Spotted microarray M and A values; differentially expressed controls

Description

Values, derived from the data in coralRG, are the subset of the M and A values, after normalisation within and between arrays, for the differentially expressed controls

Usage

data(DEnma)

Format

The format is: Formal class 'MAList' [package "limma"] Can be accessed as a list, with named elements "targets", "source", "genes", "printer", "M" and "A"

Source

Centre for the Molecular Genomics of Genetic Development, ANU

Examples

data(DEnma)

Spotted microarray red and green foreground and background values

Description

Unnormalised red and green values, and corresponding background values. Further information is in the data frame coralTargets

Usage

data(coralRG)

Format

The format is: Formal class 'RGList' [package "limma"] Can be accessed as a list, with named elements "R" ,"G" ,"Rb" ,"Gb" ,"targets", "source" ,"genes" and "printer"

Source

Lauretto Grasso and Eldon Ball, Molecular Genetics and Evolution Group, Research School of Biological Sciences, Australian National University.

Examples

data(coralRG)

Targets file to accompany spotted expression array data

Description

Targets file, in the form expected by limma, to accompany the expression array data in coralRg

Usage

data(coralTargets)

Format

A data frame with 6 observations on the following 4 variables.

SlideNumber

a character vector

FileName

Names of files that hold spotted array data

Cy3

Treatment assigned to Cy3 ("red")

Cy5

Treatment assigned to Cy5 ("green")

Examples

data(coralTargets)
## maybe str(coralTargets) ; plot(coralTargets) ...

Image plot of spotted expression array data

Description

Creates an image of graduated colors that represent the values of a statistic for each spot on a spotted microarray. By default, the only the 5 shown. The initial version was based on plot.spatial in the sma package.

Usage

imgplot(z = DAAGbio::coralRG$R[, 1], layout = DAAGbio::coralRG$printer, crit1 = 0.05,
crit2 = crit1, key.side=2,
lohi.colors = c("#9E0142", "#D53E4F", "#F46D43", "#FDAE61", "#ABDDA4",
"#66C2A5", "#3288BD", "#5E4FA2"), nacolor = "#FFFF00",
boxplot.side = 1, split = "quantiles")

Arguments

Value

A plot is created on the current graphics device

Author(s)

J. H. Maindonald

Examples

## The function is currently defined as
function (z=DAAGbio::coralRG$R[,1], layout=cDAAGbio::oralRG$printer, crit1 = 0.05,
            crit2 = crit1, key.side=2,
            lohi.colors=c("#9E0142","#D53E4F","#F46D43","#FDAE61",
              "#ABDDA4","#66C2A5","#3288BD","#5E4FA2"),
            nacolor="#FFFF00", boxplot.side=1, split="quantiles")
{
  "block2matrix" <-
    function(z, sr=3, sc=2, gr=2, gc=2){
      ## Assumes that values in the vector z are  in row major
      ## order within blocks of dimension sr x sc, with blocks
      ## in row major order within a gr x gc array of grids.
      ## Elements in the vector that is returned are in row
      ## major order wrt the sr*gr x sc*gc matrix of values on
      ## the slide. (It is given the dimensions of a matrix.)
      xy <- array(z, dim=c(sc, sr, gc, gr))
      xy <- aperm(xy, c(1,3,2,4))
      dim(xy) <- c(sc*gc, gr*sr)
      xy}
  quantile.na <- function (z, ...)
    {
      tmp <- !(is.na(z) | is.infinite(z))
      quantile(z[tmp], ...)
    }
  length.na <- function (z, ...)
    {
      tmp <- !(is.na(z) | is.infinite(z))
      length(z[tmp], ...)
    }
    if(is.matrix(z))warning("z is a matrix, You probably want a column vector")
  bplot <- function(z, boxplot.side=1){
    xrange <- range(z,na.rm=TRUE)
    iqr <- diff(quantile(xrange, c(.25,.75)))
    bwex <- diff(xrange)/(3*iqr)
    xhi <- max(z,na.rm=TRUE)
    xusr <- par()$usr[c(1:2)]
    xpos=pretty(z[!is.na(z)], n=5)
    z <- xusr[1]+(z-xrange[1])*diff(xusr)/diff(xrange)
    newpos <- xusr[1]+(xpos-xrange[1])*diff(xusr)/diff(xrange)
    par(xpd=TRUE)
    atvert <- switch(boxplot.side, par()$usr[3]-par()$cxy[2]*0.8,
                     "", par()$usr[4]+par()$cxy[2]*0.8, "")
    if(atvert!=""){
      boxplot(z, at=atvert, boxwex=bwex, add=TRUE, horizontal=TRUE, xaxt="n")
      axis(side=boxplot.side, line=1.5,
           at=newpos, labels=xpos, cex.axis=0.75, mgp=c(2, 0.5, 0))
    }
    par(xpd=FALSE)
  }
  if (crit1 >= 1)
    crit1 <- crit1/(length.na(z))
  if (crit2 >= 1)
    crit2 <- crit2/(length.na(z))
  tmpind <- (z > quantile.na(z, probs = 1 - crit2)) | (z <
                                  quantile.na(z, probs = crit1))
  n <- prod(unlist(layout))
  n.all <- length(z)
  n.na <- sum(is.na(z))
  nhalf <- length(lohi.colors)%/%2
  n2 <- 2*nhalf
  n.one <- length(lohi.colors)
  plo <- crit1*(0:nhalf)/nhalf
  phi <- 1-crit2*(nhalf:0)/nhalf
  quiles1 <- quantile.na(z, plo)
  quiles2 <- quantile.na(z, phi)
  if(split=="intervals"){
    quiles1[2:nhalf] <- quiles1[1]+(quiles1[nhalf+1]-quiles1[1])*
      (1:(nhalf-1))/nhalf
    quiles2[2:nhalf] <- quiles2[1]-(quiles2[nhalf+1]-quiles2[1])*
      ((nhalf-1):1)/nhalf
    plo[-1] <- sapply(quiles1[-1],
                      function(x, z)sum(z<=x, na.rm=TRUE)/length.na(z), z=z)
    phi[-1] <- sapply(quiles2[-1],
                      function(x, z)sum(z<=x, na.rm=TRUE)/length.na(z), z=z)
  }

