R/Rmva.R
In gnyamundanda/sma: Statistical Microarray Analysis
###########################################################################
# Statistics for Microarray Analysis
# MA plots
#
# Date : March 19, 2001
#
# History:
# May 17, 2001: Incorporate postscript fix from kadowaki@pharmadesign.co.jp
# March 19, 2001: Some of the plot functions from Rarray.R.
#
#
# Authors: Sandrine Dudoit and Yee Hwa (Jean) Yang.
##########################################################################
########################################################################/**
# \name{plot.mva}
#
# \alias{plot.mva}
#
# \title{M vs. A Plot}
#
# \description{
# For a single slide, this function produces a scatter plot of log
# intensity ratios \eqn{M = log_2(R/G)} versus average log intensities
# \eqn{A = log_2 \sqrt{RG}}{A = log_2(R*G)/2}, where R and G represent
# the fluorescence intensities in the red and green channels
# respectively.
# }
#
# \usage{
# plot.mva(RG, layout, norm="p", pout=T, image.id=1, extra.type="tci",
# crit1=0.025,crit2=crit1, nclass=10, labs=NULL, plot.type="n",
# col.ex=NULL, ...)
# }
#
# \arguments{
# \item{RG}{A list with at least 4 elements. Each element of the list
# being a matrix with p rows for p genes and n columns for n slides.
# The first element 'R' contains the raw red intensities,
# the second element 'G' contains the raw green intensities,
# the third element 'Rb' contains the background red intensities and
# the 4th element 'Gb' contains the background green intensities.
# This data structure can be generated by an interactive function
# \code{\link{init.data}}.}
#
# \item{layout}{a list specifying the dimensions of the spot matrix
# and the grid matrix. This can be generated by calling
# \code{\link{init.grid}}.}
#
# \item{norm}{character string, one of "n", "m", "l", "p" or "s". This
# argument specifies the type of normalization method to be
# performed: "n" no normalization between the 2 channels; "m"
# \code{\link{median}} normalization, which sets the median of log
# intensity ratios to zero; "l" global \code{\link{lowess}}
# normalization; "p" print-tip group lowess normalization and "s"
# scaled print-tip group lowess normalization.}
#
# \item{pout}{if TRUE, an M vs. A plot will be produced. Otherwise, the
# function returns the normalized log intensity ratios M and the mean
# log intensities A for each gene.}
#
# \item{image.id}{integer value; the index of the slide which is considered.}
#
# \item{extra.type}{a character string, one of "t", "p", "tci","pci" or
# "lci". This argument specifies the type of plot to be drawn. The
# possible types are: \cr
# * "t" for text, \cr
# * "p" for points, \cr
# * "tci" for highlighting a certain proportion of extreme `M' values
# by text,\cr
# * "pci" for highlighting a certain proportion of extreme `M' values
# by points,\cr
# * "lci" for including 2 intensity dependent lines where a
# prespecified proportion of points have more extreme `M' values.
# }
# \item{crit1}{The number of points to be highlighted on the M vs A
# plot. If crit1 < 1, the crit1*100\% spots with the smallest M
# values will be highlighted. If crit1 >= 1, the crit spots
# with the smallest M values are highlighted.}
# \item{crit2}{Similar to "crit1". If crit2 < 1, the crit2*100\% spots
# with the largest M values will be highlighted. If crit2 >= 1, the
# crit2 spots with the smallest M values are highlighted.}
# \item{nclass}{A single number giving the approximate number of
# intensity dependent groups to consider.}
# \item{labs}{one or more character strings or expressions specifying the
# text to be written. If this string is not specified, by
# default the index of the vector `M' will be used.}
# \item{plot.type}{a character string, this argument is either "n", "r"
# or "b". The different number of plots to be included are:\cr
# * "n" for normalised M vs A plot, \cr
# * "r" for unnormalised M vs A plot, and \cr
# * "b" both unnormalised and normalised M vs A plots.
# }
# \item{col.ex}{The colour used for the highlighting extreme points,
# lines or text.