Package 'genArise'

Description

Extract A values from a Spot.

Usage

a.arise(mySpot)

Arguments

Value

List of A-values. (log(cy3, 2) + log(cy5, 2))/2

See Also

m.arise.

Examples

## read the spot from a file and save it in spot
data(Simon)
## Extract A from spot and save in a
a <- a.arise(mySpot = Simon)

Description

This function allows to remove from the spot repeated Id's. Before moving one of the repeated Id's the function compute the log ratio of both values with the same Id and delete the least absolute value if both of them are positive or negative. In other case delete both observations.

Usage

alter.unique(mySpot)

Arguments

Value

Spot object without duplicates.

Examples

data(Simon)
## filter the spot and save it in f.spot
f.spot <- filter.spot(Simon)
## remove duplicates and save it in u.spot
u.spot <- alter.unique(f.spot)

Description

Auxiliar function of genArise GUI, in this window you can apply operations to original data.

Usage

analysis.window(texto, follow.wizard = FALSE, envir, swap)

Arguments

Value

tkwidget

Description

Performed an HTML file

Usage

annotations(specie.data, specie, column, symbol,
output.file = "annotations.html")

Arguments

Value

HTML file with link for each spot in data frame

Description

Auxiliar function of genArise GUI.

Usage

back.gui(envir)

Arguments

Value

tkwidget

Description

This function use the background data to eliminate unwanted effects in signal. The background correction establish the new Cy3 signal as Cy3 - BgCy3 and the new Cy5 as Cy5 - BgCy5.

Usage

bg.correct(mySpot)

Arguments

Value

Spot object with the background correction done.

Examples

data(Simon)
## background correction and save it in c.spot
c.spot <- bg.correct(Simon)

Description

This package defines the following data classes.

Spot

A class used to store spot data with the following attributes: Cy3, Cy5, BgCy3, BgCy5, Ids as they are read by read.spot or obtained from a function that return a spot object.

DataSet

A class used to store spot data with the following attributes: Cy3, Cy5, Ids, Z-score.

Description

Auxiliar function for genAriseGUI. Create the directory's hierarchy of the project.

Usage

create.project(project.name, results.file = "Results",
               graphics.file = "Graphics")

Arguments

Description

This function shows the plot of the values from the log Cy3 against log Cy5 intensities that belongs to an object of the Spot class.

Usage

cys.plot(mySpot, col = "green")

Arguments

Examples

data(Simon)
cys.plot(Simon)

Description

A simple list-based class for storing red and green channel foreground, z-scores and the Ids.

Creating Objects from the Class

Objects can be created by calls of the form new("DataSet",sets, type) where sets is a list containing Cy3, Cy5, Id and Zscore and type is "ri" or "ma". Objects are normally created by read.spot.

Slots/List Components

This class contains no slots (other than .Data), but objects should contain the following list components:

All of these matrices should have the same dimensions.

Methods

This class inherits directly from class list so any operation appropriate for lists will work on objects of this class.

Description

This function keep only array elements with intensities that are 2 standard deviation above background.

Usage

filter.spot(mySpot)

Arguments

Value

Array elements with intensities that are 2 standard deviation above background.

References

John Quackenbush "Microarray data normalization and transformation". Nature Genetics. Vol.32 supplement pp496-501 (2002)

Examples

data(Simon)
## background correction and save it in c.spot
c.spot <- bg.correct(Simon)
## normalize spot
n.spot <- grid.norm(c.spot, nr = 23, nc = 24)
## filtering the spot
filter.spot(n.spot)

Description

This is the main function and display the GUI of genArise.

Usage

genArise()

Description

Auxiliar function of genArise GUI, this function show a principal menu of genAriseGUI

Usage

genArise.init(envir)

Arguments

Value

tkwidget

Description

After we finished our slice analysis we get a up-regulated and down-regulated set. This will be the set of study genes for genMege. Given this set, genMerge retrieves functional genomic data for each gene and provides statistical rank scores for over-representation of particular functions in the dataset.

Usage

genMerge(gene.association, description, population.genes,
study.genes, output.file = "GenMerge.txt")

Arguments

Note

This function is completly based on GeneMerge from Cristian I. Castillo-Davis and Daniel L. Hartl

References

Cristian I. Castillo-Davis Department of Statistics Harvard University http://www.oeb.harvard.edu/hartl/lab/publications/GeneMerge

Description

This function get values from an DataSet object.

