| RPPADesign-class {SuperCurve} | R Documentation |
This class represents the information that describes how a particular set of RPPA slides was designed.
RPPADesign(raw, steps = NULL, series = NULL,
grouping = c("byRow","byCol", "bySample", "blockSample"),
ordering = c("decreasing","increasing"),
alias = NULL, center = FALSE, controls = list())
seriesNames(design)
getSteps(design)
## S4 method for signature 'RPPADesign':
image(x, ...)
## S4 method for signature 'RPPADesign':
summary(object, ...)
## S4 method for signature 'RPPADesign':
names(x)
raw |
A data frame or an RPPA object. |
steps |
An optional numeric vector listing the dilution step associated with each spot, on a logarithmic scale. |
series |
An optional character vector or factor identifying the dilution series to which each spot corresponds. |
grouping |
Describes the way dilution series are oriented on the array. |
ordering |
Are dilution series arranges in order of
increasing or decreasing concentrations. Default is
decreasing. |
alias |
A data frame containing two columns: Alias and Sample |
center |
A logical value: if TRUE, then dilution steps are centered around 0. |
controls |
A list containing the character strings that identify control spots on the array. |
x |
A RPPADesign object |
object |
A RPPADesign object |
design |
A RPPADesign object |
... |
The usual extra arguments for generic or plotting routines. |
From their inception, reverse-phase protein array experiments have spotted samples on the array in dilution series. Thus, a critical aspect of the design and analysis is to understand how the dilution series are placed on the array.
The optional grouping and ordering arguments allow the
user to specify several standard layouts without having to go into great
detail. The most common layout is byRow, which indicates that
each row of a subgrid on the array should be considered as a separate
dilution series. Although considerably less common (for reasons related
to the robotics of how arrays are printed), the byCol layout
indicates that each column of a subgrid is its own dilution series. The
bySample layout means that each unique sample name indicates its
own dilution series. Finally, the blockSample layout indicates
that all occurrences of a sample name within a subgrid (or block) refer
to the same dilution series. The blockSample layout can be used,
for example, when a dilution series is long enough to extend over more
than one row of a subgrid. One layout we have seen used seven dilution
steps followed by a control spot, contained in two successive rows of a
design with 4x4 subgrids, leading to the pattern:
7 6 5 4
3 2 1 C
If the design of an RPPA experiment does not follow one of the built-in
patterns, you can create an object by supplying vectors of dilution
series names (in the series argument) and corresponding dilution
steps (in the steps argument) that explicitly provide the mapping
for each spot.
The image method invisibly returns the displayed matrix of
dilution steps.
The summary method returns the summary object of the
layout data frame.
The names method returns a character vector.
The getSteps function returns a numeric vector containing, for
each non-control spot, the step represented by that spot in its dilution
series.
The seriesNames function returns a character vector containing
the names of the unique (non-control) dilution series on the array.
Objects of the RPPADesign class should be constructed using the
RPPADesign function.
layout:alias:sampleMap:controls:image method produces a
two-dimensional graphical display of the layout design. Colors are used
to represent different dilution steps, and laid out in the same pattern
as the rows and columns of the array. This provides a visual check that
the design has been specified correctly.summary method lists the
names of the control spots on the array and then prints a summary of the
data frame describing the layout.names method returns a character vector
containing, for each non-control spot, the name of the dilution series
to which that spot belongs. Kevin R. Coombes <kcoombes@mdanderson.org>
KRC
path <- system.file("rppaTumorData", package="SuperCurve")
erk2 <- RPPA("ERK2.txt", path=path)
design <- RPPADesign(erk2, grouping="blockSample", center=TRUE)
image(design)
summary(design)
design <- RPPADesign(erk2, grouping="blockSample",
controls=list("neg con", "pos con"))
image(design)
summary(design)
path <- system.file("rppaCellData", package="SuperCurve")
akt <- RPPA("Akt.txt", path=path)
# Uses duplicate 8-step dilution series within 4x4 subgrids.
# They are interleaved, with the top two identical rows
# containing the first 4 steps and the bottom two identical
# rows containing the last 4 steps.
steps <- rep(c(rep(8:5, 2), rep(4:1, 2)), 40) - 4.5
rep.temp <- factor(paste('Rep', rep(rep(1:2, each=4), 80), sep=''))
series <- factor(paste(as.character(akt@data$Sample),
as.character(rep.temp),
sep='.'))
design40 <- RPPADesign(akt, steps=steps, series=series)
image(design40)
summary(design40)