}
# \item{\dots}{graphical parameters may also be supplied as arguments to the
# function (see \code{\link{par}}). }
# }
#
# \value{A plot is created on the current graphics device. The top
# plot is based on unnormalized log ratios and the bottom plot is
# based on normalized log ratios.}
#
# \details{M vs. A plots tend to be more revealing than their log R
# vs. log G counterparts in terms of identifying spot artifacts and
# detecting intensity dependent patterns in the log ratios. They are
# also very useful for normalization.}
#
# \references{S. Dudoit, Y. H. Yang, M. J. Callow, and T. P. Speed. Statistical
# methods for identifying differentially expressed genes in replicated
# cDNA microarray experiments (Statistics, UC Berkeley, Tech Report \#
# 578).}
#
# \author{
# Yee Hwa Yang, \email{yeehwa@stat.berkeley.edu} \cr
# Sandrine Dudoit, \email{sandrine@stat.berkeley.edu} \cr
# Natalie Roberts, \email{nroberts@wehi.edu.au}
# }
#
# \seealso{\code{\link{ma.func}}, \code{\link{plot.smooth.line}},
# \code{\link{stat.ma}}, \code{\link{lowess}}, \code{\link{plot}}.}
#
# \examples{
# data(MouseArray)
# # mouse.setup <- init.grid()
# # mouse.data <- init.data() ## see \emph{init.data}
# mouse.lratio <- stat.ma(mouse.data, mouse.setup)
#
# ## Look at the normalized second data sets in the list using points to
# ## highlight large positive or large negative ratios.
# plot.mva(mouse.data, mouse.setup, norm="l", 2, extra.type="pci",
# plot.type="n")
#
# ## Look at the both unnormalized and normalized first data sets in the
# ## list using text to highlight large positive or negative ratios.
# ## plot.mva(mouse.data, mouse.setup, norm="l", 2, extra.type="tci", plot.type="b")
# }
#
# \keyword{microarray}
#
#*/########################################################################
plot.mva <- function(x, layout, norm="p", pout=TRUE, image.id=1, extra.type="tci", crit1=0.025,crit2=crit1, nclass=10, labs=NULL, plot.type="n", col.ex=NULL, pch=".", ...)
{
# RG <- x
ma.func(R = x$R[,image.id], G=x$G[,image.id], Rb=x$Rb[,image.id], Gb = x$Gb[,image.id], layout=layout, norm=norm, pout=pout, extra.type=extra.type, crit1=crit1, crit2=crit2, nclass=nclass, labs=labs, plot.type=plot.type, col.ex=col.ex, pch=pch,...)
}
########################################################################/**
# \name{plot.smooth.line}
#
# \alias{plot.smooth.line}
#
# \title{Adding Lowess Lines to Current Plot}
#
# \description{
# This function adds a \code{\link{lowess}} line to the current
# plot. The type of line can be specified as well as other
# parameters.}
#
# \usage{
# plot.smooth.line(A, M, f=0.1, ...)
# }
#
# \arguments{
# \item{A}{a vector giving the x-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of
# average log intensities.}
#
# \item{M}{a vector giving the y-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of
# log intensity ratios.}
#
# \item{f}{the smoother span. This gives the proportion of points in the
# plot which influence the smoothness at each value. Larger
# values give greater smoothness. }
#
# \item{\dots}{graphical parameters may also be supplied as arguments
# to the function (see \code{\link{par}}).}
# }
#
# \value{
# lines are added to the current plot.
# }
#
# \note{An M vs A plot must be constructed \bold{prior} to the execution of this function.}
#
# \references{ Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey,
# P. A. (1983). Graphical Methods for Data Analysis. Wadsworth, Belmont,
# California. }
#
# \seealso{ \code{\link{plot.mva}}, \code{\link{stat.ma}},
# \code{\link{lines}}, \code{\link{lowess}}, \code{\link{smooth}}.
# }
#
# \examples{
# data(MouseArray)
# ## mouse.setup <- init.grid()
# ## mouse.data <- init.data()
#
# plot.mva(mouse.data, mouse.setup)
# plot.smooth.line(mouse.lratio$A, mouse.lratio$M)
# }
#
# \keyword{microarray, lowess.}
#*/########################################################################
plot.smooth.line <- function(x, M, f = 0.1, ...)
{
# A <- x
ind <- !(is.na(x) | is.na(M) | is.infinite(x) | is.infinite(M))
#lines(lowess(A[ind], M[ind], f = f), ...)
lines(approx(lowess(x[ind], M[ind], f = f)), ...)
