Boxplots for cDNA microarray spot statistics

Description

The function boxplot produces boxplots of microarray spot statistics for the classes " , " . We encourage users to use boxplot rather than maBoxplot . The name of the arguments have changed slightly.

Usage

list(list("boxplot"), list("marrayRaw"))(x, xvar="maPrintTip", yvar="maM", ...)
list(list("boxplot"), list("marrayNorm"))(x, xvar="maPrintTip", yvar="maM", ...)

Arguments

Details

If there are more than one array in the batch, the function produces a boxplot for each array in the batch. Such plots are useful when assessing the need for between array normalization, for example, to deal with scale differences among different arrays. Default graphical parameters are chosen for convenience using the function maDefaultPar (e.g. color palette, axis labels, plot title) but the user has the option to overwrite these parameters at any point.

Seealso

maBoxplot , maDefaultPar .

Author

Jean Yang and Sandrine Dudoit

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# To see the demo type demo(marrayPlots)

# Examples use swirl dataset, for description type ? swirl
data(swirl)

# Boxplots of pre-normalization log-ratios M for each of the 16
# print-tip-groups for the Swirl 93 array.
# - Default arguments
boxplot(swirl[,3])

# All spots
boxplot(swirl[,3], xvar=NULL, col="green")

# Boxplots of pre-normalization red foreground intensities for each grid row
# for the Swirl 81 array.
boxplot(swirl[,1], xvar="maGridRow", yvar = "maRf", main = "Swirl array 81: pre-normalization red foreground intensity")

# Boxplots of pre-normalization log-ratios for each array in swirl
boxplot(swirl, main="Swirl arrays: pre-normalization log-ratios")

Subsetting methods for microarray objects

Description

Subsetting methods were defined for the microarray classes, marrayInfo , marrayLayout , marrayRaw and marrayNorm . These methods create instances of the given class, for a subset of spots and/or arrays in a batch.

Combine marrayRaw, marrayNorm or marrayInfo Objects

Description

Combine a series of marrayRaw , marrayNorm and marrayInfo objects.

Usage

list(list("cbind"), list("marrayRaw"))(list(), deparse.level=1)
list(list("cbind"), list("marrayNorm"))(list(), deparse.level=1)
list(list("rbind"), list("marrayInfo"))(list(), deparse.level=1)

Arguments

Details

cbind combines data objects assuming the same gene lists but different arrays. rbind combines data objects assuming equivalent arrays, i.e., the same RNA targets, but different genes.

For cbind , the matrices o f expression data from the individual objects are cbinded. The data.frames of target information, if they exist, are rbinded. The combined data object will preserve any additional components or attributes found in the first object to be combined. For rbind , the matrices of expression data are rbinded while the target information, in any, is unchanged.

Seealso

cbind in the base package.

Author

Jean Yang

Verifying the order between intensities matrix and target file information

Description

Check that the foreground and backgruond intensities are stored in the same order as provided in the first column of target file.

Usage

checkTargetInfo(mraw)

Arguments

Value

A logical value. This function returns "TRUE" if the first column from the Target information is the same order as the foreground and backgruond intensities.

Author

Yee Hwa (Jean) Yang

Examples

datadir <- system.file("swirldata", package="marray")
swirl.targets <- read.marrayInfo(file.path(datadir, "SwirlSample.txt"))
data(swirl)
swirl@maTargets <- swirl.targets

checkTargetInfo(swirl)

checkTargetInfo(swirl[, 2:4])

## reorder
swirl@maTargets <- swirl.targets[c(2:4, 1),]
checkTargetInfo(swirl)

Coerce an object to belong to a given microarray class

Description

Coercing methods were defined to convert microarray objects of one class into objects of another class, e.g., instances of the " class into instances of the " class.

Note

Use Package convert to convert object to other data types such as ExpressionSet and MAList .

Retrieve the Dimensions of an marrayRaw, marrayNorm or marrayInfo Object

Description

Retrieve the number of rows (genes) and columns (arrays) for an marrayRaw, marrayNorm or marrayInfo object.

Usage

list(list("dim"), list("marrayRaw"))(x)

Arguments

Details

Microarray data objects share many analogies with ordinary matrices in which the rows correspond to spots or genes and the columns to arrays. These methods allow one to extract the size of microarray data objects in the same way that one would do for ordinary matrices.

A consequence is that row and column commands nrow(x) , ncol(x) and so on also work.

Value

Numeric vector of length 2. The first element is the number of rows (genes) and the second is the number of columns (arrays).

Seealso

dim in the base package.

Author

modified from Gordon Smyth's function

Examples

M <- A <- matrix(11:14,4,2)
rownames(M) <- rownames(A) <- c("a","b","c","d")
colnames(M) <- colnames(A) <- c("A1","A2")
MA <- new("marrayNorm", maM=M,maA=A)
dim(MA)
dim(M)

Find ID when given an accession number

Description

Search gene ID with a vector of accession number from gene names or ID values.

Usage

findID(text, Gnames = gnames, ID = "Name")

Arguments

Value

A numeric vector the gene ID.

Seealso

grep

Author

Yee Hwa (Jean) Yang

Examples

data(swirl)
findID("fb24a09", swirl, ID="ID")
findID("geno1", swirl)

Display gene list as a HTML page

Description

Given a set of index to a data.frame containing gene names information. We create a web page with one element per genes that contains URLs links to various external database links. E.g Operon oligodatabase , Riken, GenBank and PubMed web sites.

Usage

htmlPage(genelist, filename = "GeneList.html", geneNames =
                 Gnames, mapURL = SFGL, othernames, title, table.head,
                 table.center = TRUE, disp = c("browser", "file")[1])
table2html(restable, filename = "GeneList.html", mapURL = SFGL,
                 title, table.head, table.center = TRUE, disp =
                 c("browser", "file")[1])

Arguments

Details

This function is an extension to ll.htmlpage

Value

No value is return, the function produce a html file "filename" and output the results in a browser.

Seealso

ll.htmlpage , URLstring , widget.mapGeneInfo

Author

Yee Hwa Yang

Examples

##library(annotate)
data(swirl)
Gnames <- maGeneTable(swirl)
swirlmap <- mapGeneInfo(Name = "none", ID="genbank")
## htmlPage(100:110, geneNames = Gnames, mapURL = swirlmap, title="Swirl")

moreinfo <- round(maM(swirl), 2)
swirlmap <- mapGeneInfo(Name = "pubmed", ID="genbank")
##htmlPage(100:110, geneNames = Gnames, mapURL = swirlmap, othernames=moreinfo, title="Swirl", disp="file")

Color image for cDNA microarray spot statistics

Description

We encourage users calling "image" rather than "maImage". The name of the arguments are change slightly. The function image creates spatial images of shades of gray or colors that correspond to the values of a statistic for each spot on the array. The statistic can be the intensity log-ratio M, a spot quality measure (e.g. spot size or shape), or a test statistic. This function can be used to explore whether there are any spatial effects in the data, for example, print-tip or cover-slip effects.

Usage

list(list("image"), list("marrayRaw"))(x, xvar = "maM", subset = TRUE, col, contours=FALSE,  bar = TRUE, overlay=NULL, ol.col=1, colorinfo=FALSE, ...)
list(list("image"), list("marrayNorm"))(x, xvar = "maM", subset = TRUE, col, contours=FALSE,  bar = TRUE, overlay=NULL, ol.col=1, colorinfo=FALSE, ...)

Arguments

Details

This function calls the general function maImage.func , which is not specific to microarray data. If there are more than one array in the batch, the plot is done for the first array, by default. Default color palettes were set for different types of spot statistics using the maPalette function. When x=c("maM", "maMloc", "maMscale") , a green-to-red color palette is used. When x=c("maGb", "maGf", "maLG") , a white-to-green color palette is used. When x=c("maRb", "maRf", "maLR") , a white-to-red color palette is used. The user has the option to overwrite these parameters at any point.

Value

If colorinfo is set to TRUE, the following list with elements will be returned.

