
mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
- NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
-set1 5 0 1 Up 0.004 0.007 0.004 0.007
-set2 5 0 0 Up 0.679 0.679 0.658 0.658
-> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
- NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
-set1 5 0.0 1 Up 0.003 0.005 0.003 0.005
-set2 5 0.2 0 Down 0.950 0.950 0.250 0.250
->
mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
- NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
-set1 5 0 1 Up 0.001 0.001 0.001 0.001
-set2 5 0 0 Down 0.791 0.791 0.146 0.146
->
-> ### camera
->
-> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
- NGenes Correlation Direction PValue
-set1 5 -0.2481655 Up 0.001050253
-> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
- NGenes Correlation Direction PValue FDR
-set1 5 -0.2481655 Up 0.0009047749 0.00180955
-set2 5 0.1719094 Down 0.9068364381 0.90683644
->
-> ### with EList arg
->
-> y <- new("EList",list(E=y))
-> roast(y=y,iset1,design,contrast=2)
- Active.Prop P.Value
-Down 0 0.997498749
-Up 1 0.003001501
-UpOrDown 1 0.006000000
-Mixed 1 0.006000000
-> camera(y=y,iset1,design,contrast=2)
- NGenes Correlation Direction PValue
-set1 5 -0.2481655 Up 0.0009047749
->
-> ### eBayes with trend
->
-> fit <- lmFit(y,design)
-> fit <- eBayes(fit,trend=TRUE)
-> topTable(fit,coef=2)
- logFC AveExpr t P.Value adj.P.Val B
-3 3.488703 1.03931081 4.860410 0.0002436118 0.01647958 0.6722078
-2 3.729512 1.73488969 4.700998 0.0003295917 0.01647958 0.3777787
-4 2.696676 1.74060725 3.280613 0.0053915597 0.17971866 -2.3313104
-1 2.391846 1.72305203 3.009776 0.0092611288 0.23152822 -2.8478458
-5 2.387967 1.63066783 2.786529 0.0144249169 0.26573834 -3.2671364
-33 -1.492317 -0.07525287 -2.735781 0.0159443006 0.26573834 -3.3613142
-80 -1.839760 -0.32802306 -2.594532 0.0210374835 0.30053548 -3.6207072
-95 -1.907074 1.26297763 -2.462009 0.0272186263 0.33449167 -3.8598265
-39 1.366141 -0.27360750 2.409767 0.0301042507 0.33449167 -3.9527943
-70 -1.789476 0.21771869 -2.184062 0.0462410739 0.46241074 -4.3445901
-> fit$df.prior
-[1] 12.17481
-> fit$s2.prior
- [1] 0.7108745 0.7186517 0.3976222 0.7224388 0.6531157 0.3014062 0.3169880
- [8] 0.3149772 0.3074632 0.2917431 0.3329334 0.3378027 0.2900500 0.3031741
- [15] 0.3221763 0.2981580 0.2897078 0.2925188 0.2924234 0.3042822 0.2923686
- [22] 0.2897022 0.3251669 0.2929813 0.4922090 0.2902725 0.3018205 0.3029119
- [29] 0.3030051 0.3331358 0.3259651 0.2939051 0.3077824 0.3553515 0.3139985
- [36] 0.3181689 0.3197601 0.4687993 0.3316536 0.2897621 0.2910744 0.2907116
- [43] 0.2907966 0.3265722 0.3240487 0.3241126 0.3003970 0.3064187 0.3645035
- [50] 0.2994391 0.3295512 0.2901076 0.2898658 0.3086659 0.2897209 0.2982976
- [57] 0.3043910 0.2900320 0.3006936 0.2935101 0.3646949 0.3596385 0.3064203
- [64] 0.3027439 0.3076483 0.3363356 0.3504336 0.3496698 0.2897618 0.2898810
- [71] 0.3182290 0.3121707 0.2945001 0.2897549 0.3579410 0.3434376 0.3037970
- [78] 0.3201893 0.3048412 0.3394079 0.3516034 0.3034589 0.3120384 0.3007827
- [85] 0.3013925 0.2902524 0.3527793 0.2969359 0.3033756 0.3170187 0.2978833
- [92] 0.2908437 0.3139422 0.3050183 0.4727609 0.2897104 0.2931671 0.2904177
- [99] 0.3231607 0.2941699
-> summary(fit$s2.post)
- Min. 1st Qu. Median Mean 3rd Qu. Max.