}
########################################################################/**
# \name{plot.confband.lines}
#
# \alias{plot.confband.lines}
#
# \title{Adding Lines Satisfying a Confidence Criterion to the Current M
# vs A Plot}
#
# \description{
# This function adds 2 lines outlining the pointwise (intensity
# dependent) confidence band on the M vs A plot. The lines are drawn
# such that a prespecified proportion of points are outside the 2
# confidence curves.
# The type of line may be specified as well as other parameters.}
#
# \usage{
# plot.confband.line(A, M, crit1=0.025, crit2=crit1, nclass=10, ...)
# }
#
# \arguments{
# \item{A}{a vector giving the x-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of
# average log intensities.}
#
# \item{M}{a vector giving the y-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of log
# intensity ratios.}
#
# \item{crit1}{The proportion of points less than the lower confidence
# curve. This takes a decimal value between 0 and 1. }
# \item{crit2}{The proportion of points greater than the upper confidence
# curve. By default, this has the same value as "crit1".}
# \item{nclass}{A single number giving the approximate number of
# intensity depedent groups to consider.}
# \item{\dots}{graphical parameters may also be supplied as arguments
# to the function (see \code{\link{par}}).}
# }
#
# \value{
# Lines are added to the current plot.
# }
#
# \note{
# An M vs A plot must be constructed \bold{prior} to the execution
# of this function.}
#
# \seealso{ \code{\link{plot.mva}}, \code{\link{stat.ma}},
# \code{\link{lines}}, \code{\link{matlines}},
# \code{\link{plot.confband.text}}, \code{\link{plot.confband.points}} .
# }
#
# \examples{data(MouseArray)
# ## mouse.setup <- init.grid
# ## mouse.data <- init.data
#
# ## To display an M vs A plot of the data
# plot.mva(mouse.data, mouse.setup)
#
# ## Calculate M and A values
# mouse.lratio <- stat.ma(mouse.data, mouse.setup)
#
# ## To add default upper and lower confidence curves line to the M vs A plot
# plot.confband.lines(mouse.lratio$A, mouse.lratio$M)
# }
#
# \keyword{microarray, point-wise confidence band.}
#*/########################################################################
plot.confband.lines<-function (x, M, crit1=0.025, crit2=crit1, nclass=10, ...)
{
# A <- x
if (crit1 >= 1) crit1 <- crit1 / length.na(M)
if (crit2 >= 1) crit2 <- crit2 / length.na(M)
cutoff<-NULL
Abin <- quantile(x, probs=seq(0, nclass, 1)/nclass, na.rm=TRUE)
for(i in (1:nclass) ){
tmpind<-(Abin[i]<=x)&(x<Abin[i+1])
xtmp <- M
xtmp[!tmpind]<-NA
n1<-sum.na(tmpind)
cutoff <- rbind(cutoff,quantile.na(xtmp, probs=c(crit1, (1-crit2))))
}
matlines(Abin[-1],cutoff, ... )
}
########################################################################/**
# \name{plot.confband.points}
#
# \alias{plot.confband.points}
#
# \title{Highlights a Set of Points on the Current M vs A Plot}
#
# \description{
# This function highlights a prespecified proportion of extreme points
# on the M vs A plots.
# }
#
# \usage{
# plot.confband.points(A, M, crit1=0.025, crit2=crit1, nclass=10, ...)
# }
#
# \arguments{
# \item{A}{a vector giving the x-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of
# average log intensities.}
#
# \item{M}{a vector giving the y-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of log
# intensity ratios.}
#
# \item{crit1}{The number of points to be highlighted on the M vs A plot.
# If crit1 < 1, the crit1*100\% spots with the smallest M values
# will be highlighted. If crit1 >= 1, the crit spots with the
# smallest M values are highlighted.}
# \item{crit2}{Similar to "crit1". If crit2 < 1, the crit2*100\%
# spots with the largest M values will be highlighted. If crit2 >= 1,
# the crit2 spots with the smallest M values are highlighted.}
# \item{nclass}{A single number giving the approximate number of
# intensity depedent groups to consider.}
# \item{\dots}{graphical parameters may also be supplied as arguments
# to the function (see \code{\link{par}}).}
# }
#
# \value{
# Points are added to the current plot.
# }
#
#
# \seealso{\code{\link{plot.mva}}, \code{\link{stat.ma}},
# \code{\link{lines}}, \code{\link{matlines}},
# \code{\link{plot.confband.text}}, \code{\link{plot.confband.lines}} .
# }
#
# \note{An M vs A plot must be constructed \bold{prior} to the
# execution of this function.}
#
# \examples{data(MouseArray)
# ## mouse.setup <- init.grid()
# ## mouse.data <- init.data()
#
# plot.mva(mouse.data, mouse.setup) ## an M vs A plot
#
# mouse.lratio <- stat.ma(mouse.data, mouse.setup)
#
# plot.confband.points(mouse.lratio$A, mouse.lratio$M)
#
# ## 2.5\% of the spots with the smallest and largest M values are
# ## highlighted on the M vs A plot.
# }
#
# \keyword{microarray, point-wise confidence band.}
#
#*/########################################################################
plot.confband.points<-function (x, M, crit1=0.025, crit2=crit1, nclass=10, col.ex=NULL, ...)
