maigesRaw-class:
maigesRaw class, store raw microarray datasets
Description
This class describes objects to handle intensity values and information
about genes and samples used in the data. Objects of this class are
obtained from class maigesPreRaw using function
createMaigesRaw.
Slots
Sf:- numeric matrix containing the test samples
spots intensity values, spots corresponding to rows and data
tables (or chips) corresponding to columns.
Sb:- numeric matrix containing the test samples
local background values, spots corresponding to rows and data
tables (or chips) corresponding to columns.
Sdye:- character vector with length equal the length of
data files (chips), specifying the channel ('ch1' or 'ch2') used
to label each test sample in each chip.
Rb:- numeric matrix containing the reference samples
spots intensity values, spots corresponding to rows and data
tables (or chips) corresponding to columns.
Rf:- numeric matrix containing the reference samples
spots intensity values, spots corresponding to rows and data
tables (or chips) corresponding to columns.
Rdye:- character vector with length equal the length of
data files (chips), specifying the channel ('ch1' or 'ch2') used
to label each reference sample in each chip.
Flag:- matrix specifying the flags for the spots, as in
the matrices above, rows and columns correspond to genes and
samples, respectively. Type o value may be numeric or character,
depending of the flags used.
BadSpots:- logical vector specifying spots that was
judged as bad ones. By default this slot is created as a vector of
FALSEs with same length as number of spots.
UseSpots:- logical matrix indexing the spots to be used
for normalisation.
GeneGrps:- a logical matrix with rows representing the
spots and columns representing different gene groups. Each column
give the index of spots in that gene group.
Paths:- list containing
graphNEL objects
specifying gene regulatory networks (or pathways). The first
object in this list is a char string giving the gene label used to
match the genes. Layout:- a list containing the number of rows (
gridR) and
columns (gridC) of grids, the number of rows (spotR)
and columns (spotC) of spots inside each grid and the total
number of spots. Glabels:- data frame giving the gene labels. These
labels are generally used during the data analysis.
Slabels:- data frame giving the sample labels. These
labels are generally used during the data analysis.
Notes:- char string that receives any comment about the
dataset. The dataset description is stored in this slot.
Date:- char string giving the date and hour that the
object was created.
V.info:- list containg three characters. The first one is
a string containing the R version used when the object was
created. The second is a char vector with base packages and the
last one is another char vector with additional packages and
version numbers.
Methods
- [
signature(x = 'maigesRaw'): subsetting operator for
spots on the array or arrays in the batch, ensures that all slots
are subset properly.- boxplot
signature(x = 'maigesRaw'): boxplot method for
maigesRaw class. Display boxplots of the slides and
print tip groups using package marray.- dim
signature(x = 'maigesRaw', value = 'numeric'): get
the dimensions of the object, numeric vector of length two.- image
signature(x = 'maigesRaw'): image method for
maigesRaw class. Display colour representation of
the slides using package marray.- plot
signature(x = 'maigesRaw'): plot method for
maigesRaw class. Display MA plots.- print
signature(x = 'maigesRaw'): print method for
maigesRaw class.- show
signature(x = 'maigesRaw'): show method for
maigesRaw class.- summary
signature(x = 'maigesRaw'): summary method for
maigesRaw class.
Details
This class of objects defines a real raw object that is used to do the
normalisation. Objects of this class are generated from objects of
class maigesPreRaw using the function
createMaigesRaw. Here it is possible to do several
plots for exploratory analysis using functions from
marray package. Using the function
selSpots, you select spots to use in the normalisation
method, that is done by the functions normLoc, normOLIN,
normRepLoess, normScaleLimma and
normScaleMarray.