This is just a function for the GUI, and can not be used in the command line.

Usage

get.values(list.values, genes.values, up.down, min.val, max.val)

Arguments

Value

An Ids list

Description

Read both files, but only extract the interested columns and create a Spot object.

Usage

get.Zscore( spot, name, Zscore.min=NULL, Zscore.max=NULL, all=FALSE, envir)

Arguments

See Also

write.spot.

Description

This function normalize R and I values and fit the value of Cy5 from his argument. In this function the normalize algorithm will be applied to all observations to get the lowess factor and then fit Cy5 with this factor. The observations. The observations with values R = 0 are deleted because they have no change in their expression levels.

Usage

global.norm(mySpot)

Arguments

Value

A new spot object but normalized, It means with a different Cy5 that is the result of the fit with the lowess factor.

Examples

data(Simon)
# Background Correction
c.spot <- bg.correct(Simon)
#Normalized data
n.spot <- global.norm(c.spot)

Description

This function show the plot of an spot sobject. This plot are identify with the graphic.type.value

Usage

graphic.choose(spot.object, graphic.type)

Arguments

Value

Plot device

Description

This function normalize R and I values and fit the value of Cy5 for each grid in the spot that it receives as argument. In this function the dimension of grid is (meta-row * meta-column).

Usage

grid.norm(mySpot, nr, nc)

Arguments

Value

Spot object with the grid normalization done.

Examples

data(Simon)
## background correction and save it in c.spot
c.spot <- bg.correct(Simon)
## normalization and save it in n.spot
n.spot <- grid.norm(c.spot, 23, 24)

Description

Display the help of genArise in the GUI. This is just a function for the GUI, and can not be used in the command line.

Usage

help()

Description

Extract I from a Spot.

Usage

i.arise(mySpot)

Arguments

Value

List of I-values

See Also

r.arise.

Examples

data(Simon)
## Extract I from spot and save in i
i.arise(Simon)

Description

Plot an image of colours representing the log intensity ratio for each spot on the array. This function can be used to explore whether there are any spatial effects in the data.

Usage

imageLimma(z, row, column, meta.row, meta.column,
low = NULL, high = NULL)

Arguments

Note

This function is based in the imageplot function from limma package.

References

Gordon K. Smyth (2004) "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments", Statistical Applications in Genetics and Molecular Biology: Vol. 3: No. 1, Article 3. http://www.bepress.com/sagmb/vol3/iss1/art3

Examples

data(Simon)
spot.data <- attr(Simon, "spotData")
M <- log(spot.data$Cy5, 2) - log(spot.data$Cy3, 2)
imageLimma(z = M, row = 23, column = 24, meta.row = 2, meta.column = 2,
low = NULL, high = NULL)

Description

Extract M values from a Spot.

Usage

m.arise(mySpot)

Arguments

Value

List of M-values

See Also

a.arise.

Examples

data(Simon)
## Extract M from spot and save in m
m <- m.arise(Simon)

Description

This function allows to plot M -vs- A in spot.

Usage

ma.plot(mySpot, col = "green")

Arguments

Examples

data(Simon)
## plot the signals for spot.
ma.plot(Simon)

Description

Read both files, but only extract the interested columns and create a Spot object.

Usage

make.swap(spot1, spot2, Cy3, Cy5, BgCy3, BgCy5, Id, Symdesc, header = FALSE, is.ifc = FALSE,envir,nr,nc)

Arguments

See Also

write.spot.

Description

This function allows to remove from the spot repeated Id's. Before moving one of the repeated Id's the function compute the average of Cy3 intensity and Cy5 intensity.

Usage

meanUnique(mySpot)

Arguments

Value

Spot object without duplicates

Examples

data(Simon)
c.spot <- bg.correct(Simon)
n.spot <- global.norm(c.spot)
f.spot <- filter.spot(n.spot)
meanUnique(f.spot)

Description

Call a editor for note about actual experiment

Usage

note(envir)

Arguments

Description

Show the information that was obtained from the analysis of a previous project. This is just an auxiliar function for genAriseGUI, and can not be used in the command line.

Usage

old.project(project.name,envir, parent)

Arguments

Value

tkwidget

Description

This function allows you to perform a set combinatorial analysis between the results previously obtained in different projects. This function is called post.analysis and it is mandatory that you have done the Zscore operation in all the selected projects. It is important to clarify that this function receives a list of files with extension prj as argument and for this reason you can't use it if the results to compare was not obtained by the genArise GUI.