{
## quantile.na removes infinite too...quantile(x, na.rm=F) doesn't.
# A <- x
if (crit1 >= 1) crit1 <- crit1 / length.na(M)
if (crit2 >= 1) crit2 <- crit2 / length.na(M)
txtA<-(rep(FALSE,length(x)))
Abin <- quantile(x, probs=seq(0, nclass, 1)/nclass, na.rm=TRUE)
for(i in 1:nclass){
tmpind<-(Abin[i]<=x)&(x<Abin[i+1])
xtmp <- M
xtmp[!tmpind]<-NA
n1<-sum.na(tmpind)
cutoff <- quantile.na(xtmp, probs=c(crit1, (1-crit2)))
vals<- ((xtmp < cutoff[1]) | (xtmp > cutoff[2]))
txtA[vals]<-TRUE
}
points(x[txtA],M[txtA],pch=18, col=col.ex,...)
}
########################################################################/**
# \name{plot.confband.text}
#
# \alias{plot.confband.text}
#
# \title{Add Selected Text to an M vs A Plot}
#
# \description{`text' draws the strings given in the vector `labs' at the
# coordinates given by `M' and `A'}
#
# \usage{
# plot.confband.text(A, M, crit1=0.025, crit2=crit1, nclass=10,
# labs=NULL, output=F, ...)
# }
#
# \arguments{
# \item{A}{a vector giving the x-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of
# average log intensities.}
#
# \item{M}{a vector giving the y-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of log
# intensity ratios.}
#
# \item{crit1}{The number of points to be highlighted on the M vs A plot.
# If crit1 < 1, the crit1*100\% spots with the smallest M values
# will be highlighted. If crit1 >= 1, the crit spots with the
# smallest M values are highlighted.}
# \item{crit2}{Similar to "crit1". If crit2 < 1, the crit2*100\%
# spots with the largest M values will be highlighted. If crit2 >= 1,
# the crit2 spots with the largest M values are highlighted.}
# \item{nclass}{A single number giving the approximate number of
# intensity depedent groups to consider.}
# \item{labs}{ one or more character strings or expressions specifying the
# text to be written. If this string is not specified, by
# default the index of the vector `M' will be used.}
# \item{output}{logical, defaulting to `FALSE'. If `TRUE' a vector
# containning the index to the vector `M' that are
# highlighted.}
# \item{\dots}{graphical parameters may also be supplied as arguments
# to the function (see \code{\link{par}}).}
# }
#
# \note{An M vs A plot must be constructed \bold{prior} to the execution of this function.}
#
# \examples{data(MouseArray)
# ## mouse.setup <- init.grid()
# ## mouse.data <- init.data()
#
# plot.mva(mouse.data, mouse.setup) ## an M vs A plot
#
# mouse.lratio <- stat.ma(mouse.data, mouse.setup)
#
# plot.confband.text(mouse.lratio$A, mouse.lratio$M)
# ## 2.5\% of the spots with the largest and smallest M values are
# ## highlighted on the M vs A plot, and each spot is assigned the
# ## default label of its corresponding index value.
# }
#
# \seealso{ \code{\link{plot.mva}}, \code{\link{stat.ma}},
# \code{\link{lines}}, \code{\link{matlines}},
# \code{\link{plot.confband.lines}}, \code{\link{plot.confband.points}} .
# }
#
# \keyword{microarray, point-wise confidence band.}
#*/########################################################################
plot.confband.text <-
function (x, M, crit1=0.025, crit2=crit1, nclass=10, labs=NULL, output=FALSE, ...)
{
# A <- x
if (crit1 >= 1) crit1 <- crit1 / length.na(M)
if (crit2 >= 1) crit2 <- crit2 / length.na(M)
txtA<-(rep(FALSE,length(x)))
Abin <- quantile.na(x, probs=seq(0, nclass, 1)/nclass)
for(i in 1:nclass){
tmpind<-(Abin[i]<=x)&(x<Abin[i+1])
xtmp <- M
xtmp[!tmpind]<-NA
n1<-sum.na(tmpind)
cutoff <- quantile.na(xtmp, probs=c(crit1, (1-crit2)))
vals<- ((xtmp < cutoff[1]) | (xtmp > cutoff[2]))
txtA[vals]<-TRUE
}
if(is.null(labs)) labs <- as.character(1:length(M))
text(x[txtA],M[txtA],labels=labs[txtA], ...)