  if(crit1+crit2<1){
    quiles <- c(quiles1,quiles2)
    frac <- c(plo, phi)
    colpal <- c(lohi.colors[1:nhalf],"#FFFFFF",
                lohi.colors[(n.one-nhalf+1):(n.one)])
    midbreak <- TRUE
  }
  else {colpal <- lohi.colors
        midbreak <- FALSE
        quiles <- quantile.na(z, (0:n.one)/n.one)
        frac <- c(plo, phi[-1])
      }
  dups <- duplicated(quiles)
  if(any(dups)){
    cats <- seq(along=quiles[-1])
    filledcats <- cats[!dups]
    cutcats <- as.integer(cut(z, quiles[!dups], include.lowest=TRUE))
    fullm <- filledcats[cutcats]}
  else fullm <- as.integer(cut(z, quiles, include.lowest=TRUE))
  n.one <- length(colpal)
  nrects <- length(quiles)
  if(any(is.na(z))){
    nacat <- TRUE
    fullm[is.na(fullm)] <- max(unique(fullm[!is.na(fullm)]))+1
    colpal <- c(colpal, nacolor)
  }
  else nacat <- FALSE
  if ((length(as.vector(z)) != n) & (!is.null(names(z)))) {
    y <- fullm[tmpind]
    fullm <- rep(NA, n)
    fullm[as.integer(names(y))] <- y
  }
  else fullm[!tmpind] <- NA
  if ((length(as.vector(z)) != n) & (is.null(names(z)))) {
    stop(paste("Error: Length of vector is different from total number\n",
               "of spots and vector has no row.name.\n"))
  }
#################################################################
  gc <- layout$ngrid.c
  gr <- layout$ngrid.r
  sc <- layout$nspot.c
  sr <- layout$nspot.r
  full <- block2matrix(fullm, sr, sc, gr, gc)
  image(1:ncol(full), 1:nrow(full), t(full), axes = FALSE,
        xlab = "", ylab = "", col=colpal)
  box()
  abline(v = ((gr - 1):1) * (sr) + 0.5)
  abline(h = (1:(gc - 1)) * (sc) + 0.5)
#################################################################
  if(boxplot.side%in%c(1,3))bplot(z, boxplot.side=boxplot.side)
  if(key.side%in%c(2,4)){
    chw <- par()$cxy[1]
    barwid <- 0.75*chw
    if(key.side==2){
      x0 <- par()$usr[1]-chw-barwid
      xcutpos <- x0 - 0.4*chw
      xquilepos <- x0+barwid+0.55*chw
      srt <- 90
    }
    else {
      x0 <- par()$usr[2]+chw
      xcutpos <- x0 + barwid + 0.4*chw
      xquilepos <- x0-0.4*chw
      srt <- -90
    }
    yvals2 <- seq(from=par()$usr[3], to=par()$usr[4],
                  length=n2+midbreak+2*nacat+1)[-(n2+midbreak+2*nacat+1)]
    eps2 <- diff(yvals2[1:2])

