Description Usage Arguments Author(s) See Also Examples
Gets the spotwise intensity-adjusted variability of replicate slides. Within-slide replicates are considered to be independent of each other.
1 2 |
robust |
If |
force |
If |
slides |
The slides which should be included in the calculations.
If |
Henrik Bengtsson (http://www.braju.com/R/)
See also *getSpotVariability()
for non-intensity dependent
scale adjustment.
variabilities see *getMOR()
.
For more information see MAData
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | SMA$loadData("mouse.data")
# Keep only slides of treatment 2
mouse.data <- lapply(mouse.data, FUN=function(x) x[,4:6])
layout <- Layout$read("MouseArray.Layout.dat", path=system.file("data-ex", package="aroma"))
raw <- RawData(mouse.data, layout=layout)
# Create four sets of slides where slide 2 and 4 are shifted R=G=a
a <- 2^11
ma <- list()
ma[[1]] <- getSignal(raw, bgSubtract=TRUE)
ma[[2]] <- clone(ma[[1]])
shift(ma[[2]], R=a, G=a)
ma[[3]] <- clone(ma[[1]])
normalizeWithinSlide(ma[[3]], method="p")
ma[[4]] <- clone(ma[[3]])
shift(ma[[4]], R=a, G=a)
# Calculates the (adjusted) spot variability
d <- list()
for (k in 1:length(ma))
d[[k]] <- getAdjustedSpotVariability(ma[[k]])
# Four plots
subplots(4)
# Plot 1 and 2: Non-normalized and print-tip normalized slides
# where each exists in a shifted and a non-shifted version
Alim <- Mlim <- NA
for (k in 1:length(ma)) {
Alim <- range(c(Alim, ma[[k]]$A), na.rm=TRUE)
Mlim <- range(c(Mlim, ma[[k]]$M), na.rm=TRUE)
}
plot(ma[[1]], xlim=Alim, ylim=Mlim, col=1)
points(ma[[2]], col=2)
plot(ma[[3]], xlim=Alim, ylim=Mlim, col=3)
points(ma[[4]], col=4)
# Plot 3: Densities of the (non-adjusted) spot variabilities
ymax <- xmax <- NA
ds <- list()
for (k in 1:length(ma)) {
ds[[k]] <- density(na.omit(d[[k]]$d))
ymax <- max(ymax, ds[[k]]$y, na.rm=TRUE)
}
for (k in 1:length(ma))
xmax <- max(xmax, ds[[k]]$x[ds[[k]]$y > 0.01*ymax], na.rm=TRUE)
xlim <- c(0,xmax)
ylim <- c(0,ymax)
plot(NA, xlim=xlim, ylim=ylim, xlab="variability", ylab="density",
main="Spot variabilities")
for (k in 1:length(ma))
lines(ds[[k]], col=k)
# Plot 4: Densities of the *adjusted* spot variabilities
ymax <- xmax <- NA
ds <- list()
for (k in 1:length(ma)) {
ds[[k]] <- density(na.omit(d[[k]]$dw))
ymax <- max(ymax, ds[[k]]$y, na.rm=TRUE)
}
for (k in 1:length(ma))
xmax <- max(xmax, ds[[k]]$x[ds[[k]]$y > 0.01*ymax], na.rm=TRUE)
xlim <- c(0,xmax)
ylim <- c(0,ymax)
plot(NA, xlim=xlim, ylim=ylim, xlab="variability", ylab="density",
main="Adjusted spot variabilities")
for (k in 1:length(ma))
lines(ds[[k]], col=k)
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