*

Seealso

maImage , maImage.func , maColorBar , maPalette

Author

Jean Yang and Sandrine Dudoit

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# Examples use swirl dataset, for description type ? swirl
data(swirl)

# Microarray color palettes
Gcol <- maPalette(low = "white", high = "green", k = 50)
Rcol <- maPalette(low = "white", high = "red", k = 50)
BYcol <- maPalette(low = "blue", mid="gray", high = "yellow", k = 50)

# Color images of green and red background and foreground intensities
##image(swirl[, 2], xvar ="maGb")
##image(swirl[, 2], xvar ="maGf", subset = TRUE, col = Gcol, contours = FALSE, bar = TRUE, main="Swirl array 93")
##image(swirl[, 1], xvar ="maRb", contour=TRUE)
##image(swirl[, 4], xvar ="maRf", bar=FALSE)

# Color images of pre-normalization intensity log-ratios
##image(swirl[, 1])

# Color images with overlay spots
##image(swirl[, 3], xvar = "maA", overlay = maTop(maA(swirl[, 3]), h = 0.1, l = 0.1), bar = TRUE, main = "Image of A values with % 10 tails highlighted")

# Color image of print-tip-group
##image(swirl[, 1],xvar = "maPrintTip")

Stratified bivariate robust local regression

Description

This function performs robust local regression of a variable z on predictor variables x and y , separately within values of a fourth variable g . It is used by maNorm2D for 2D spatial location normalization.

Usage

ma2D(x, y, z, g, w=NULL, subset=TRUE, span=0.4, ...)

Arguments

Details

z is regressed on x and y , separately within values of g using the loess function.

Value

A numeric vector of fitted values.

Seealso

maNormMain , maNorm2D , loess .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# See examples for maNormMain.

Boxplots for cDNA microarray spot statistics

Description

The function maBoxplot produces boxplots of microarray spot statistics for the classes marrayRaw and marrayNorm .We encourage users to use "boxplot" rather than "maBoxplot". The name of the arguments have changed.

Usage

maBoxplot(m, x="maPrintTip", y="maM", ...)

Arguments

Details

If there are more than one array in the batch, the function produces a boxplot for each array in the batch. Such plots are useful when assessing the need for between array normalization, for example, to deal with scale differences among different arrays. Default graphical parameters are chosen for convenience using the function maDefaultPar (e.g. color palette, axis labels, plot title) but the user has the option to overwrite these parameters at any point.

Seealso

boxplot , maDefaultPar .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

## see example in boxplot

Calibration bar for color images

Description

This function produces a color image (color bar) which can be used for the legend to another color image obtained from the functions image , maImage , or maImage.func .

Usage

maColorBar(x, horizontal=TRUE, col=heat.colors(50), scale=1:length(x), k=10, ...)

Arguments

Seealso

image , maImage , maImage.func , maPalette .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine , Yee Hwa (Jean) Yang.

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

par(mfrow=c(3,1))
Rcol <- maPalette(low="white", high="red", k=10)
Gcol <- maPalette(low="white", high="green", k=50)
RGcol <- maPalette(low="green", high="red", k=100)
maColorBar(Rcol)
maColorBar(Gcol, scale=c(-5,5))
maColorBar(1:50, col=RGcol)

par(mfrow=c(1,3))
x<-seq(-1, 1, by=0.01)
maColorBar(x, col=Gcol, horizontal=FALSE, k=11)
maColorBar(x, col=Gcol, horizontal=FALSE, k=21)
maColorBar(x, col=Gcol, horizontal=FALSE, k=51)

Generate grid and spot matrix coordinates

Description

This function generates grid and spot matrix coordinates from ranges of rows and columns for the grid and spot matrices. Spots on the array are numbered consecutively starting from the top left grid and the top left spot within each grid.

Usage

maCompCoord(grows, gcols, srows, scols)

Arguments

Value

a matrix of spot four-coordinates, with rows corresponding to spots and columns to grid row, grid column, spot row, and spot column coordinates.

Seealso

marrayLayout , maCoord2Ind , maInd2Coord , maCompInd .

Author

Yee Hwa (Jean) Yang, Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

Examples

maCompCoord(1:2,1,1:4,1:3)

Generate spot indices

Description

This function generates spot indices from ranges of rows and columns for the grid and spot matrices. Spots on the array are numbered consecutively starting from the top left grid and the top left spot within each grid.

Usage

maCompInd(grows, gcols, srows, scols, L)

Arguments

Value

a numeric vector of spot indices.

Seealso

marrayLayout , maCoord2Ind , maInd2Coord , maCompCoord .

Author

Yee Hwa (Jean) Yang, Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

Examples

L <- new("marrayLayout", maNgr=4, maNgc=4, maNsr=22, maNsc=24)
maCompInd(1:2,1,1:4,1:3,L)

Generate a marrayLayout object

Description

Take a matrix of cooordiates and generate a marrayLayout object.

Usage

maCompLayout(mat, ncolumns = 4)

Arguments

Value

An object of class " .

Author

Jean Yang

Examples

X <- cbind(Block = c(1,1,2,2,3,3,4,4), Rows=c(1,2,1,2,1,2,1,2), Columns=rep(1,8))
maCompLayout(X, ncolumns=2)

Weights for composite normalization

Description

This function is used for composite normalization with intensity dependent weights. The function should be used as an argument to the main normalization function maNormMain . It only applies when two normalization procedures are combined.

Usage

maCompNormA()
maCompNormEq()

Value

A function which takes as arguments x and n , the spot average log-intensities A and the number of normalization procedures. This latter function returns a matrix of weights for combining two normalization procedures, rows correspond to spots and columns to normalization procedures. The weights for the first procedure are given by the empirical cumulative distribution function of the spot average log-intensities A. Note that when performing composite normalization as described in Yang et al. (2002), the first normalization procedure is the global fit and the second procedure is the within-print-tip-group fit. list() list() For maCompEq , equal weights are given for each procedure.

Seealso

maNormMain , maNormLoess , ecdf .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine , Yee Hwa (Jean) Yang.

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, list("The Analysis of Gene Expression Data: Methods and Software") , Springer, New York. list() list()

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. list("Nucleic Acids Research") , Vol. 30, No. 4.

Examples

# See examples for maNormMain

Generate plate IDs

Description

This function generates plate IDs from the dimensions of the grid and spot matrices. Note that this function only applies to arrays with a regular plate layout, where the number of spots is a multiple of the number of wells on a plate (usually 96 or 384) and each well contributes exactly one spot. It should thus be used with caution.

Usage

maCompPlate(x, n=384)

Arguments

Details

Having plate IDs may be useful for the purpose of normalization. Normalization by plate can be done using the function maNormMain .

Value

a vector of plate IDs ( factor ).

Seealso

marrayLayout , marrayRaw , marrayNorm

Author

Yee Hwa (Jean) Yang, Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

Examples

L<-new("marrayLayout", maNgr=4, maNgc=4, maNsr=22, maNsc=24)
plate<-maCompPlate(L,384)
table(plate)
maPlate(L)<-plate

Convert grid and spot matrix coordinates to spot indices

Description

This functions converts grid and spot matrix coordinates (four coordinates) to spot indices, where spots on the array are numbered consecutively starting from the top left grid and the top left spot within each grid.

Usage

maCoord2Ind(x, L)

Arguments

Value

a numeric vector of spot indices.

Seealso

marrayLayout , maInd2Coord , maCompCoord , maCompInd .

Author

Yee Hwa (Jean) Yang, Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

Examples

L <- new("marrayLayout", maNgr=4, maNgc=4, maNsr=22, maNsc=24)
coord<-cbind(rep(2,4),rep(1,4),rep(1,4),1:4)
maCoord2Ind(coord, L)

Default graphical parameters for microarray objects

Description

This function returns default graphical parameters for microarray objects. The parameters may be passed as arguments to the functions maBoxplot and maPlot .