if(output)
res <- txtA
else res <- NULL
res
}
##########################################################################
# End of file
##########################################################################
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###########################################################################
# Statistics for Microarray Analysis
# MA plots
#
# Date : March 19, 2001
#
# History:
# May 17, 2001: Incorporate postscript fix from kadowaki@pharmadesign.co.jp
# March 19, 2001: Some of the plot functions from Rarray.R.
#
#
# Authors: Sandrine Dudoit and Yee Hwa (Jean) Yang.
##########################################################################
########################################################################/**
# \name{plot.mva}
#
# \alias{plot.mva}
#
# \title{M vs. A Plot}
#
# \description{
# For a single slide, this function produces a scatter plot of log
# intensity ratios \eqn{M = log_2(R/G)} versus average log intensities
# \eqn{A = log_2 \sqrt{RG}}{A = log_2(R*G)/2}, where R and G represent
# the fluorescence intensities in the red and green channels
# respectively.
# }
#
# \usage{
# plot.mva(RG, layout, norm="p", pout=T, image.id=1, extra.type="tci",
# crit1=0.025,crit2=crit1, nclass=10, labs=NULL, plot.type="n",
# col.ex=NULL, ...)
# }
#
# \arguments{
# \item{RG}{A list with at least 4 elements. Each element of the list
# being a matrix with p rows for p genes and n columns for n slides.
# The first element 'R' contains the raw red intensities,
# the second element 'G' contains the raw green intensities,
# the third element 'Rb' contains the background red intensities and
# the 4th element 'Gb' contains the background green intensities.
# This data structure can be generated by an interactive function
# \code{\link{init.data}}.}
#
# \item{layout}{a list specifying the dimensions of the spot matrix
# and the grid matrix. This can be generated by calling
# \code{\link{init.grid}}.}
#
# \item{norm}{character string, one of "n", "m", "l", "p" or "s". This
# argument specifies the type of normalization method to be
# performed: "n" no normalization between the 2 channels; "m"
# \code{\link{median}} normalization, which sets the median of log
# intensity ratios to zero; "l" global \code{\link{lowess}}
# normalization; "p" print-tip group lowess normalization and "s"
# scaled print-tip group lowess normalization.}
#
# \item{pout}{if TRUE, an M vs. A plot will be produced. Otherwise, the
# function returns the normalized log intensity ratios M and the mean
# log intensities A for each gene.}
#
# \item{image.id}{integer value; the index of the slide which is considered.}
#
# \item{extra.type}{a character string, one of "t", "p", "tci","pci" or
# "lci". This argument specifies the type of plot to be drawn. The
# possible types are: \cr
# * "t" for text, \cr
# * "p" for points, \cr
# * "tci" for highlighting a certain proportion of extreme `M' values
# by text,\cr
# * "pci" for highlighting a certain proportion of extreme `M' values
# by points,\cr
# * "lci" for including 2 intensity dependent lines where a
# prespecified proportion of points have more extreme `M' values.
# }
# \item{crit1}{The number of points to be highlighted on the M vs A
# plot. If crit1 < 1, the crit1*100\% spots with the smallest M
# values will be highlighted. If crit1 >= 1, the crit spots
# with the smallest M values are highlighted.}
# \item{crit2}{Similar to "crit1". If crit2 < 1, the crit2*100\% spots
# with the largest M values will be highlighted. If crit2 >= 1, the
# crit2 spots with the smallest M values are highlighted.}
# \item{nclass}{A single number giving the approximate number of
# intensity dependent groups to consider.}
# \item{labs}{one or more character strings or expressions specifying the
# text to be written. If this string is not specified, by
# default the index of the vector `M' will be used.}
# \item{plot.type}{a character string, this argument is either "n", "r"
# or "b". The different number of plots to be included are:\cr
# * "n" for normalised M vs A plot, \cr
# * "r" for unnormalised M vs A plot, and \cr
# * "b" both unnormalised and normalised M vs A plots.
