CompleteChannel-class {PreProcess} | R Documentation |
Class "CompleteChannel"
Description
An object of the CompleteChannel
class represents one channel (red or
green) of a two-color fluorescence microarray experiment. Alternatively,
it can also represent the entirety of a radioactive microarray experiment.
Affymetrix experiments produce data with a somewhat different structure
because they use multiple probes for each target gene.
Usage
CompleteChannel(name, type, data)
## S4 method for signature 'CompleteChannel'
print(x, ...)
## S4 method for signature 'CompleteChannel'
show(object)
## S4 method for signature 'CompleteChannel'
summary(object, ...)
## S4 method for signature 'CompleteChannel'
as.data.frame(x, row.names=NULL, optional=FALSE)
## S4 method for signature 'CompleteChannel,missing'
plot(x, main=x@name, useLog=FALSE, ...)
## S4 method for signature 'CompleteChannel'
image(x, ...)
## S4 method for signature 'CompleteChannel'
analyze(object, useLog=FALSE, ...)
## S4 method for signature 'CompleteChannel,Processor'
process(object, action, parameter)
## S4 method for signature 'CompleteChannel'
channelize(object, ...)
Arguments
name |
character string specifying the name of the object |
type |
object of class |
data |
data frame. For the pre-defined “extraction”
processors to work correctly, this should include columns called
|
x |
object of class |
object |
object of class |
main |
character string specifying the title for the plot |
useLog |
logical scalar. If |
action |
object of class |
parameter |
any object that makes sense as a parameter to the
function represented by the |
row.names |
See |
optional |
See |
... |
extra arguments for generic or plotting routines |
Details
The names come from the default column names in the ArrayVision software package used at M.D. Anderson for quantifying glass or nylon microarrays. Column names used by other software packages should be mapped to these.
Value
The analyze
method returns a list of three density functions.
The return value of the process
function depends on the
Processor
performing the action, but is typically a
Channel
object.
Graphical methods invisibly return the object on which they were invoked.
Slots
name
:character string containing the name of the object
type
:object of class
ChannelType
data
:data frame
history
:list that keeps a record of the calls used to produce this object
Methods
- print(x, ...)
Print all the data on the object. Since this includes the data frame, you rarely want to do this.
- show(object)
Print all the data on the object. Since this includes the data frame, you rarely want to do this.
- summary(object, ...)
Write out a summary of the object.
- as.data.frame(x,row.names=NULL, optional=FALSE)
Convert the
CompleteChannel
object into a data frame. As you might expect, this simply returns the data frame in thedata
slot of the object.- plot(x, useLog=FALSE, ...)
Produces three estimated density plots: one for the signal, one for the background, and one for the background-corrected signal. Additional graphical parameters are passed along. The logical flag
useLog
determines whether the data are log-transformed before estimating and plotting densities.- analyze(object, useLog=FALSE, ...)
This method computes the estimated probability density functions for the three data components (signal, background, and background-corrected signal), and returns them as a list.
- image(object, ...)
Uses the image method for
Channel
objects to produce geographically aligned images of the log-transformed intensity and background estimates.- channelize(object, ...)
character string giving the name of the class of a channel that is produced when you process a
CompleteChannel
object.- process(object, action, parameter=NULL)
Use the
Processor
action
to process theCompleteChannel
object
. Returns an object of the class described bychannelize
, which defaults toChannel
.
Pre-defined Processors
The library comes with several Processor
objects already
defined; each one takes a CompleteChannel
as input, extracts a
single value per spot, and produces a Channel
as output.
PROC.BACKGROUND
Extract the vector of local background measurements.
PROC.SIGNAL
Extract the vector of foreground signal intensity measurements.
PROC.CORRECTED.SIGNAL
Extract the vector of background-corrected signal measurements. Note that many software packages automatically truncate these value below at zero, so this need not be the same as SIGNAL - BACKGROUND.
PROC.NEG.CORRECTED.SIGNAL
Extract the vector of background-corrected signal intensities by subtracting the local background from the observed foreground, without truncation.
PROC.SD.SIGNAL
Extract the vector of pixel standard deviations of the signal intensity.
PROC.SIGNAL.TO.NOISE
Extract the vector of signal-to-noise ratios, defined as CORRECTED.SIGNAL divided by the standard deviation of the background pixels.
Author(s)
Kevin R. Coombes krc@silicovore.com, P. Roebuck proebuck@mdanderson.org
See Also
process
,
Processor
,
Pipeline
,
Channel
,
as.data.frame
Examples
showClass("CompleteChannel")
## simulate a complete channel object
v <- rexp(10000, 1/1000)
b <- rnorm(10000, 60, 6)
s <- sapply(v-b, function(x) {max(0, x)})
ct <- ChannelType('user', 'random', 100, 100, 'fake')
x <- CompleteChannel(name='fraud', type=ct,
data=data.frame(vol=v, bkgd=b, svol=s))
rm(v, b, s, ct)
summary(x)
opar <- par(mfrow=c(2,3))
plot(x)
plot(x, main='Log Scale', useLog=TRUE)
par(opar)
opar <- par(mfrow=c(2,1))
image(x)
par(opar)
b <- process(x, PROC.NEG.CORRECTED.SIGNAL)
summary(b)
q <- process(b, PIPELINE.STANDARD)
summary(q)
q <- process(x, PIPELINE.MDACC.DEFAULT)
summary(q)
## cleanup
rm(x, b, q, opar)