Usage

maDefaultPar(m, x, y, z)

Arguments

Value

A list with elements

*

Seealso

maBoxplot , maPlot , maLegendLines , maLoessLines , maText , maDotsDefaults .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# See examples for maPlot.

Replace graphical default parameters by user supplied parameters

Description

This function may be used to compare default graphical parameters for microarray diagnostic plots to user supplied parameters given in ... . User supplied parameters overwrite the defaults. It is used in maBoxplot , maPlot , and maImage .

Usage

maDotsDefaults(dots, defaults)

Arguments

Value

*

Seealso

maDefaultPar , maBoxplot , maPlot , maImage .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

dots<-list(xlab="X1", ylab="Y1")
defaults<-list(xlab="X1", ylab="Y2", col=2)
pars<-maDotsDefaults(dots, defaults)

do.call("plot",c(list(x=1:10), pars))

Replace default arguments of a function by user supplied values

Description

This function may be used to replace default arguements for any functions to user supplied parameters.

Usage

maDotsMatch(dots, defaults)

Arguments

Value

*

Seealso

maDefaultPar , maDotsDefaults

Author

Jean Yee Hwa Yang

Examples

dots<-list(x=1:10, y=11:20)
argsfun <- maDotsMatch(dots, formals(args(plot)))
do.call("plot", argsfun)

Generating a vector recording the control status of the spotted probe sequences.

Description

ControlCode is a matrix representing certain regular expression pattern and the control status of the spotted probe sequences. This function uses grep' searches for matches topattern' (its first argument) within the character vector x' (second argument). ## Usage ```r maGenControls(Gnames, controlcode, id = "ID") ``` ## Arguments |Argument |Description| |------------- |----------------| |Gnames| An object of classmatrix,data.frameormarrayInfowhich contains description of spotted probe sequences.| |controlcode| A character matrix of n by 2 columns. The first column contains a few regular expression of spotted probe sequences and the second column contains the corresponding control status.| |id| the column number of column name inGnamesthat contains description of each spot on the array.| ## Value A vector of characters recording the control status of the spotted probe sequences. ## Seealso [grep`](#grep) ## Author Jean Yee Hwa Yang ## Examples r data(swirl) maControls(swirl) <- maGenControls(maGnames(swirl), id="Name") table(maControls(swirl))

Table of spot coordinates and gene names

Description

This function produces a table of spot coordinates and gene names for objects of class " and " .

Usage

maGeneTable(object)

Arguments

Value

an object of class data.frame , with rows corresponding to spotted probe sequences. The first four columns are the grid matrix and spot matrix coordinates, and the remaining columns are the spot descriptions stored in the maGnames slot of the microarray object.

Seealso

marrayInfo , marrayLayout , marrayRaw , marrayNorm , maCompCoord .

Author

Yee Hwa (Jean) Yang

Examples

# Example uses swirl dataset, for description type ? swirl

data(swirl)

tab<-maGeneTable(swirl)
tab[1:10,]

Color image for cDNA microarray spot statistics

Description

We encourage users calling "image" rather than "maImage". The name of the arguments are change slightly.

The function maImage creates spatial images of shades of gray or colors that correspond to the values of a statistic for each spot on the array. The statistic can be the intensity log-ratio M, a spot quality measure (e.g. spot size or shape), or a test statistic. This function can be used to explore whether there are any spatial effects in the data, for example, print-tip or cover-slip effects.

Usage

maImage(m, x="maM", subset=TRUE, col, contours=FALSE, bar=TRUE,
overlay=NULL, ol.col=1, colorinfo=FALSE, ...)

Arguments

Details

This function calls the general function maImage.func , which is not specific to microarray data. If there are more than one array in the batch, the plot is done for the first array, by default. Default color palettes were set for different types of spot statistics using the maPalette function. When x=c("maM", "maMloc", "maMscale") , a green-to-red color palette is used. When x=c("maGb", "maGf", "maLG") , a white-to-green color palette is used. When x=c("maRb", "maRf", "maLR") , a white-to-red color palette is used. The user has the option to overwrite these parameters at any point.

Value

If colorinfo is set to TRUE, the following list with elements will be returned.

*

Seealso

image , maImage.func , maColorBar , maPalette , summary .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# To see the demo type demo(marrayPlots)

# Examples use swirl dataset, for description type ? swirl
data(swirl)

# Microarray color palettes
Gcol <- maPalette(low = "white", high = "green", k = 50)
Rcol <- maPalette(low = "white", high = "red", k = 50)
RGcol <- maPalette(low = "green", high = "red", k = 50)

# Color images of green and red background and foreground intensities
maImage(swirl[, 3], x="maGb")
maImage(swirl[, 3], x = "maGf", subset = TRUE, col = Gcol, contours = FALSE, bar = TRUE, main="Swirl array 93")
maImage(swirl[, 3], x = "maRb", contour=TRUE)
maImage(swirl[, 3], x = "maRf", bar=FALSE)

# Color images of pre-normalization intensity log-ratios
maImage(swirl[, 1])
maImage(swirl[, 3], x = "maM", subset = maTop(maM(swirl[, 3]), h = 0.1, l = 0.1), col = RGcol, contours = FALSE, bar = TRUE, main = "Swirl array 93: image of pre-normalization M for % 10 tails")

# Color image of print-tip-group
maImage(swirl[, 1],x="maPrintTip")

Color image for cDNA microarray spot statistics

Description

This function creates spatial images of shades of gray or colors that correspond to the values of a statistic for each spot on the array. The statistic can be the intensity log-ratio M, a spot quality measure (e.g. spot size or shape), or a test statistic. This function can be used to explore whether there are any spatial effects in the data, for example, print-tip or cover-slip effects. This function is called by maImage .

Usage

maImage.func(x, L, subset=TRUE, col=heat.colors(12), contours=FALSE, overlay=NULL, ol.col=1,  ...)

Arguments

Seealso

image , maImage , maColorBar , maPalette .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# See examples for image.

Convert spot indices to grid and spot matrix coordinates

Description

This functions converts spot indices to grid and spot matrix coordinates (four coordinates), where spots on the array are numbered consecutively starting from the top left grid and the top left spot within each grid.

Usage

maInd2Coord(x, L)

Arguments

Value

a matrix of spot four-coordinates, with rows corresponding to spots and columns to grid row, grid column, spot row, and spot column coordinates.

Seealso

marrayLayout , maCoord2Ind , maCompCoord , maCompInd .

Author

Yee Hwa (Jean) Yang, Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

Examples

L <- new("marrayLayout", maNgr=4, maNgc=4, maNsr=22, maNsc=24)
maInd2Coord(c(1:10,529:538), L)

Add a legend to a plot

Description

This function may be used to add a legend for lines in plots such as those produced by plot , maPlot , or maPlot.func .

Usage

maLegendLines(legend="", col=2, lty=1, lwd=2.5, ncol=1, ...)

Arguments

Value

A function with bindings for legend , col , lty , lwd , ncol , and list() . This latter function takes as arguments x and y , the coordinates for the location of the legend on the plot, and it adds the legend to the current plot.

Seealso

legend , maPlot , maPlot.func .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# See examples for maPlot.

Stratified univariate robust local regression

Description

This function performs robust local regression of a variable y on predictor variable x , separately within values of a third variable z . It is used by maNormLoess for intensity dependent location normalization.

Usage

maLoess(x, y, z, w=NULL, subset=TRUE, span=0.4, list())

Arguments

Details

y is regressed on x , separately within values of z using the loess function.

Value

A numeric vector of fitted values.

Seealso

maNormMain , maNormLoess , loess .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, list("The Analysis of Gene Expression Data: Methods and Software") , Springer, New York. list() list()

Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed (2001). Normalization for cDNA microarray data. In M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), list("Microarrays: Optical Technologies and Informatics") , Vol. 4266 of list("Proceedings of SPIE") . list() list()

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. list("Nucleic Acids Research") , Vol. 30, No. 4.

Examples

# See examples for maNormMain.

Add smoothed fits to a plot

Description

This function may be used to compute and plot loess or lowess fits for an existing plot. The plot can be produced by plot , maPlot , or maPlot.func .

