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

 Package: limma

-Version: 3.29.7

-Date: 2016-06-12

+Version: 3.29.8

+Date: 2016-06-13

 Title: Linear Models for Microarray Data

 Description: Data analysis, linear models and differential expression for microarray data.

 Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb]

@@ -59,6 +59,8 @@ S3method(cbind,MAList)

 S3method(cbind,RGList)

 S3method(cbind,EList)

 S3method(cbind,EListRaw)

+S3method(detectionPValues,default)

+S3method(detectionPValues,EListRaw)

 S3method(dim,MAList)

 S3method(dim,MArrayLM)

 S3method(dim,RGList)

new file mode 100644

@@ -0,0 +1,41 @@

+detectionPValues <- function(x,...) UseMethod("detectionPValues")

+

+detectionPValues.EListRaw <- function(x,status=NULL,...)

+#	Detection p-values from negative controls for EListRaw

+#	Gordon Smyth

+#	Created 13 June 2016

+{

+	if(is.null(status)) status <- x$genes$Status

+	detectionPValues(x=x$E, status=status, ...)

+}

+

+detectionPValues.default <- function(x, status, negctrl="negative",...)

+#	Detection p-values from negative controls

+#	Gordon Smyth

+#	Created 13 June 2016

+{

+	x <- as.matrix(x)

+

+#	Identify negative control rows

+	if(missing(status) || is.null(status)) stop("need to specify status vector")

+	isneg <- as.integer(status==negctrl)

+	notneg <- 1L-isneg

+	nneg <- sum(isneg)

+

+#	Count the number of negative controls greater than each value

+#	cs1 counts number of negative controls >= the observed value

+#	cs2 counts number of negative controls > the observed value

+#	By taking the average of cs1 and cs2, we count ties as 1/2

+	DetectionPValue <- x

+	cs1 <- cs2 <- rep_len(0,length.out=nrow(x))

+	for (j in 1:ncol(x)) {

+		o1 <- order(x[,j],isneg,decreasing=TRUE)

+		o2 <- order(x[,j],notneg,decreasing=TRUE)

+		cs1[o1] <- cumsum(isneg[o1])

+		cs2[o2] <- cumsum(isneg[o2])

+		DetectionPValue1[,j] <- cs1+cs2

+	}

+

+#	Convert to p-values

+	DetectionPValue/(2*nneg)

+}

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

+13 Jun 2016: limma 3.29.8

+

+- new function detectionPValues() to compute detection p-values from

+  negative control probes.

+

 12 Jun 2016: limma 3.29.7

 - new argument 'verbose' for read.idat().

@@ -26,6 +26,7 @@ There are also a series of utility functions which read the header information f

 and produces an \code{ElistRaw} object.

 \code{\link{read.idat}} reads Illumina files in IDAT format, and produces an \code{EListRaw} object.

+\code{\link{detectionPValues}} can be used to add detection p-values.

 The function \link{as.MAList} can be used to convert a \code{marrayNorm} object to an \code{MAList} object if the data was read and normalized using the marray and marrayNorm packages.

 }

@@ -7,7 +7,7 @@

 Test whether a set of genes is highly ranked relative to other genes in terms of differential expression, accounting for inter-gene correlation.

 }

 \usage{

-\S3method{camera}{default}(y, index, design, contrast = ncol(design), weights = NULL,

+\method{camera}{default}(y, index, design, contrast = ncol(design), weights = NULL,

        use.ranks = FALSE, allow.neg.cor=FALSE, inter.gene.cor=0.01, trend.var = FALSE,

        sort = TRUE, \dots)

 interGeneCorrelation(y, design)

new file mode 100644

@@ -0,0 +1,58 @@

+\name{detectionPValues}

+\alias{detectionPValues.default}

+\alias{detectionPValues.EListRaw}

+\alias{detectionPValues}

+

+\title{Detection P-Values from Negative Controls}

+

+\description{Compute the proportion of negative controls greater than each observed expression value.

+Particularly useful for Illumina BeadChips.}

+

+\usage{

+\method{detectionPValues}{EListRaw}(x, status = NULL, \dots)

+\method{detectionPValues}{default}(x, status, negctrl = "negative", \dots)

+}

+

+\arguments{

+  \item{x}{object of class \code{EListRaw} or a numeric \code{matrix} containing raw intensities for regular and control probes from a series of microarrays.}

+  \item{status}{character vector giving probe types.  Defaults to \code{x$genes$Status} if \code{x} is an \code{EListRaw} object.}

+  \item{negctrl}{character string identifier for negative control probes.}

+  \item{\dots}{other arguments are not currently used.}

+  }

+

+\details{

+The rows of \code{x} for which \code{status == negctrl} are assumed to correspond to negative control probes.

+

+For each column of \code{x}, the detection p-values are defined as \code{(N.eq/2 + N.gt) / N.neg}, where \code{N.gt} is the number of negative controls with expression greater than the observed value, \code{N.eq} is the number of negative controls with expression equal to the observed value, and \code{N.neg} is the total number of negative controls.

+}

+

+\value{numeric matrix of same dimensions as \code{x} containing detection p-values.}

+

+\references{

+Shi, W, de Graaf, C, Kinkel, S, Achtman, A, Baldwin, T, Schofield, L, Scott, H, Hilton, D, Smyth, GK (2010).

+Estimating the proportion of microarray probes expressed in an RNA sample.

