R: The Processor class

Processor-class {PreProcess}

R Documentation

The Processor class

Description

A 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.

Usage

## S4 method for signature 'Channel, Processor':
process(object, action, parameter=NULL)
## S4 method for signature 'Processor':
summary(object, ...)

Arguments

`object`	In the `process` method, a `Channel` object. In the `summary` method, a `Processor` object
`action`	A `Processor` object used to process a `Channel`.
`parameter`	Any object that makes sense as a parameter to the function represented by the `Processor` `action`
`...`	Additional arguments are as in the underlying generic methods.

Value

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 Processor.

Slots

f:: A function that will be used to process microarray-related object
default:: The default value of the parameters to the function f
name:: A string containing the name of the object
description:: A string containing a longer description of the object

Methods

process(object, action, parameter): Apply the function represnted by action to the Channel object, updating the history appropriately. If the parameter is NULL, then use the default value.
summary(object, ...): Write out a summary of the object.

Pre-defined Processors

The library comes with several Processor objects already defined; each one takes a Channel as input and produces a modified Channel as output.

PROC.SUBTRACTOR: Subtracts a global constant (default: 0) from the data vector in the Channel.
PROC.THRESHOLD: Truncates the data vector below, replacing the values below a threshold (default: 0) with the threshold value.
PROC.GLOBAL.NORMALIZATION: Normalizes the data vector in the Channel by dividing by a global constant. If the parameter takes on its default value of 0, then divide by the 75th percentile.
PROC.LOG.TRANSFORM: Performs a log transformation of the data vector. The parameter specifies the base of the logarithm (default: 2).
PROC.MEDIAN.EXPRESSED.NORMALIZATION: Normalizes the data vector by dividing by the median of the expressed genes, where ``expressed'' is taken to mean ``greater than zero''.
PROC.SUBSET.NORMALIZATION: 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.
PROC.SUBSET.MEAN.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.

Author(s)

Kevin R. Coombes <kcoombes@mdanderson.org>

Examples

# 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, x=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)

[Package PreProcess version 1.3 Index]