Usage

maLoessLines(subset=TRUE, weights=NULL, loess.args=list(span = 0.4,
degree=1, family="symmetric", control=loess.control(trace.hat =
"approximate", iterations=5, surface="direct")), col=2, lty=1, lwd=2.5, ...)
maLowessLines(subset = TRUE, f = 0.3, col = 2, lty = 1, lwd = 2.5, ...)

Arguments

Value

A function with bindings for subset , weights , loess.args , col , lty , lwd , and list() . This latter function takes as arguments x and y , the abscissa and ordinates of points on the plot, and z a vector of discrete values used to stratify the points. Loess (or lowess) fits are performed separately within values of z .

Seealso

loess , lowess , maPlot , maPlot.func .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# See examples for maPlot.

Stratified MAD calculation

Description

This function computes the median absolute deviation (MAD) of values in y separately within values of x . It is used by maNormMAD for MAD scale normalization.

Usage

maMAD(x, y, geo=TRUE, subset=TRUE)

Arguments

Value

A numeric vector of MAD values.

Seealso

maNormMain , maNormMAD , mad .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, list("The Analysis of Gene Expression Data: Methods and Software") , Springer, New York. list() list()

Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed (2001). Normalization for cDNA microarray data. In M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), list("Microarrays: Optical Technologies and Informatics") , Vol. 4266 of list("Proceedings of SPIE") . list() list()

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. list("Nucleic Acids Research") , Vol. 30, No. 4.

Examples

# See examples for maNormMain.

Stratified median calculation

Description

This function computes the median of values in y separately within values of x . It is used by maNormMed for median location normalization.

Usage

maMed(x, y, subset=TRUE)

Arguments

Value

A numeric vector of median values.

Seealso

maNormMain , maNormMed , median .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, list("The Analysis of Gene Expression Data: Methods and Software") , Springer, New York. list() list()

Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed (2001). Normalization for cDNA microarray data. In M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), list("Microarrays: Optical Technologies and Informatics") , Vol. 4266 of list("Proceedings of SPIE") . list() list()

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. list("Nucleic Acids Research") , Vol. 30, No. 4.

Examples

# See examples for maNormMain.

Basic Statistical Functions for Handling Missing Values

Description

Basic statistical functions for handling missing values or NA. list() In log.na , sum.na , mean.na and var.na , quantile.na , length.na , missing values are omitted from the calculation. list() The function cor.na calls cor with the argument use="pairwise.complete.obs" . list() The function order.na only handles vector arguments and not lists. However, it gives the option of omitting the NAs ( na.last=NA ), of placing the NAs at the start of the ordered vector ( na.last=F ) or at the end ( na.last=T ). list() The function scale.na is a modified version of scale which allows NAs in the variance calculation. If scale = T , the function f in scale.na uses var.na to perform the variance calculation. The function prod.na is similar to the prod function with na.rm=TRUE . This function returns the product of all the values present in its arguments, omitting any missing values.

Seealso

log , sum , mean , var , cor , order , scale , prod .

Author

Yee Hwa Yang, list("jean@biostat.berkeley.edu") list()

Simple location and scale normalization function

Description

This function is a simple wrapper function around the main normalization function maNormMain . It allows the user to choose from a set of six basic location and scale normalization procedures. The function operates on an object of class " (or possibly " , if normalization is performed in several steps) and returns an object of class " .

Usage

maNorm(mbatch, norm=c("printTipLoess", "none", "median", "loess",
"twoD", "scalePrintTipMAD"), subset=TRUE, span=0.4, Mloc=TRUE,
Mscale=TRUE, echo=FALSE, ...)

Arguments

|norm | Character string specifying the normalization procedures: list(" ", list(list("none"), list("no normalization")), " ", list(list("median"), list("for global median location normalization")), " ", list(list("loess"), list("for global intensity or A-dependent location normalization using ", "the ", list(list("loess")), " function")), " ", list(list("twoD"), list("for 2D spatial location normalization using the ", list(list("loess")), " function")), " ", list(list("printTipLoess"), list("for within-print-tip-group intensity dependent location ", |

"normalization using the ", list(list("loess")), " function")), "

", list(list("scalePrintTipMAD"), list("for within-print-tip-group intensity dependent ", "location normalization followed by within-print-tip-group scale normalization ", "using the median absolute deviation (MAD). ", list(), " ")), " ", "This argument can be specified using the first letter of each method.") |subset | A "logical" or "numeric" vector indicating the subset of points used to compute the normalization values.| |span | The argument span which controls the degree of smoothing in the loess function.| |Mloc | If TRUE , the location normalization values are stored in the slot maMloc of the object of class " returned by the function, if FALSE , these values are not retained.| |Mscale | If TRUE , the scale normalization values are stored in the slot maMscale of the object of class " returned by the function, if FALSE , these values are not retained.| |echo | If TRUE , the index of the array currently being normalized is printed.| |... | Misc arguments|

Details

See maNormMain for details and also more general procedures.

Value

*

Seealso

maNormMain , maNormScale .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, list("The Analysis of Gene Expression Data: Methods and Software") , Springer, New York. list() list()

Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed (2001). Normalization for cDNA microarray data. In M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), list("Microarrays: Optical Technologies and Informatics") , Vol. 4266 of list("Proceedings of SPIE") . list() list()

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. list("Nucleic Acids Research") , Vol. 30, No. 4.

Examples

# Examples use swirl dataset, for description type ? swirl
data(swirl)

# Global median normalization for swirl arrays 2 and 3
mnorm<-maNorm(swirl[,2:3], norm="median", echo=TRUE)

# Within-print-tip-group loess location normalization for swirl array 1
mnorm<-maNorm(swirl[,1], norm="p", span=0.45)

2D spatial location normalization function

Description

This function is used for 2D spatial location normalization, using the robust local regression function loess . It should be used as an argument to the main normalization function maNormMain .

Usage

maNorm2D(x="maSpotRow", y="maSpotCol", z="maM", g="maPrintTip", w=NULL,
subset=TRUE, span=0.4, ...)

Arguments

Details

The spot statistic named in z is regressed on spot row and column coordinates, separately within print-tip-group, using the loess function.

Value

A function with bindings for the above arguments. This latter function takes as argument an object of class " (or possibly " ), and returns a vector of fitted values to be substracted from the raw log-ratios. It calls the function ma2D , which is not specific to microarray objects.

Seealso

maNormMain , ma2D , loess .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# See examples for maNormMain.

Intensity dependent location normalization function

Description

This function is used for intensity dependent location normalization, using the robust local regression function loess . It should be used as an argument to the main normalization function maNormMain .

Usage

maNormLoess(x="maA", y="maM", z="maPrintTip", w=NULL, subset=TRUE,
span=0.4, ...)

Arguments

Value

A function with bindings for the above arguments. This latter function takes as argument an object of class " (or possibly " ), and returns a vector of fitted values to be substracted from the raw log-ratios. It calls the function maLoess , which is not specific to microarray objects.

Seealso

maNormMain , maLoess , loess .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, list("The Analysis of Gene Expression Data: Methods and Software") , Springer, New York. list() list()

Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed (2001). Normalization for cDNA microarray data. In M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), list("Microarrays: Optical Technologies and Informatics") , Vol. 4266 of list("Proceedings of SPIE") . list() list()

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. list("Nucleic Acids Research") , Vol. 30, No. 4.

Examples

# See examples for maNormMain.

MAD scale normalization function

Description

This function is used for scale normalization using the median absolute deviation (MAD) of intensity log-ratios for a group of spots. It can be used for within or between array normalization. The function should be used as an argument to the main normalization function maNormMain .

Usage

maNormMAD(x=NULL, y="maM", geo=TRUE, subset=TRUE)

Arguments

Value

A function with bindings for the above arguments. This latter function takes as argument an object of class " (or possibly " ), and returns a vector of values used to scale the location normalized log-ratios. It calls the function maMAD , which is not specific to microarray objects.