Usage

post.analysis(values, min.val, max.val, up.down, output)

Arguments

Value

Once obtained the ids list for each project a number of files with extension set are created in a directory. The name of this files consists in a sequence of 0 and 1. The number of digits in the file names is the same to the number of projects in the list passed as argument to the function. There is then, a relation between the number of digits in the file names and the projects. This relation is defined by the position specified in the file order.txt in the same directory you have passed as another argument in the function.

Description

This function show a window with the information of experiment like name and dimensions, too plot an image of colours representing the log intensity ratio for each spot on the array. This is just an auxiliar function for genAriseGUI, and can not beused in the command line.

Usage

principal(envir, swap)

Arguments

Value

tkwidget

Description

Previous window to post-analysis. In this window you can select one or several files (projects) and arguments to be used by post analisis function.

This is just an auxiliar function for genAriseGUI, and can not be used in the command line.

Usage

projects.select(envir, nombre)

Arguments

Value

tkwidget

Description

Get the R values from an object of the Spot class.

Usage

r.arise(mySpot)

Arguments

Value

A vector containing the R value ( log(Cy5/Cy3)) for each observation of the spot object.

See Also

i.arise.

Examples

data(Simon)
#Get R-value from an object of the Spot class and save the result
R <- r.arise(Simon)
#Show the R-values

Description

Read all file and extract the interested columns to create a DataSet object (this file contain the zscore with all the genes after the duplicates filtering and makes not distinction between up-regulated and down-regulated. If you want to make this distinction you must write the data with the function write.dataSet, but there is no way to read this files with this function).

Usage

read.dataset(file.name, cy3 = 1, cy5 = 2, ids = 3, symdesc = NULL,
zscore = 4, type = 6, header = FALSE, sep = "\t")

Arguments

See Also

write.zscore.

Description

Read all file, but only extract the interested columns and create a Spot object.

Usage

read.spot(file.name, cy3, cy5, bg.cy3, bg.cy5, ids, symdesc, header =
FALSE, sep = "\t", is.ifc = FALSE, envir)

Arguments

See Also

write.spot.

Description

Clean all the operations saved in the prj history file.

Usage

reset.history(history.file, text)

Arguments

Value

The history file without operations.

Description

This function allows to plot R-values vs I-values I-value from a Spot object

Usage

ri.plot(mySpot, col = "green")

Arguments

See Also

colors()

Examples

data(Simon)
ri.plot(Simon)

Description

Auxiliary function for genAriseGUI

Usage

set.grid.properties(envir, name, nr, nc, nmr, nmc)

Arguments

Description

Save in the history file each operation performed while the analysis. This is just to get the open this particular project in the future. This is just an auxiliary function for the GUI, and can not be used in the command line.

Usage

set.history.project(history.file, id.name, data.file)

Arguments

Value

The history file with the new performed operation.

Description

Auxiliar function for genAriseGUI

Usage

set.path.project(path, results.file, graphics.file, envir)

Arguments

Description

Auxiliar function for genAriseGUI

Usage

set.project.properties(envir)

Arguments

Description

This structure is a data fragment of a yeast microarray from the Microarrays Unit in IFC UNAM. The original microarray contains 6 meta-rows and 4 meta-columns, however this data just belongs to the first meta-row order in a way of 2 meta-rows and 2 meta-columns.

Usage

data(Simon)

Format

A list that contains 1104 observations, because the dimensions of this example are: 2 meta-rows, 2 meta-columns, 23 rows, 24 columns.

Examples

data(Simon)
#A preview from the chip
datos <- attr(Simon, "spotData")
M <- log(datos$Cy3, 2) - log(datos$Cy5, 2)
imageLimma(M, 23, 24, 2, 2)

Description

Read both files, but only extract the interested columns and create a Spot object.

Usage

single.norm(envir)

Arguments

See Also

write.spot.

Description

A simple list-based class for storing red and green channel foreground and background intensities for a batch of spotted microarrays and the Ids.

Creating Objects from the Class

Objects can be created by calls of the form new("Spot",spot) where spot is a list. Objects are normally created by read.spot.

Slots/List Components

This class contains no slots (other than .Data), but objects should contain the following list components:

All of these matrices should have the same dimensions.

