Processor represents a function that acts on the data of a some
object to process it in some way. The result is always another related
object, which should record some history about exactly how it was processed.
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Any object that makes sense as a parameter to the
function represented by the
Additional arguments are as in the underlying generic methods.
The return value of the generic function
process is always
an object related to its
Channel input, which keeps a record
of its history. The precise class of the result depends on the
function used to create the
A function that will be used to process microarray-related object
The default value of the parameters to the
A string containing the name of the object
A string containing a longer description of the object
Apply the function
action to the
Channel object, updating
the history appropriately. If the
then use the default value.
Write out a summary of the object.
The library comes with several
Processor objects already
defined; each one takes a
Channel as input and produces a
Channel as output.
Subtracts a global constant (default:
0) from the data vector in the
Truncates the data vector below, replacing the values below a threshold (default: 0) with the threshold value.
Normalizes the data vector
Channel by dividing by a global constant. If the
parameter takes on its default value of 0, then divide by the 75th
Performs a log transformation of the data vector. The parameter specifies the base of the logarithm (default: 2).
Normalizes the data vector by dividing by the median of the expressed genes, where “expressed” is taken to mean “greater than zero”.
Normalizes the data vector by dividing by the median of a subset of genes. When the parameter has a default value of 0, then this method uses the global median. Otherwise, the parameter should be set to a logical or numerical vector that selects the subset of genes to be used for normalization.
Normalizes the data vector by dividing by the mean of a subset of genes. When the parameter has a default value of 0, then this method uses the global mean. Otherwise, the parameter should be set to a logical or numerical vector that selects the subset of genes to be used for normalization.
Kevin R. Coombes firstname.lastname@example.org
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showClass("Processor") ## simulate a moderately realistic looking microarray nc <- 100 nr <- 100 v <- rexp(nc*nr, 1/1000) b <- rnorm(nc*nr, 80, 10) s <- sapply(v-b, max, 1) ct <- ChannelType('user', 'random', nc, nr, 'fake') subbed <- Channel(name='fraud', parent='', type=ct, vec=s) rm(ct, nc, nr, v, b, s) # clean some stuff ## example of standard data processing nor <- process(subbed, PROC.GLOBAL.NORMALIZATION) thr <- process(nor, PROC.THRESHOLD, 25) processed <- process(thr, PROC.LOG.TRANSFORM, 2) summary(processed) par(mfrow=c(2,1)) plot(processed) hist(processed) par(mfrow=c(1,1)) image(processed) rm(nor, thr, subbed, processed)
Loading required package: oompaBase Class "Processor" [package "PreProcess"] Slots: Name: f default name description Class: function numeric or NULL character character log normalized fraud, a microarray channel object Parent object: NA Microarray type: user random Labeled with: fake Design size: 100 by 100 Design information object: History: Channel(parent = "", name = "fraud", type = ct, vec = s) Global normalization (using object: PROC.GLOBAL.NORMALIZATION) with parameter = 0 Truncated below (using object: PROC.THRESHOLD) with parameter = 25 Log transformation (using object: PROC.LOG.TRANSFORM) with parameter = 2 Min. 1st Qu. Median Mean 3rd Qu. Max. 4.644 7.264 8.878 8.461 9.966 12.837
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