Seealso

maNormMain , maMAD , mad .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, list("The Analysis of Gene Expression Data: Methods and Software") , Springer, New York. list() list()

Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed (2001). Normalization for cDNA microarray data. In M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), list("Microarrays: Optical Technologies and Informatics") , Vol. 4266 of list("Proceedings of SPIE") . list() list()

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. list("Nucleic Acids Research") , Vol. 30, No. 4.

Examples

# See examples for maNormMain.

Main function for location and scale normalization of cDNA microarray data

Description

This is the main function for location and scale normalization of cDNA microarray data. Normalization is performed for a batch of arrays using location and scale normalization procedures specified by the lists of functions f.loc and f.scale . Typically, only one function is given in each list, otherwise composite normalization is performed using the weights computed by the functions a.loc and a.scale . The function operates on an object of class " (or possibly " , if normalization is performed in several steps) and returns an object of class " . Simple wrapper functions are provided by maNorm and maNormScale .

Usage

maNormMain(mbatch, f.loc=list(maNormLoess()), f.scale=NULL,
a.loc=maCompNormEq(), a.scale=maCompNormEq(), Mloc=TRUE, Mscale=TRUE, echo=FALSE)

Arguments

Details

When both location and scale normalization functions ( f.loc and f.scale ) are passed, location normalization is performed before scale normalization. That is, scale values are computed for the location normalized log-rations. The same results could be obtained by two applications of the function maNormMain , first with only the location normalization function and f.scale=NULL , and second with only the scale normalization function and f.loc=NULL .

Value

*

Seealso

maNorm , maNormScale , maNormLoess , maLoess , maNormMAD , maMAD , maNormMed , maMed , maNorm2D , ma2D , maCompNormA , maCompNormEq .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, list("The Analysis of Gene Expression Data: Methods and Software") , Springer, New York. list() list()

Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed (2001). Normalization for cDNA microarray data. In M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), list("Microarrays: Optical Technologies and Informatics") , Vol. 4266 of list("Proceedings of SPIE") . list() list()

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. list("Nucleic Acids Research") , Vol. 30, No. 4.

Examples

# Examples use swirl dataset, for description type ? swirl
data(swirl)

# Within-print-tip-group loess location normalization of batch swirl
# - Default normalization
swirl.norm<-maNormMain(swirl)

boxplot(swirl.norm)
boxplot(swirl.norm[,3])
plot(swirl.norm[,3])

# Global median normalization for arrays 81 and 82
swirl.norm <- maNormMain(swirl[,1:2], f.loc = list(maNormMed(x=NULL,y="maM")))

# Global loess normalization for array 81
swirl.norm <- maNormMain(swirl[,1], f.loc = list(maNormLoess(x="maA",y="maM",z=NULL)))

# Composite normalization as in Yang et al. (2002)
# No MSP controls are available here, so all spots are used for illustration
# purposes
swirl.norm <- maNormMain(swirl[,1], f.loc = list(maNormLoess(x="maA",y="maM",z=NULL),maNormLoess(x="maA",y="maM",z="maPrintTip")), a.loc=maCompNormA())

Median location normalization function

Description

This function is used for location normalization using the median of intensity log-ratios for a group of spots. The function should be used as an argument to the main normalization function maNormMain .

Usage

maNormMed(x=NULL, y="maM", subset=TRUE)

Arguments

Value

A function with bindings for the above arguments. This latter function takes as argument an object of class " (or possibly " ), and returns a vector of fitted values to be subtracted from the raw log-ratios. It calls the function maMed , which is not specific to microarray objects.

Seealso

maNormMain , maMed , median .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, list("The Analysis of Gene Expression Data: Methods and Software") , Springer, New York. list() list()

Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed (2001). Normalization for cDNA microarray data. In M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), list("Microarrays: Optical Technologies and Informatics") , Vol. 4266 of list("Proceedings of SPIE") . list() list()

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. list("Nucleic Acids Research") , Vol. 30, No. 4.

Examples

# See examples for maNormMain.

Simple scale normalization function

Description

This function is a simple wrapper function around the main normalization function maNormMain . It allows the user to choose from a set of two basic scale normalization procedures. The function operates on an object of class " (or possibly " , if normalization is performed in several steps) and returns an object of class " . This function can be used to conormalize a batch of arrays ( norm="globalMAD" option).

Usage

maNormScale(mbatch, norm=c("globalMAD", "printTipMAD"), subset=TRUE, geo=TRUE,  Mscale=TRUE, echo=FALSE)

Arguments

|norm | A character string specifying the normalization procedures: list(" ", list(list("globalMAD"), list("for global scale ", " normalization using the median absolute deviation (MAD), this allows between ", "slide scale normalization")), " ", list(list("printTipMAD"), list("for within-print-tip-group scale normalization ", " using the median absolute deviation (MAD).")), " ", "This argument can be ", " specified using the first letter of each method.")| |subset | A "logical" or "numeric" vector indicating the subset of points used to compute the normalization values.| |geo | If TRUE , the MAD of each group is divided by the geometric mean of the MADs across groups (cf. Yang et al. (2002)). This allows observations to retain their original units.| |Mscale | If TRUE , the scale normalization values are stored in the slot maMscale of the object of class " returned by the function, if FALSE , these values are not retained.| |echo | If TRUE , the index of the array currently being normalized is printed.|

Details

See maNormMain for details and more general procedures.

Value

*

Seealso

maNormMain , maNorm .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, list("The Analysis of Gene Expression Data: Methods and Software") , Springer, New York. list() list()

Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed (2001). Normalization for cDNA microarray data. In M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), list("Microarrays: Optical Technologies and Informatics") , Vol. 4266 of list("Proceedings of SPIE") . list() list()

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng, J. Ngai, and T. P. Speed (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. list("Nucleic Acids Research") , Vol. 30, No. 4.

Examples

# Examples use swirl dataset, for description type ? swirl
data(swirl)

# Global median normalization followed by global MAD normalization for
# only arrays 2 and 3 in the batch swirl

mnorm1<-maNorm(swirl[,2:3], norm="m")
mnorm2<-maNormScale(mnorm1, norm="g")

Convert a numeric vector of indices to a logical vector

Description

This function converts a numeric vector of indices to a logical vector. It is used for subsetting purposes.

Usage

maNum2Logic(n=length(subset), subset=TRUE)

Arguments

Value

a logical vector.

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

Examples

maNum2Logic(10, 1:3)

Microarray color palette

Description

This function returns a vector of color names corresponding to a range of colors specified in the arguments.

Usage

maPalette(low = "white", high = c("green", "red"), mid=NULL, k =50)

Arguments

Value

A "character" vector of color names. This can be used to create a user-defined color palette for subsequent graphics by palette , in a col= specification in graphics functions, or in par .

Seealso

image , maColorBar , maImage , maImage.func .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine , Yee Hwa (Jean) Yang.

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

par(mfrow=c(1,4))
pal <- maPalette(low="red", high="green")
maColorBar(seq(-2,2, 0.2), col=pal, horizontal=FALSE, k=21)
pal <- maPalette(low="red", high="green", mid="yellow")
maColorBar(seq(-2,2, 0.2), col=pal, horizontal=FALSE, k=21)
pal <- maPalette()
maColorBar(seq(-2,2, 0.2), col=pal, horizontal=FALSE, k=21)
pal <- maPalette(low="purple", high="purple",mid="white")
maColorBar(seq(-2,2, 0.2), col=pal, horizontal=FALSE, k=21)

Scatter-plots for cDNA microarray spot statistics

Description

The function maPlot produces scatter-plots of microarray spot statistics for the classes " and " . It also allows the user to highlight and annotate subsets of points on the plot, and display fitted curves from robust local regression or other smoothing procedures.