# }
# \item{col.ex}{The colour used for the highlighting extreme points,
# lines or text.}
# \item{\dots}{graphical parameters may also be supplied as arguments to the
# function (see \code{\link{par}}). }
# }
#
# \value{A plot is created on the current graphics device. The top
# plot is based on unnormalized log ratios and the bottom plot is
# based on normalized log ratios.}
#
# \details{M vs. A plots tend to be more revealing than their log R
# vs. log G counterparts in terms of identifying spot artifacts and
# detecting intensity dependent patterns in the log ratios. They are
# also very useful for normalization.}
#
# \references{S. Dudoit, Y. H. Yang, M. J. Callow, and T. P. Speed. Statistical
# methods for identifying differentially expressed genes in replicated
# cDNA microarray experiments (Statistics, UC Berkeley, Tech Report \#
# 578).}
#
# \author{
# Yee Hwa Yang, \email{yeehwa@stat.berkeley.edu} \cr
# Sandrine Dudoit, \email{sandrine@stat.berkeley.edu} \cr
# Natalie Roberts, \email{nroberts@wehi.edu.au}
# }
#
# \seealso{\code{\link{ma.func}}, \code{\link{plot.smooth.line}},
# \code{\link{stat.ma}}, \code{\link{lowess}}, \code{\link{plot}}.}
#
# \examples{
# data(MouseArray)
# # mouse.setup <- init.grid()
# # mouse.data <- init.data() ## see \emph{init.data}
# mouse.lratio <- stat.ma(mouse.data, mouse.setup)
#
# ## Look at the normalized second data sets in the list using points to
# ## highlight large positive or large negative ratios.
# plot.mva(mouse.data, mouse.setup, norm="l", 2, extra.type="pci",
# plot.type="n")
#
# ## Look at the both unnormalized and normalized first data sets in the
# ## list using text to highlight large positive or negative ratios.
# ## plot.mva(mouse.data, mouse.setup, norm="l", 2, extra.type="tci", plot.type="b")
# }
#
# \keyword{microarray}
#
#*/########################################################################
plot.mva <- function(x, layout, norm="p", pout=TRUE, image.id=1, extra.type="tci", crit1=0.025,crit2=crit1, nclass=10, labs=NULL, plot.type="n", col.ex=NULL, pch=".", ...)
{
# RG <- x
ma.func(R = x$R[,image.id], G=x$G[,image.id], Rb=x$Rb[,image.id], Gb = x$Gb[,image.id], layout=layout, norm=norm, pout=pout, extra.type=extra.type, crit1=crit1, crit2=crit2, nclass=nclass, labs=labs, plot.type=plot.type, col.ex=col.ex, pch=pch,...)
}
########################################################################/**
# \name{plot.smooth.line}
#
# \alias{plot.smooth.line}
#
# \title{Adding Lowess Lines to Current Plot}
#
# \description{
# This function adds a \code{\link{lowess}} line to the current
# plot. The type of line can be specified as well as other
# parameters.}
#
# \usage{
# plot.smooth.line(A, M, f=0.1, ...)
# }
#
# \arguments{
# \item{A}{a vector giving the x-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of
# average log intensities.}
#
# \item{M}{a vector giving the y-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of
# log intensity ratios.}
#
# \item{f}{the smoother span. This gives the proportion of points in the
# plot which influence the smoothness at each value. Larger
# values give greater smoothness. }
#
# \item{\dots}{graphical parameters may also be supplied as arguments
# to the function (see \code{\link{par}}).}
# }
#
# \value{
# lines are added to the current plot.
# }
#
# \note{An M vs A plot must be constructed \bold{prior} to the execution of this function.}
#
# \references{ Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey,
# P. A. (1983). Graphical Methods for Data Analysis. Wadsworth, Belmont,
# California. }
#
# \seealso{ \code{\link{plot.mva}}, \code{\link{stat.ma}},
# \code{\link{lines}}, \code{\link{lowess}}, \code{\link{smooth}}.
# }
#
# \examples{
# data(MouseArray)
# ## mouse.setup <- init.grid()
# ## mouse.data <- init.data()
#
# plot.mva(mouse.data, mouse.setup)
# plot.smooth.line(mouse.lratio$A, mouse.lratio$M)
# }
#
# \keyword{microarray, lowess.}
#*/########################################################################
plot.smooth.line <- function(x, M, f = 0.1, ...)
{
# A <- x
ind <- !(is.na(x) | is.na(M) | is.infinite(x) | is.infinite(M))
#lines(lowess(A[ind], M[ind], f = f), ...)
lines(approx(lowess(x[ind], M[ind], f = f)), ...)