    if(nacat){
      nlast <- length(yvals2)
      nclast <- length(colpal)
      rect(x0, yvals2[nlast], x0+barwid, yvals2[nlast]+eps2,
           col=colpal[nclast], xpd=TRUE)
      text(x0+0.5*barwid, yvals2[nlast]+0.5*eps2, "NA",
           xpd=TRUE, srt=srt)
      yvals2 <- yvals2[-((nlast-1):nlast)]
      colpal <- colpal[-nclast]
    }
    if(!midbreak){
      rect(x0, yvals2, x0+barwid, yvals2+eps2,
           col=colpal, xpd=TRUE)
      text(xcutpos, c(yvals2[1],yvals2+eps2),
           paste(signif(quiles,3)), srt=srt, xpd=TRUE, cex=0.8)
      text(xquilepos, yvals2[1], "(0%)", srt=srt, xpd=TRUE, cex=0.65)
      fracs <- frac[-c(1, length(frac))]
      text(xquilepos, yvals2[-1],
           paste("(",round(fracs*100,2),")",sep=""),
           srt=srt, xpd=TRUE, cex=0.65)
      text(xquilepos, yvals2[length(yvals2)]+eps2, "(100%)", srt=srt,
           xpd=TRUE, cex=0.65)

    }
    else {rect(x0, yvals2[1:nhalf], x0+barwid, yvals2[1:nhalf]+eps2,
               col=colpal[1:nhalf], xpd=TRUE)
          rect(x0, yvals2[(nhalf+2):(2*nhalf+1)], x0+barwid,
               yvals2[(nhalf+2):(2*nhalf+1)]+eps2,
               col=colpal[(nhalf+2):(2*nhalf+1)], xpd=TRUE)
          text(xcutpos, yvals2[1:(nhalf+1)],
               paste(signif(quiles1,3)), srt=srt, xpd=TRUE, cex=0.8)
          text(xquilepos, yvals2[2:(nhalf+1)],
               paste("(",round(plo[-1]*100,2),")",sep=""), srt=srt,
               xpd=TRUE, cex=0.65)
          text(xquilepos, yvals2[1], "(0%)", srt=srt, xpd=TRUE, cex=0.65)
          text(xcutpos,
               c(yvals2[(nhalf+2):(2*nhalf+1)], yvals2[2*nhalf+1]+eps2),
               paste(signif(quiles2,3)), srt=srt, xpd=TRUE, cex=0.8)
          text(xquilepos, yvals2[(nhalf+2):(2*nhalf+1)],
               paste("(",round(phi[-length(phi)]*100,2),")",sep=""),
               srt=srt, xpd=TRUE, cex=0.65)
          text(xquilepos, yvals2[2*nhalf+1]+eps2, "(100%)", srt=srt,
               xpd=TRUE, cex=0.65)
        }
  }
  invisible()
  }

Matrix holding mRNA counts

Description

Three treatments (3 samples each) were applied to Arabidopsis plants. RNA-Seq technology was used to determine messenger RNA (mRNA) counts. These were processed to remove counts for sequences that could not be identified as corresponding to a gene.

Usage

data("plantStressCounts")

Format

The matrix plantStressCounts has 28775 rows, and 9 columns. Rows have the nondescript names "Gene1" "Gene2" "Gene3" "Gene4" ... . Columns are named "CTL1", "CTL2", "CTL3", "Light1", "Light2", "Light3", "Drought1", "Drought2", "Drought3"

Details

The treatments were:

Control

Plants were grown under normal light and watering conditions

Light stress

One hour of continuous exposure to light at ten times the level that the plants are normally grown under

Drought stress

Nine days without water, causing wilting of the leaves

The interest is in how light and drought stress affect gene expression to produce proteins.

Source

Data are from Peter Crisp, obtained as part of his PhD work in the ARC Centre of Excellence in Plant Energy Biology at Australian National University.

Examples

data(plantStressCounts)
## maybe str(plantStressCounts) ; plot(plantStressCounts) ...

Sequence of movements of spotted microarray printhead

Description

Shows the sequence of movements of a spotted microarray printhead, when a slide is printed.

Usage

plotprintseq(ngrid.r = 4, ngrid.c = 4, nspot.r = 16, nspot.c = 12,
gridorder = expand.grid(row = 1:ngrid.c, col = 1:ngrid.r),
spotorder = list(x = nspot.r:1, y = nspot.c:1), rowmajor = FALSE, eps =
1, delay1 = 100,
delay2 = 2000)

Arguments

Examples

plotprintseq()