Usage

maPlot(m, x="maA", y="maM", z="maPrintTip", lines.func, text.func, legend.func, list())

Arguments

Details

This function calls the general function maPlot.func , which is not specific to microarray data. If there are more than one array in the batch, the plot is done for the first array, by default. Default graphical parameters are chosen for convenience using the function maDefaultPar (e.g. color palette, axis labels, plot title) but the user has the option to overwrite these parameters at any point.

Seealso

maPlot.func , maDefaultPar , maLoessLines , maLegendLines , maText , plot , lowess , loess , legend .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# To see the demo type demo(marrayPlots)

# Examples use swirl dataset, for description type ? swirl
data(swirl)

# - Default arguments
maPlot(swirl)

# Lowess fit using all spots
maPlot(swirl, z=NULL, legend.func=NULL)

# Loess fit using all spots
maPlot(swirl, z=NULL, legend.func=maLegendLines(legend="All spots",col="green"), lines.func=maLoessLines(loess.args=list(span=0.3),col="green"))

# Pre-normalization MA-plot for the Swirl 81 array, with the lowess fits for
# individual grid columns and 1% tails of M highlighted
defs <- maDefaultPar(swirl[, 1], x = "maA", y = "maM", z = "maGridCol")
legend.func <- do.call("maLegendLines", defs$def.legend)
lines.func <- do.call("maLowessLines", c(list(TRUE, f = 0.3), defs$def.lines))
text.func<-maText(subset=maTop(maM(swirl)[,1],h=0.01,l=0.01), labels="o", col="violet")
maPlot(swirl[, 1], x = "maA", y = "maM", z = "maGridCol", lines.func=lines.func, text.func = text.func, legend.func=legend.func, main = "Swirl array 81: pre-normalization MA-plot")

Scatter-plots with fitted curves and text

Description

This function produces scatter-plots of x vs. y . It also allows the user to highlight and annotate subsets of points on the plot, and display fitted curves from robust local regression or other smoothing procedures.

Usage

maPlot.func(x, y, z, 
    lines.func = maLowessLines(subset = TRUE, f = 0.3, col = 1:length(unique(z)), lty = 1, lwd = 2.5),
        text.func = maText(), 
    legend.func = maLegendLines(legend = as.character(unique(z)), col = 1:length(unique(z)), lty = 1, lwd = 2.5, ncol = 1),
     ...)

Arguments

Seealso

maPlot , maLoessLines , maLegendLines , maText , plot , lowess , loess , legend .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# See examples for maPlot.

Select genes according to the values of a few different statistics

Description

Select genes by considering the union or intersect of multiple statistics.

Usage

maSelectGnames(statdata, crit1 = 50, crit2 = crit1, sub = TRUE, selectstat, operate = c("intersect", "union"))

Arguments

Details

This functions calls stat.gnames to select say the 100 most extreme genes from various statistics and combined the different gene lists by either union or intersection.

Value

A vector of numeric values.

Seealso

stat.gnames , order

Author

Jean Yee Hwa Yang

Examples

X <- matrix(rnorm(1000), 100,10)
Xstat <- cbind(mean=apply(X, 1, mean, na.rm=TRUE),
var=apply(X, 1, var, na.rm=TRUE))
maSelectGnames(Xstat, crit1=50)

Highlight points on a plot

Description

This function may be used to highlight a subset of points on an existing plot, such as a plot produced by plot , maPlot , or maPlot.func .

Usage

maText(subset=NULL, labels=as.character(1:length(subset)), ...)

Arguments

Value

A function with bindings for subset , labels , and list() . This latter function takes as arguments x and y , the absissa and ordinates of points on the plot.

Seealso

text , maPlot , maPlot.func .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# See examples for maPlot.

Identify extreme values

Description

This function determines which values in a numeric vector are above or below user supplied cut-offs.

Usage

maTop(x, h=1, l=1)

Arguments

Value

A "logical" vector indicating which entries are above or below the cut-offs.

Seealso

maPlot , maImage , quantile .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# See examples for maPlot.

Changing signs for two sample analysis

Description

Taking target file information and flip the dye swaps experiments.

Usage

maTwoSamples(targetfile, normdata, Trt, Ctl, targetID = "TargetName", slidesID = "Slides", dyesID = "Dyes", RedID = 5, path = ".", output = TRUE)

Arguments

Value

An objects of 'marrayNorm' with the dye assignment adjusted.

Author

Yee Hwa (Jean) Yang

Creating URL strings for external database links

Description

These functions are used with htmlPage . The function mapGeneInfo , takes all the arguments and generate a character matrix of two columns. The first columns representing the name of the argument and the second columns represents the value of an argument. The function widget.mapGeneInfo allows the user to enter this information interactively.

Usage

mapGeneInfo(widget = FALSE, Gnames, Name = "pubmed", ID =
                 "genbank", ACC = "SMDacc", ...)
widget.mapGeneInfo(Gnames)

Arguments

Details

The function mapGeneInfo generates a character matrix with the first column representing the column headings of "Gnames" and the second column representing the corresponding names in the list URLstring . For example, if a particular column in "Gnames" with column names "ID" contains genebank accession number, then the function mapGeneInfo generates a row containing "ID" in the first column and "genbank" in the second. Examples are SFGL and UCBFGL . list()

URLstring is a list contains the URL to various external database, E.g. operon, Riken, genbank. list() The current choices are: "pubmed", "locuslink", "riken", "SMDclid", "SMDacc", "operonh2", "operonh1" , "operonm2", "operonm1" and "genbank" . "SMDclid" and "SMDacc" are links to Stanford Microarray Databases.

Author

Jean Yee Hwa Yang

Examples

mapGeneInfo(ID="genebank", ll="locuslink")
mapGeneInfo(ID="locuslink", Sample.ID="riken")

Class "marrayInfo", description of target samples or spotted probe sequences

Description

This class is used to store information on target samples hybridized to a batch of arrays or probe sequences spotted onto these arrays. It is not specific to the microarray context.

Seealso

marrayLayout , marrayRaw , marrayNorm .

Author

Jean Yang and Sandrine Dudoit

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

## See marrayRaw

Class "marrayLayout", classes and methods for layout parameters of cDNA microarrays

Description

This class is used to keep track of important layout parameters for two-color cDNA microarrays. It contains slots for: the total number of spotted probe sequences on the array, the dimensions of the spot and grid matrices, the plate origin of the probes, information on spotted control sequences (e.g. probe sequences which should have equal abundance in the two target samples, such as housekeeping genes). The terms print-tip-group , grid , spot matrix , and sector are used interchangeably and refer to a set of spots printed using the same print-tip.

Seealso

marrayRaw , marrayNorm , marrayInfo and [[-methods](#[-methods) .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

## See marrayRaw

Class "marrayNorm", classes and methods for post-normalization cDNA microarray intensity data

Description

This class represents post-normalization intensity data for a batch of cDNA microarrays. A batch of arrays consists of a collection of arrays with the same layout ( " ). The class contains slots for the average log-intensities A, the normalized log-ratios M, the location and scale normalization values, the layout of the arrays, and descriptions of the target samples hybridized to the arrays and probe sequences spotted onto the arrays.

Seealso

marrayLayout , marrayRaw , marrayInfo

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# Examples use swirl dataset, for description type ? swirl

data(swirl)

# Median normalization
mnorm<-maNorm(swirl[,2:3],norm="m")

# Object of class marrayNorm for the second and third swirl arrays
mnorm

# Function call
maNormCall(mnorm)

#  Object of class marrayInfo -- Probe sequences
maGnames(mnorm)

#  Object of class marrayInfo -- Target samples
maTargets(mnorm)

# Density plot of log-ratios M for third array
plot(density(maM(mnorm[,2])), lwd=2, col=2, main="Density plots of log-ratios M")
lines(density(maM(swirl[,3])), lwd=2)
abline(v=0)
legend(2,1,c("Pre-normalization","Post-normalization"))

Class "marrayRaw", classes and methods for pre-normalization cDNA microarray intensity data

Description

This class represents pre-normalization intensity data for a batch of cDNA microarrays. A batch of arrays consists of a collection of arrays with the same layout ( " ). The class contains slots for the green (Cy3) and red (Cy5) foreground and background intensities, the layout of the arrays, and descriptions of the target samples hybridized to the arrays and probe sequences spotted onto the arrays.