}
########################################################################/**
# \name{plot.confband.lines}
#
# \alias{plot.confband.lines}
#
# \title{Adding Lines Satisfying a Confidence Criterion to the Current M
# vs A Plot}
#
# \description{
# This function adds 2 lines outlining the pointwise (intensity
# dependent) confidence band on the M vs A plot. The lines are drawn
# such that a prespecified proportion of points are outside the 2
# confidence curves.
# The type of line may be specified as well as other parameters.}
#
# \usage{
# plot.confband.line(A, M, crit1=0.025, crit2=crit1, nclass=10, ...)
# }
#
# \arguments{
# \item{A}{a vector giving the x-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of
# average log intensities.}
#
# \item{M}{a vector giving the y-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of log
# intensity ratios.}
#
# \item{crit1}{The proportion of points less than the lower confidence
# curve. This takes a decimal value between 0 and 1. }
# \item{crit2}{The proportion of points greater than the upper confidence
# curve. By default, this has the same value as "crit1".}
# \item{nclass}{A single number giving the approximate number of
# intensity depedent groups to consider.}
# \item{\dots}{graphical parameters may also be supplied as arguments
# to the function (see \code{\link{par}}).}
# }
#
# \value{
# Lines are added to the current plot.
# }
#
# \note{
# An M vs A plot must be constructed \bold{prior} to the execution
# of this function.}
#
# \seealso{ \code{\link{plot.mva}}, \code{\link{stat.ma}},
# \code{\link{lines}}, \code{\link{matlines}},
# \code{\link{plot.confband.text}}, \code{\link{plot.confband.points}} .
# }
#
# \examples{data(MouseArray)
# ## mouse.setup <- init.grid
# ## mouse.data <- init.data
#
# ## To display an M vs A plot of the data
# plot.mva(mouse.data, mouse.setup)
#
# ## Calculate M and A values
# mouse.lratio <- stat.ma(mouse.data, mouse.setup)
#
# ## To add default upper and lower confidence curves line to the M vs A plot
# plot.confband.lines(mouse.lratio$A, mouse.lratio$M)
# }
#
# \keyword{microarray, point-wise confidence band.}
#*/########################################################################
plot.confband.lines<-function (x, M, crit1=0.025, crit2=crit1, nclass=10, ...)
{
# A <- x
if (crit1 >= 1) crit1 <- crit1 / length.na(M)
if (crit2 >= 1) crit2 <- crit2 / length.na(M)
cutoff<-NULL
Abin <- quantile(x, probs=seq(0, nclass, 1)/nclass, na.rm=TRUE)
for(i in (1:nclass) ){
tmpind<-(Abin[i]<=x)&(x<Abin[i+1])
xtmp <- M
xtmp[!tmpind]<-NA
n1<-sum.na(tmpind)
cutoff <- rbind(cutoff,quantile.na(xtmp, probs=c(crit1, (1-crit2))))
}
matlines(Abin[-1],cutoff, ... )
}
########################################################################/**
# \name{plot.confband.points}
#
# \alias{plot.confband.points}
#
# \title{Highlights a Set of Points on the Current M vs A Plot}
#
# \description{
# This function highlights a prespecified proportion of extreme points
# on the M vs A plots.
# }
#
# \usage{
# plot.confband.points(A, M, crit1=0.025, crit2=crit1, nclass=10, ...)
# }
#
# \arguments{
# \item{A}{a vector giving the x-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of
# average log intensities.}
#
# \item{M}{a vector giving the y-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of log
# intensity ratios.}
#
# \item{crit1}{The number of points to be highlighted on the M vs A plot.
# If crit1 < 1, the crit1*100\% spots with the smallest M values
# will be highlighted. If crit1 >= 1, the crit spots with the
# smallest M values are highlighted.}
# \item{crit2}{Similar to "crit1". If crit2 < 1, the crit2*100\%
# spots with the largest M values will be highlighted. If crit2 >= 1,
# the crit2 spots with the smallest M values are highlighted.}
# \item{nclass}{A single number giving the approximate number of
# intensity depedent groups to consider.}
# \item{\dots}{graphical parameters may also be supplied as arguments
# to the function (see \code{\link{par}}).}
# }
#
# \value{
# Points are added to the current plot.
# }
#
#
# \seealso{\code{\link{plot.mva}}, \code{\link{stat.ma}},
# \code{\link{lines}}, \code{\link{matlines}},
# \code{\link{plot.confband.text}}, \code{\link{plot.confband.lines}} .
# }
#
# \note{An M vs A plot must be constructed \bold{prior} to the
# execution of this function.}
#
# \examples{data(MouseArray)
# ## mouse.setup <- init.grid()
# ## mouse.data <- init.data()
#
# plot.mva(mouse.data, mouse.setup) ## an M vs A plot
#
# mouse.lratio <- stat.ma(mouse.data, mouse.setup)
#
# plot.confband.points(mouse.lratio$A, mouse.lratio$M)
#
# ## 2.5\% of the spots with the smallest and largest M values are
# ## highlighted on the M vs A plot.
# }
#
# \keyword{microarray, point-wise confidence band.}
#
#*/########################################################################
plot.confband.points<-function (x, M, crit1=0.025, crit2=crit1, nclass=10, col.ex=NULL, ...)