Methods

This class inherits directly from class list so any operation appropriate for lists will work on objects of this class.

Description

We consider replicate measures of two samples and adjust the log(ratio,2) measures for each gene so that the transformed values are equal. To do this we take the geometric mean.\ This procedure can be extended to averaging over n replicates.

Usage

spotUnique(mySpot)

Arguments

Value

Spot object without duplicates

Examples

data(Simon)
c.spot <- bg.correct(Simon)
f.spot <- filter.spot(c.spot)
spotUnique(mySpot = f.spot)

Description

This is just an auxiliar function for genAriseGUI, and can not be used in the command line.

Usage

swap.select(envir)

Arguments

Value

tkwidget

Description

Extract white spaces at the begining or end of a word.

Usage

trim(word)

Arguments

Value

Returns a string of characters, with leading and trailing whitespace omitted.

Examples

trim("        This is a String            ")
## return [1] "This is a String"

Description

Write the values for observations of an object of DataSet class in an output file. This values are writen in columns with the follow order: Cy3, Cy5, Cy3 Background, Cy5 Background, Ids and finally the Zscore value. By default this output file has no header.

Usage

write.dataSet(dataSet.spot, fileName, quote
= FALSE, col.names = FALSE, row.names = FALSE,
Zscore.min = NULL, Zscore.max = NULL, sep = "\t")

Arguments

Examples

data(WT.dataset)
write.dataSet(dataSet.spot = WT.dataset, fileName = "Example.csv", Zscore.min = 1,
Zscore.max = 1.5, sep = "\t")

Description

Write the values for observations of an object of Spot class in an output file. This values are writen in columns with the follow order: Cy3, Cy5, Cy3 Background, Cy5 Background and finally Ids. By default this file has no header.

Usage

write.spot(spot, fileName, quote = FALSE,sep = "\t",
col.names = FALSE, row.names = FALSE)

Arguments

read.spot.

Examples

data(Simon)
write.spot(spot = Simon, fileName = "Example.csv", quote = FALSE, sep =
"\t", col.names = FALSE, row.names = FALSE)

Description

Write the values for observations of an object of DataSet class in an output file. This values are writen in columns tab separated with the follow order: Cy3, Cy5, Cy3 Background, Cy5 Background, Ids and finally the z-score value. The header of the output file is the selected type for the z-score (ri or ma).

Usage

write.zscore(dataSet.spot, fileName, sep = "\t")

Arguments

Examples

data(WT.dataset)
write.zscore(dataSet.spot = WT.dataset, fileName = "Zscore.csv", sep =
"\t")

Description

This data set is a Microarray from the IFC.

Usage

data(WT.dataset)

Format

A vector containing 4036 observations.

Examples

data(WT.dataset)
Zscore.plot(WT.dataset)

Description

This function identify differential expressed genes by calculating an intensity-dependent Z-score. This function use a sliding window to calculate the mean and standard deviation within a window surrounding each data point, and define a Z-score where Z measures the number of standard deviations a data point is from the mean.

Usage

Zscore(spot.object,type,window.size)

Arguments

Value

A dataSet object with attributes Cy3, Cy5, Id, Z-score.

Examples

data(Simon)
# Background Correction
c.spot <- bg.correct(Simon)
#Normalized data
n.spot <- grid.norm(c.spot,23,24)
#Filter spot
f.spot <- filter.spot(n.spot)
#Replicate filtering
u.spot <- spotUnique(f.spot)
#Zscore analysis
s.spot <- Zscore(u.spot)

Description

This function allows to plot R-values vs I-values or M-values vs A-values for identifying differential expression.

Usage

Zscore.plot(dataSet.spot, Zscore.min, Zscore.max, all, col)

Arguments

See Also

colors()

Examples

data(WT.dataset)
Zscore.plot(WT.dataset, Zscore.min = 1, Zscore.max = 2)

Description

This function display the window that show the results after the Z-score. This window allow:

1. Show the plots of the up and down generated with the function Zscore.plot regulated spots in: Zscore < 1 sd 1 sd < Zscore < 1.5 sd 1.5 sd < Zscore < 2 sd Zscore > 2 sd and All the points

2. Save the plots in pdf and save the results in an output file

3. Gene annotations. Denote any gene information beyond the expression level data.

This is just a function for the GUI, and can not be used in the command line.

Usage

Zscore.points(type,text,envir, swap)

Arguments