Seealso

marrayLayout , marrayNorm , marrayInfo .

Author

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine .

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# Examples use swirl dataset, for description type ? swirl
require(limma)
data(swirl)

# Object of class marrayRaw for the 4 swirl arrays
swirl

# Object of class marrayLayout
maLayout(swirl)

# Access only the first 100 spots of the third array
swirl[1:100,3]

# Accessor methods -- How many spots on the array
maNspots(swirl)

# Density plot of log-ratios M for third array
plot(density(maM(swirl[,3])))

# Assignment methods -- Replace maNotes slot
maNotes(swirl)
maNotes(swirl)<-"This is a zebrafish microarray"
maNotes(swirl)

Internal marray functions

Description

Internal marray functions

Details

These are not to be called by the user.

Determine the operon oligo set ID

Description

This functions looks the operon ID and determine whether it belongs to "Human Genome Oligo Set V1", "Human Genome Oligo Set V2", "Mouse Genome Oligo Set V1" or "Mouse Genome Oligo Set V2".

Usage

opVersionID(opID)

Arguments

Value

A value "operonh1", "operonh2", "operonm1" or "operonm2" to represents "Human Genome Oligo Set V1", "Human Genome Oligo Set V2", "Mouse Genome Oligo Set V1" or "Mouse Genome Oligo Set V2".

Seealso

URLstring , htmlPage

Author

Jean Yee Hwa Yang

References

http://oparray.operon.com/

Examples

opVersionID("M000205_01")
URLstring[opVersionID("M000205_01")]

Scatter-plots for cDNA microarray spot statistics

Description

The function maPlot or plot produces scatter-plots of microarray spot statistics for the classes " , " . It also allows the user to highlight and annotate subsets of points on the plot, and display fitted curves from robust local regression or other smoothing procedures.

Usage

list(list("plot"), list("marrayRaw"))(x, xvar = "maA", yvar = "maM", zvar="maPrintTip", lines.func,text.func,legend.func, list())
list(list("plot"), list("marrayNorm"))(x, xvar = "maA", yvar = "maM", zvar="maPrintTip", lines.func,text.func,legend.func, list())
addText(object, xvar="maA", yvar="maM", subset=NULL, labels=as.character(1:length(subset)), list())
addPoints(object, xvar="maA", yvar="maM", subset=TRUE, list())
addLines(object, xvar="maA", yvar="maM", zvar="maPrintTip", subset=TRUE, list())
list(list("text"), list("marrayRaw"))(x, xvar = "maA", yvar = "maM", list())
list(list("text"), list("marrayNorm"))(x, xvar = "maA", yvar = "maM", list())
list(list("lines"), list("marrayRaw"))(x, xvar = "maA", yvar = "maM", zvar = "maPrintTip", list())
list(list("lines"), list("marrayNorm"))(x, xvar = "maA", yvar = "maM", zvar = "maPrintTip",list())
list(list("points"), list("marrayRaw"))(x, xvar = "maA", yvar = "maM", list())
list(list("points"), list("marrayNorm"))(x, xvar = "maA", yvar = "maM", list())

Arguments

Details

This function calls the general function maPlot.func , which is not specific to microarray data. If there are more than one array in the batch, the plot is done for the first array, by default. Default graphical parameters are chosen for convenience using the function maDefaultPar (e.g. color palette, axis labels, plot title) but the user has the option to overwrite these parameters at any point.

Seealso

maPlot.func , maDefaultPar , maLoessLines , maLegendLines , maText , plot , lowess , loess , legend .

Author

Jean Yee Hwa Yang

References

S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, editors, The Analysis of Gene Expression Data: Methods and Software , Springer, New York.

Examples

# To see the demo type demo(marrayPlots)

# Examples use swirl dataset, for description type ? swirl
data(swirl)

# Pre-normalization MA-plot for the Swirl 93 array, with the lowess fits for
# individual print-tip-groups.
# - Default arguments
plot(swirl[,3])

# Lowess fit using all spots
plot(swirl[,3], zvar=NULL, legend.func=NULL)

# Loess fit using all spots
plot(swirl[,3], zvar=NULL, legend.func=maLegendLines(legend="All spots",col="green"), lines.func=maLoessLines(loess.args=list(span=0.3),col="green"))

Printing summary methods for microarray objects

Description

Print methods were defined for the microarray classes, " , " , " , " . These methods produce summaries of the intensity and textual data stored in different classes of microarray objects.

Reading GenePix Gal file

Description

Reading a standard Gal file containing gene information.

Usage

read.Galfile(galfile, path = ".", info.id = c("ID", "Name"),
layout.id =c(Block="Block", Row="Row", Column="Column"),
labels = "ID", notes = "", sep = "  ", skip = NULL,   ncolumns=4, ...)

Arguments

Value

*

Seealso

read.marrayInfo , read.marrayLayout

Author

Yee Hwa (Jean) Yang

Examples

library(marray)
datadir <- system.file("swirldata", package="marray")
try <- read.Galfile(galfile="fish.gal", path=datadir)
names(try)
try$layout
try$gnames

Create objects of class marrayInfo

Description

This function creates objects of class marrayInfo . The marrayInfo class is used to store information regarding the target mRNA samples co-hybridized on the arrays or the spotted probe sequences (e.g. data frame of gene names, annotations, and other identifiers).

Usage

read.marrayInfo(fname, info.id=NULL, labels=NULL, notes=fname, sep="    ",skip=0, quote=""", ...)

Arguments

Value

An object of class marrayInfo .

Author

Jean Yang, yeehwa@stat.berkeley.edu

References

http://www.bioconductor.org/

Examples

datadir <- system.file("swirldata", package="marray")

## Reading target sample information
swirl.samples <- read.marrayInfo(file.path(datadir, "SwirlSample.txt"))

## Reading probe sequence information
swirl.gnames <- read.marrayInfo(file.path(datadir, "fish.gal"),
info.id=4:5, labels=5, skip=21)

Create objects of class marrayLayout

Description

This function creates objects of class marrayLayout to store layout parameters for two-color cDNA microarrays.

Usage

read.marrayLayout(fname = NULL, ngr, ngc, nsr, nsc, pl.col = NULL, ctl.col = NULL, sub.col = NULL, notes = fname, skip, sep = " ", quote = """, ...)

Arguments

Value

An object of class marrayLayout .

Author

Jean Yang yeehwa@stat.berkeley.edu

References

http://www.bioconductor.org/

Examples

datadir <- system.file("swirldata", package="marray")

### Reading in control information from file
skip <-  grep("Row", readLines(file.path(datadir,"fish.gal"), n=100)) - 1
swirl.layout <- read.marrayLayout(fname=file.path(datadir,"fish.gal"), ngr=4, ngc=4,
nsr=22, nsc=24, ctl.col=4, skip=skip)

### Setting control information.
swirl.gnames <- read.marrayInfo(file.path(datadir,"fish.gal"), info.id=4:5, labels=5, skip=21)
x <-  maInfo(swirl.gnames)[,1]
y <- rep(0, maNspots(swirl.layout))
y[x == "control"] <- 1
slot(swirl.layout, "maControls") <- as.factor(y)

Create objects of class "marrayRaw"

Description

This function reads in cDNA microarray data from a directory and creates objects of class " from spot quantification data files obtained from image analysis software or databases.