{
## quantile.na removes infinite too...quantile(x, na.rm=F) doesn't.
# A <- x
if (crit1 >= 1) crit1 <- crit1 / length.na(M)
if (crit2 >= 1) crit2 <- crit2 / length.na(M)
txtA<-(rep(FALSE,length(x)))
Abin <- quantile(x, probs=seq(0, nclass, 1)/nclass, na.rm=TRUE)
for(i in 1:nclass){
tmpind<-(Abin[i]<=x)&(x<Abin[i+1])
xtmp <- M
xtmp[!tmpind]<-NA
n1<-sum.na(tmpind)
cutoff <- quantile.na(xtmp, probs=c(crit1, (1-crit2)))
vals<- ((xtmp < cutoff[1]) | (xtmp > cutoff[2]))
txtA[vals]<-TRUE
}
points(x[txtA],M[txtA],pch=18, col=col.ex,...)
}
########################################################################/**
# \name{plot.confband.text}
#
# \alias{plot.confband.text}
#
# \title{Add Selected Text to an M vs A Plot}
#
# \description{`text' draws the strings given in the vector `labs' at the
# coordinates given by `M' and `A'}
#
# \usage{
# plot.confband.text(A, M, crit1=0.025, crit2=crit1, nclass=10,
# labs=NULL, output=F, ...)
# }
#
# \arguments{
# \item{A}{a vector giving the x-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of
# average log intensities.}
#
# \item{M}{a vector giving the y-coordinates of the points in the scatter
# plot. In the microarray context, this could be a vector of log
# intensity ratios.}
#
# \item{crit1}{The number of points to be highlighted on the M vs A plot.
# If crit1 < 1, the crit1*100\% spots with the smallest M values
# will be highlighted. If crit1 >= 1, the crit spots with the
# smallest M values are highlighted.}
# \item{crit2}{Similar to "crit1". If crit2 < 1, the crit2*100\%
# spots with the largest M values will be highlighted. If crit2 >= 1,
# the crit2 spots with the largest M values are highlighted.}
# \item{nclass}{A single number giving the approximate number of
# intensity depedent groups to consider.}
# \item{labs}{ one or more character strings or expressions specifying the
# text to be written. If this string is not specified, by
# default the index of the vector `M' will be used.}
# \item{output}{logical, defaulting to `FALSE'. If `TRUE' a vector
# containning the index to the vector `M' that are
# highlighted.}
# \item{\dots}{graphical parameters may also be supplied as arguments
# to the function (see \code{\link{par}}).}
# }
#
# \note{An M vs A plot must be constructed \bold{prior} to the execution of this function.}
#
# \examples{data(MouseArray)
# ## mouse.setup <- init.grid()
# ## mouse.data <- init.data()
#
# plot.mva(mouse.data, mouse.setup) ## an M vs A plot
#
# mouse.lratio <- stat.ma(mouse.data, mouse.setup)
#
# plot.confband.text(mouse.lratio$A, mouse.lratio$M)
# ## 2.5\% of the spots with the largest and smallest M values are
# ## highlighted on the M vs A plot, and each spot is assigned the
# ## default label of its corresponding index value.
# }
#
# \seealso{ \code{\link{plot.mva}}, \code{\link{stat.ma}},
# \code{\link{lines}}, \code{\link{matlines}},
# \code{\link{plot.confband.lines}}, \code{\link{plot.confband.points}} .
# }
#
# \keyword{microarray, point-wise confidence band.}
#*/########################################################################
plot.confband.text <-
function (x, M, crit1=0.025, crit2=crit1, nclass=10, labs=NULL, output=FALSE, ...)
{
# A <- x
if (crit1 >= 1) crit1 <- crit1 / length.na(M)
if (crit2 >= 1) crit2 <- crit2 / length.na(M)
txtA<-(rep(FALSE,length(x)))
Abin <- quantile.na(x, probs=seq(0, nclass, 1)/nclass)
for(i in 1:nclass){
tmpind<-(Abin[i]<=x)&(x<Abin[i+1])
xtmp <- M
xtmp[!tmpind]<-NA
n1<-sum.na(tmpind)
cutoff <- quantile.na(xtmp, probs=c(crit1, (1-crit2)))
vals<- ((xtmp < cutoff[1]) | (xtmp > cutoff[2]))
txtA[vals]<-TRUE
}
if(is.null(labs)) labs <- as.character(1:length(M))
text(x[txtA],M[txtA],labels=labs[txtA], ...)
if(output)
res <- txtA
else res <- NULL
res
}
##########################################################################
# End of file
##########################################################################
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