+TestRe Median Mean 3rd Qu. Max.
0.2518 0.2746 0.3080 0.3425 0.3583 0.7344
>
> y$E[1,1] <- NA
@@ -1379,4 +1251,54 @@ GO:0070062 0.055161144
>
> proc.time()
user system elapsed
- 3.08 0.17 4.02
+ 2.83 0.18 3.04
+ limma/vignettes/ 0000755 0001263 0001264 00000000000 12553317532 015252
5 ustar 00biocbuild phs_compbio
[...]
+%\VignetteDepends{}
+%\VignetteKeywords{microarray linear model}
+%\VignettePackage{limma}
+\documentclass[12pt]{article}
+
+\textwidth=6.2in
+\textheight=8.5in
+\oddsidemargin=0.2in
+\evensidemargin=0.2in
+\headheight=0in
+\headsep=0in
+
+\begin{document}
+\title{Limma Package Introduction}
+\author{Gordon Smyth}
+\date{23 October 2004, Revised 21 October 2013}
+\maketitle
+
+Limma is an R package for the analysis of gene expression microarray data,
especially the use of linear models for analysing designed experiments and the
assessment of differential expression.
+Limma provides the ability to analyse comparisons between many RNA targets
simultaneously in arbitrary complicated designed experiments.
+Empirical Bayesian methods are used to provide stable results even when the
number of arrays is small.
+The normalization and data analysis functions are for two-color spotted
microarrays.
+The linear model and differential expression functions apply to all microarray
technologies including Affymetrix and other single-channel oligonucleotide
platforms.
+
+The full Limma User's Guide is available as part of the online documentation.
+To reach the User's Guide you need to install the limma package.
+If you've installed the package and you're using Windows, type
\texttt{library(limma)} at the R prompt then click on ``limma'' from the
drop-down menu called ``Vignettes''.
+If you're not using Windows, you can type
+\begin{Schunk}
+\begin{Sinput}
+> library(limma)
+> limmaUsersGuide()
+\end{Sinput}
+\end{Schunk}
+or alternatively
+\begin{Schunk}
+\begin{Sinput}
+> help.start()
+\end{Sinput}
+\end{Schunk}
+and follow the links to the limma package help.
+
+\end{document}
+
+
+
+
+
+
[...]
\ No newline at end of file
diff --git a/vignettes/intro.Rnw b/vignettes/intro.Rnw
deleted file mode 100644
index 88f952a..0000000
--- a/vignettes/intro.Rnw
+++ /dev/null
@@ -1,49 +0,0 @@
-%\VignetteIndexEntry{Limma One Page Introduction}
-%\VignetteDepends{}
-%\VignetteKeywords{microarray linear model}
-%\VignettePackage{limma}
-\documentclass[12pt]{article}
-
-\textwidth=6.2in
-\textheight=8.5in
-\oddsidemargin=0.2in
-\evensidemargin=0.2in
-\headheight=0in
-\headsep=0in
-
-\begin{document}
-\title{Limma Package Introduction}
-\author{Gordon Smyth}
-\date{23 October 2004, Revised 21 October 2013}
-\maketitle
-
-Limma is an R package for the analysis of gene expression microarray data,
especially the use of linear models for analysing designed experiments and the
assessment of differential expression.
-Limma provides the ability to analyse comparisons between many RNA targets
simultaneously in arbitrary complicated designed experiments.
-Empirical Bayesian methods are used to provide stable results even when the
number of arrays is small.
-The normalization and data analysis functions are for two-color spotted
microarrays.
-The linear model and differential expression functions apply to all microarray
technologies including Affymetrix and other single-channel oligonucleotide
platforms.
-
-The full Limma User's Guide is available as part of the online documentation.
-To reach the User's Guide you need to install the limma package.
-If you've installed the package and you're using Windows, type
\texttt{library(limma)} at the R prompt then click on ``limma'' from the
drop-down menu called ``Vignettes''.
-If you're not using Windows, you can type
-\begin{Schunk}
-\begin{Sinput}
-> library(limma)
-> limmaUsersGuide()
-\end{Sinput}
-\end{Schunk}
-or alternatively
-\begin{Schunk}
-\begin{Sinput}
-> help.start()
-\end{Sinput}
-\end{Schunk}
-and follow the links to the limma package help.
-
-\end{document}
-
-
-
-
-
--
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