Usage

read.marrayRaw(fnames, path=".", name.Gf=NULL, name.Gb=NULL, name.Rf=NULL,
name.Rb=NULL,name.W=NULL, layout=NULL, gnames=NULL, targets=NULL,
notes=NULL, skip=NULL, sep="    ", quote=""", DEBUG=FALSE, ...)
read.GenePix(fnames = NULL, path = NULL, name.Gf = "F532 Median",
name.Gb ="B532 Median", name.Rf = "F635 Median", name.Rb = "B635 Median",
name.W ="Flags", layout = NULL, gnames = NULL, targets = NULL,
notes = NULL, skip=NULL, sep = "    ", quote = """, DEBUG=FALSE, ...)
read.SMD(fnames = NULL, path = NULL, name.Gf = "Ch1 Intensity (Median)",
name.Gb = "Ch1 Background (Median)", name.Rf = "Ch2 Intensity (Median)",
name.Rb = "Ch2 Background (Median)", name.W = NULL, info.id = c("Name",
"Clone ID"), layout = NULL, gnames = NULL, targets = NULL, notes = NULL, skip = NULL, sep = "   ", quote = """, DEBUG=FALSE, ...)
read.Spot(fnames = NULL, path = ".", name.Gf = "Gmean", name.Gb =
"morphG", name.Rf = "Rmean", name.Rb = "morphR",name.W = NULL, layout =
NULL, gnames = NULL, targets = NULL, notes = NULL, skip = NULL, sep = " ", quote = """, DEBUG=FALSE, ...)
read.Agilent(fnames = NULL, path=NULL, name.Gf = "gMedianSignal", name.Gb = "gBGMedianSignal", name.Rf = "rMedianSignal", name.Rb = "rBGMedianSignal", name.W= NULL, layout = NULL, gnames = NULL, targets = NULL, notes=NULL, skip=NULL, sep=" ", quote=""", DEBUG=FALSE, info.id=NULL, ...)
widget.marrayRaw(ext = c("spot", "xls", "gpr"), skip = 0, sep = "   ",  quote = """, ...)

Arguments

Value

An object of class " .

Seealso

scan , read.marrayLayout , read.marrayInfo

Author

Jean Yang, yeehwa@stat.berkeley.edu

References

http://www.bioconductor.org/ .

Examples

datadir <- system.file("swirldata", package="marray")

## Quick guide
swirl.targets <- read.marrayInfo(file.path(datadir, "SwirlSample.txt"))
data <- read.Spot(path=datadir, targets=swirl.targets)

## Alternate commands
skip <-  grep("Row", readLines(file.path(datadir,"fish.gal"), n=100)) - 1

swirl.layout <- read.marrayLayout(ngr=4, ngc=4, nsr=22, nsc=24)

swirl.targets <- read.marrayInfo(file.path(datadir, "SwirlSample.txt"))

swirl.gnames <- read.marrayInfo(file.path(datadir, "fish.gal"),
info.id=4:5, labels=5, skip=skip)

x <-  maInfo(swirl.gnames)[,1]
y <- rep(0, maNspots(swirl.layout))
y[x == "control"] <- 1
slot(swirl.layout, "maControls") <- as.factor(y)

fnames <- dir(path=datadir,pattern="spot")
swirl<- read.Spot(fnames, path=datadir,
layout = swirl.layout,
gnames = swirl.gnames,
targets = swirl.targets)

Remove missing values

Description

Remove NA's, NAN's and INF's from a vector.

Usage

rm.na(x)

Arguments

Value

A vector with all NA's remove.

Author

Jean Yang

Examples

x <- round(rnorm(10), 2)
x[c(2,4,5)] <- NA
x
rm.na(x)

Show Large Data Object - class

Description

A virtual class including the data classes marrayRaw , marrayNorm , marrayInfo , marrayLayout , PrinterInfo , RGData and MAData , all of which typically contain large quantities of numerical data in vector, matrices and data.frames.

Author

modifid from Gordon Smyth's function

Rank genes according to the value of a statistic.

Description

Select values based on intensities binning.

Usage

stat.confband.text(M, A, crit1=0.025, crit2=crit1, nclass=5)

Arguments

Value

A vector of selected spot index.

Seealso

stat.gnames

Examples

library(marray)
data(swirl)
aveA <- apply(maA(swirl), 1, mean.na)
aveM <- apply(maM(swirl), 1, mean.na)
stat.confband.text(aveM, aveA, crit1=20, crit2=50, nclass=5)

Sort Genes According to the Value of a Statistic

Description

Lists genes and corresponding statistics in decreasing order of the statistics. This function applies to any type of statistic, including log ratios, one and two-sample t-statistics, and F-statistics. Missing values are ignored, as in sort .

Usage

stat.gnames(x, gnames, crit= 50)

Arguments

Value

List containing the following components

*

Seealso

order , sort .

Author

Yee Hwa Yang, list("yeehwa@stat.berkeley.edu") list() Sandrine Dudoit, list("sandrine@stat.berkeley.edu")

Examples

data(swirl)
aveM <- apply(maM(swirl), 1, mean.na)
Gnames <- maGeneTable(swirl)

stat.gnames(abs(aveM), Gnames, crit=10)
stat.gnames(aveM, Gnames, crit=0.01)

Gene expression data from Swirl zebrafish cDNA microarray experiment

Description

The swirlRaw dataset consists of an object swirl of class marrayRaw , which represents pre-normalization intensity data for a batch of cDNA microarrays.

This experiment was carried out using zebrafish as a model organism to study early development in vertebrates. Swirl is a point mutant in the BMP2 gene that affects the dorsal/ventral body axis. Ventral fates such as blood are reduced, whereas dorsal structures such as somites and notochord are expanded. A goal of the Swirl experiment is to identify genes with altered expression in the swirl mutant compared to wild-type zebrafish. Two sets of dye-swap experiments were performed, for a total of four replicate hybridizations. For each of these hybridizations, target cDNA from the swirl mutant was labeled using one of the Cy3 or Cy5 dyes and the target cDNA wild-type mutant was labeled using the other dye. Target cDNA was hybridized to microarrays containing 8,448 cDNA probes, including 768 controls spots (e.g. negative, positive, and normalization controls spots). Microarrays were printed using $4 imes 4$ print-tips and are thus partitioned into a $4 imes 4$ grid matrix. Each grid consists of a $22 imes 24$ spot matrix that was printed with a single print-tip. Here, spot row and plate coordinates should coincide, as each row of spots corresponds to probe sequences from the same 384 well-plate. list()

Each of the four hybridizations produced a pair of 16-bit images, which were processed using the image analysis software package Spot . Raw images of the Cy3 and Cy5 fluorescence intensities for all fourhybridizations are available at http://fgl.lsa.berkeley.edu/Swirl/index.html .the dataset includes four output files swirl.1.spot , swirl.2.spot , swirl.3.spot , and swirl.4.spot from the Spot package. Each of these files contains 8,448 rows and 30 columns; rows correspond to spots and columns to different statistics from the Spot image analysis output. The file fish.gal is a gal file generated by the GenePix program; it contains information on individual probe sequences, such as gene names, spot ID, spot coordinates. Hybridization information for the mutant and wild-type target samples is stored in SwirlSample.txt .

Usage

data(swirl)

Data Output

Description

Writes information from a list into a text file.

Usage

write.list(x, filename = "data", append = FALSE, closefile = TRUE, outfile)

Arguments

Details

This function may be called recursively if there exists list structure within a list.

Seealso

write.table , write

Author

Jean Yee Hwa Yang

Examples

data(swirl)
test <- list(A = 1:10, B= maM(swirl)[1:10,], C=list(x=1:10, y=1:4),
D = summary(maA(swirl[,1])))
write.list(test, filename="test.txt")

Data Output

Description

Calls the function write.table with predefine argument. The entries in each line (row) are separated by tab.

Usage

write.marray(mraw, file="maRawResults.xls", val="maM", ...)

Arguments

Details

see write.table

Seealso

write.table , write.list

Author

Jean Yee Hwa Yang

Examples

data(swirl)
write.marray(swirl[1:10,])

Data Output

Description

Calls the function write.table with predefine argument. The entries in each line (row) are separated by tab.

Usage

write.xls(res, file = "test.xls", ...)

Arguments

Details

see write.table

Seealso

write.table , write.list

Author

Jean Yee Hwa Yang

Examples

data(swirl)
write.xls(maM(swirl)[1:10,], "normM.xls")