## The function is currently defined as
function(ngrid.r=4, ngrid.c=4,
             nspot.r=16, nspot.c=12,
             gridorder=expand.grid(row=1:ngrid.c, col=1:ngrid.r),
             spotorder=list(x=nspot.r:1, y=nspot.c:1),
             rowmajor=FALSE, eps=1, delay1=100, delay2=2000){
        oldpar <- par(mar=par()$mar-c(0,0,2.5,0))
        on.exit(par(oldpar))
        plotpoints <- function(i, j, delay1=5000, delay2=10000){
            points(i+xy$x, j+xy$y, pch=15,
                   cex=0.5, col="cyan")
            x <- 0
            for(k in 1:delay2)x <- x+1
            points(i+xy$x, j+xy$y, pch=15,
                   cex=0.85, col="grey60")
            x <- 0
            for(k in 1:delay1)x <- x+1
        }

        xy <- gridorder-1
        names(xy) <- c("x","y")
        xy$x <- xy$x*(nspot.c+eps)
        xy$y <- xy$y*(nspot.r+eps)
        plot(c(1, ngrid.c*(nspot.c+eps)),
             c(1, ngrid.r*(nspot.r+eps)),
             type="n",xlab="",ylab="",axes=FALSE)
        mtext(side=1, line=1,
              paste("Grid layout:  #rows of Grids =", ngrid.r,
                      "   #columns of Grids =", ngrid.c))
        mtext(side=1, line=2.5,
              paste("In each grid:  #rows of Spots =", nspot.r,
                      "  #columns of Spots =", nspot.c))
        if (rowmajor)
        for(j in spotorder$x) for(i in spotorder$y)
        	plotpoints(i,j, delay1=delay1, delay2=delay2)
        	else
        	for(i in spotorder$y) for(j in spotorder$x)
        	plotpoints(i,j, delay1=delay1, delay2=delay2)
            }

Mitochondrial DNA sequence data from 14 primates

Description

Bases at 232 mitochondrial locations (not continuous), for each of 14 primates.

Usage

data(primateDNA)

Format

A matrix of 14 rows (primate species) by 232 locations.

Source

Data, originally from Masami Hasegawa, are from http://evolution.genetics.washington.edu/book/primates.dna

References

Felsenstein, J. 2003. Inferring Phylogenies. Sinauer.

Examples

data(primateDNA)
## Not run: 
library(ape)
primates.dist <- dist.dna(as.DNAbin(primateDNA), model = "K80") 
primates.cmd <- cmdscale(primates.dist)
lefrt <- primates.cmd[,1] < apply(primates.cmd, 1, mean)
plot(primates.cmd)
text(primates.cmd, rownames(primates.cmd), pos=lefrt*2+2)

## End(Not run)

Repeat specified plot across multiple columns of a matrix

Description

This is designed to repeat a plot, usually an image plot, across multiple columns of a matrix of gene expression values. A boxplot that shows the distribution of values appears below each panel.

Usage

xplot(data = DAAGbio::coralRG$R, images = 1:6, layout = DAAGbio::coralRG$printer, mfrow =
c(3, 2),
FUN = imgplot, device = NULL, title = NULL, width = 7.5, height = 10,
paneltitles = c("1:R/G", "2:G/R", "3:R/G", "4:G/R", "5:R/G", "6:G/R"),
paneltitles.line = 0.5,
mar = c(3.6, 3.6, 1.6, 0.6), oma = c(0.6, 0.6, 1.6, 0.6), file = NULL)

Arguments

Author(s)

J. H. Maindonald

Examples

## Not run: 
xplot(data=coralRG$R, layout=coralRG$printer, FUN=imgplot)

## End(Not run)

## The function is currently defined as
function(data = DAAGbio::coralRG$R, images=1:6, layout = DAAGbio::coralRG$printer, mfrow=c(3,2),
           FUN = imgplot, device=NULL, title=NULL, width=7.5, height=10,
           paneltitles=c("1:R/G","2:G/R", "3:R/G","4:G/R", "5:R/G","6:G/R"),
           paneltitles.line=0.5,
           mar=c(3.6,3.6,1.6,0.6), oma=c(0.6,0.6,1.6,0.6), file=NULL){
    if(is.null(title)){title <- as.character(substitute(data))
    title <- paste(title[2], title[3], sep=":")
                    }
    if(is.null(file))file <- title
    nch <- nchar(title)
    if(!is.null(device)){devnam <- deparse(substitute(device))
    ext <- switch(devnam, ps="ps", pdf="pdf", png="png",
                  jpeg="jpg", bitmap="bmp")
      file <- paste(title,".", ext, sep="")
      print(file)
      device(file=file, width=width, height=height)
    }
    oldpar <- par(mfrow=mfrow, mgp=c(1,0.25,0), oma=oma, mar=mar)
    on.exit(par(oldpar))
    for(i in images){
      FUN(data[,i], layout=layout)
      mtext(side=3,line=paneltitles.line,paneltitles[i],adj=0)
    }
    mtext(side=3, line=0.25, title, outer=TRUE)
    if(!is.null(device))dev.off()
  }