Description Usage Arguments Value Slots Methods Pre-defined Processors Author(s) See Also Examples
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.
1 2 3 4 |
object |
In the |
action |
A |
parameter |
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 Processor
.
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
Apply the function
represented by action
to the Channel
object, updating
the history appropriately. If the parameter
is NULL
,
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
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.
Kevin R. Coombes krc@silicovore.com
Channel
,
CompleteChannel
,
process
,
Pipeline
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 | 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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