+\emph{Nucleic Acids Research} 38, 2168-2176.

+\url{http://nar.oxfordjournals.org/content/38/7/2168}

+}

+

+\author{Gordon Smyth}

+

+\seealso{ 

+  An overview of LIMMA functions to read expression data is given in \link{03.ReadingData}.

+

+  \code{\link{read.idat}} reads Illumina BeadChip expression data from binary IDAT files.

+

+  \code{\link{neqc}} performs normexp background correction and quantile normalization aided by control probes.  

+}

+

+\examples{

+\dontrun{

+# Read Illumina binary IDAT files

+x <- read.idat(idat, bgx)

+x$genes$DectionPValue <- detectionPValues(x)

+y <- neqc(x)

+}

+}

+

+\keyword{background correction}

+\keyword{illumina beadchips}

@@ -8,21 +8,25 @@ propexpr(x, neg.x=NULL, status=x$genes$Status, labels=c("negative","regular"))

 \arguments{

   \item{x}{matrix or similar object containing raw intensities for a set of arrays.}

   \item{neg.x}{matrix or similar object containing raw intensities for negative control probes for the same arrays. If \code{NULL}, then negative controls must be provided in \code{x}.}

-  \item{status}{character vector giving probe types.}

-  \item{labels}{character vector giving probe type identifiers.}

+  \item{status}{character vector specifying control type of each probe. Only used if \code{neg.x} is \code{NULL}.}

+  \item{labels}{character vector giving the \code{status} values for negative control probes and regular (non-control) probes respectively. If of length 1, then all probes other than the negative controls are assumed to be regular. Only used if \code{neg.x} is \code{NULL}.}

   }

+

 \details{

-This function estimates the proportion of expressed in a microarray by utilizing the negative control probes.

+This function estimates the overall proportion of probes on each microarray that are correspond to expressed genes using the method of Shi et al (2010).

+The function is especially useful for Illumina BeadChips arrays, although it can in principle be applied to any platform with good quality negative controls.

+

+The negative controls can be supplied either as rows of \code{x} or as a separate matrix.

+If supplied as rows of \code{x}, then the negative controls are identified by the \code{status} vector.

+\code{x} might also include other types of control probes, but these will be ignored in the calculation.

+

 Illumina BeadChip arrays contain 750~1600 negative control probes.

-The expression profile of these control probes can be saved to a separate file by the Illumina BeadStudio software when using it to output the expression profile for regular probes.

-The control probe profile could be re-generated if it was not generated when the regular probe profile was created by BeadStudio.

-Other microarray platforms can also use this function to estimate the proportion of expressed probes in each array, provided that they have a set of negative control probes.

-

-\code{labels} can include one or two probe type identifiers. 

-Its first element should be the identifier for negative control probes (\code{negative} by default).

-If \code{labels} only contains one identifier, then it will be assumed to contain the identifier for negative control probes.

-By default, \code{regular} is the identifier for regular probes.

+If \code{read.idat} is used to read Illumina expression IDAT files, then the control probes will be populated as rows of the output \code{EListRaw} object, and the vector \code{x$genes$Status} will be set to identify control probes.

+

+Alternatively, expression values can be exported from Illumina's GenomeStudio software as tab-delimited text files.

+In this case, the control probes are usually written to a separate file from the regular probes.

 }

+

 \value{

 Numeric vector giving the proportions of expressed probes in each array.

 }

@@ -42,7 +46,16 @@ Description to the control probes in Illumina BeadChips can be found in \code{\l

 \examples{

 \dontrun{

-x <- read.ilmn(files="sample probe profile.txt",ctrlfiles="control probe profile.txt")

+# Read Illumina binary IDAT files

+x <- read.idat(idat, bgx)

+propexpr(x)

+

+# Read text files exported from GenomeStudio

+x <- read.ilmn(files = "sample probe profile.txt",

+               ctrlfiles = "control probe profile.txt")

 propexpr(x)

 }

 }

+

+\keyword{background correction}

+\keyword{illumina beadchips}

@@ -13,14 +13,14 @@ Rotation gene set testing for linear models.

 }

 \usage{

-\S3method{roast}{default}(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL,

+\method{roast}{default}(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL,

       set.statistic = "mean", gene.weights = NULL, var.prior = NULL, df.prior = NULL,

       nrot = 999, approx.zscore = TRUE, \dots)

-\S3method{mroast}{default}(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL,

+\method{mroast}{default}(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL,

        set.statistic = "mean", gene.weights = NULL, var.prior = NULL, df.prior = NULL,

        nrot = 999, approx.zscore = TRUE, adjust.method = "BH",

        midp = TRUE, sort = "directional", \dots)

-\S3method{fry}{default}(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL,

+\method{fry}{default}(y, index = NULL, design = NULL, contrast = ncol(design), geneid = NULL,

       standardize = "posterior.sd", sort = "directional", \dots)

 }

@@ -7,7 +7,7 @@

 Gene set enrichment analysis for linear models using rotation tests (ROtation testing using MEan Ranks).

 }

 \usage{

-\S3method{romer}{default}(y, index, design = NULL, contrast = ncol(design),

+\method{romer}{default}(y, index, design = NULL, contrast = ncol(design),

       array.weights = NULL, block = NULL, correlation,

       set.statistic = "mean", nrot = 9999, shrink.resid = TRUE, \dots)

 }