@@ -1,6 +1,6 @@

 Package: limma

-Version: 3.25.6

-Date: 2015/06/03

+Version: 3.25.7

+Date: 2015-06-05

 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]

@@ -13,6 +13,7 @@ S3method("[",EList)

 S3method("[",EListRaw)

 S3method(anova,MAList)

 S3method(as.data.frame,EList)

+S3method(as.data.frame,EListRaw)

 S3method(as.data.frame,MAList)

 S3method(as.data.frame,MArrayLM)

 S3method(as.matrix,MAList)

@@ -142,7 +142,7 @@ dimnames.MArrayLM <- function(x) dimnames(x$coefficients)

 #  Gordon Smyth

 #  17 Dec 2005. Last modified 23 March 2009.

 {

-	exprmatrices <- c("R","G","Rb","Gb","M","A","E","weights")

+	exprmatrices <- c("R","G","Rb","Gb","M","A","E","Eb","weights")

 	for (a in exprmatrices) if(!is.null(x[[a]])) dimnames(x[[a]]) <- value

 	for(a in names(x$other)) dimnames(x$other[[a]]) <- value

 	if(!is.null(x$targets)) row.names(x$targets) <- value[[2]]

@@ -1,3 +1,8 @@

+ 5 June 2015: limma 3.25.7

+

+- The dimnames<- method for EListRaw objects now sets rownames for

+  the background matrix Eb as well as for the foreground matrix E.

+

  3 June 2015: limma 3.25.6

 - new function kegga() to conduct KEGG pathway over-representation

@@ -33,8 +33,12 @@ This page gives an overview of the LIMMA functions for gene set testing and path

 \item{ \code{\link{barcodeplot}} }{

 	Enrichment plot of a gene set.}

-\item{ \code{\link{goana}} and  \code{\link{topGO}}}{

-	Gene ontology analysis of gene lists using Entrez Gene IDs.

+\item{ \code{\link{goana}} and \code{\link{topGO}}}{

+	Gene ontology 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.}

+

+\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.}

 }

 }

@@ -26,6 +26,7 @@ These classes contains no slots (other than \code{.Data}), but objects should co

 \section{Optional Components}{

 Optional components include:

 \describe{

+  \item{\code{Eb}}{numeric matrix containing unlogged background expression values, of same dimensions as \code{E}. For an \code{EListRaw} object only.}

   \item{\code{weights}}{numeric matrix of same dimensions as \code{E} containing relative spot quality weights.  Elements should be non-negative.}

   \item{\code{other}}{list containing other matrices, all of the same dimensions as \code{E}.}

   \item{\code{genes}}{data.frame containing probe information. Should have one row for each probe. May have any number of columns.}

@@ -12,14 +12,12 @@ Test for over-representation of gene ontology (GO) terms or KEGG pathways in one

 }

 \usage{

-\method{goana}{MArrayLM}(de, coef = ncol(de), geneid = rownames(de), FDR = 0.05, 

-      trend = FALSE, \dots)

-\method{goana}{default}(de, universe = NULL, species = "Hs", prior.prob = NULL,

-      covariate=NULL, plot=FALSE, \dots)

-\method{kegga}{MArrayLM}(de, coef = ncol(de), geneid = rownames(de), FDR = 0.05, 

-      trend = FALSE, \dots)

-\method{kegga}{default}(de, universe = NULL, species = "Hs", prior.prob = NULL,

-      covariate=NULL, plot=FALSE, \dots)

+\method{goana}{MArrayLM}(de, coef = ncol(de), geneid = rownames(de), FDR = 0.05, trend = FALSE, \dots)

+\method{goana}{default}(de, universe = NULL, species = "Hs", prior.prob = NULL, covariate=NULL,

+      plot=FALSE, \dots)

+\method{kegga}{MArrayLM}(de, coef = ncol(de), geneid = rownames(de), FDR = 0.05, trend = FALSE, \dots)

+\method{kegga}{default}(de, universe = NULL, species = "Hs", prior.prob = NULL, covariate=NULL,

+      plot=FALSE, \dots)

 }

 \arguments{

@@ -68,7 +66,7 @@ While \code{tricubeMovingAverage} does not enforce monotonicity, it has the adva

 }

 \value{

-The default method produces a data frame with a row for each GO term and the following columns:

+The \code{goana} default method produces a data frame with a row for each GO term and the following columns:

   \item{Term}{GO term.}

   \item{Ont}{ontology that the GO term belongs to.  Possible values are \code{"BP"}, \code{"CC"} and \code{"MF"}.}

   \item{N}{number of genes in the GO term.}

@@ -77,7 +75,7 @@ The default method produces a data frame with a row for each GO term and the fol

 The last two column names above assume one gene set with the name \code{DE}.

 In general, there will be a pair of such columns for each gene set and the name of the set will appear in place of \code{"DE"}.

-The \code{MArrayLM} method produces a data frame with a row for each GO term and the following columns:

+The \code{goana} method for \code{MArrayLM} objects produces a data frame with a row for each GO term and the following columns:

   \item{Term}{GO term.}

   \item{Ont}{ontology that the GO term belongs to.  Possible values are \code{"BP"}, \code{"CC"} and \code{"MF"}.}

   \item{N}{number of genes in the GO term.}

@@ -87,6 +85,8 @@ The \code{MArrayLM} method produces a data frame with a row for each GO term and

   \item{P.Down}{p-value for over-representation of GO term in down-regulated genes.}

 The row names of the data frame give the GO term IDs.

+

+The output from \code{kegga} is the same except that row names become KEGG pathway IDs, \code{Term} becomes \code{Path} and there is no \code{Ont} column.

 }

 \references{

@@ -97,12 +97,12 @@ The row names of the data frame give the GO term IDs.

 }

 \seealso{

-\code{\link{topGO}}

+\code{\link{topGO}}, \code{\link{topKEGG}}

 The goseq package provides an alternative implementation of methods from Young et al (2010).

-The goseq version will work with a variety of gene identifiers, not only Entrez Gene as here, and includes a database of gene length information for various species.

+Unlike the limma functions documented here, goseq will work with a variety of gene identifiers and includes a database of gene length information for various species.

-The gostats package also does GO analyses with some different options.

+The gostats package also does GO analyses without adjustment for bias but with some other options.

 See \link{10.GeneSetTests} for a description of other functions used for gene set testing.

 }