Description Usage Arguments Details Value Author(s) References See Also Examples
Performs CIA on two datasets as described by Culhane et al., 2003. Used for metaanalysis of two or more datasets.
1 2 3 4 
df1 
The first dataset. A 
df2 
The second dataset. A 
cia.nf 
Integer indicating the number of coinertia analysis axes to be saved. Default value is 2. 
cia.scan 
Logical indicating whether the coinertia analysis
eigenvalue (scree) plot should be shown so that the number of axes,

nsc 
A logical indicating whether coinertia analysis should be
performed using two nonsymmetric correspondence analyses 
x 
An object of class 
nlab 
Numeric. An integer indicating the number of variables (genes) to be labelled on plots. 
axis1 
Integer, the column number for the xaxis. The default is 1. 
axis2 
Integer, the column number for the yaxis. The default is 2. 
genecol 
Character, the colour of genes (variables). The default is "gray25". 
genelabels1, genelabels2 
A vector of variables labels, by default the row.names of each input matrix df1, and df2 are used. 
... 
further arguments passed to or from other methods. 
CIA has been successfully applied to the crossplatform comparison (metaanalysis) of microarray gene expression datasets (Culhane et al., 2003). Please refer to this paper and the vignette for help in interpretation of the output from CIA.
Coinertia analysis (CIA) is a multivariate method that identifies trends or corelationships
in multiple datasets which contain the same samples. That is the rows or columns of the matrix have to
be weighted similarly and thus must be "matchable". In cia
, it is assumed that the analysis is being performed
on the microarray cases, and thus the columns will be matched between the 2 datasets. Thus please
ensure that the order of cases (the columns) in df1 and df2 are equivalent before performing CIA.
CIA simultaneously finds ordinations (dimension reduction diagrams) from the datasets that are most similar. It does this by finding successive axes from the two datasets with maximum covariance. CIA can be applied to datasets where the number of variables (genes) far exceeds the number of samples (arrays) such is the case with microarray analyses.
cia
calls coinertia
in the ADE4 package. For more information on
coinertia analysis please refer to coinertia
and several recent reviews (see below).
In the paper by Culhane et al., 2003, the datasets df1 and df2 are transformed using COA and Row weighted COA respectively, before coinertia analysis. It is now recommended to perform non symmetric correspondence analysis (NSC) rather than correspondence analysis (COA) on both datasets.
The RV coefficient
In the results, in the object cia
returned by the analysis, \$coinertia\$RV gives the RV coefficient.
This is a measure of global similarity between the datasets, and is a number between 0 and 1. The closer it
is to 1 the greater the global similarity between the two datasets.
Plotting and visualising cia results
plot.cia
draws 3 plots.
The first plot uses S.match.col
to plots the projection (normalised scores \$mY
and \$mX) of the samples
from each dataset onto the one space. Cases (microarray samples) from one dataset are represented by circles,
and cases from the second dataset are represented by arrow tips. Each circle and arrow is joined by a line,
where the length of the line is proportional to the divergence between the gene expression profiles of that
sample in the two datasets. A short line shows good agreement between the two
datasets.
The second two plots call plot.genes
are show the projection of the variables (genes, \$li and \$co)
from each dataset in the new space. It is important to note both the direction of project of Variables
(genes) and cases (microarray samples). Variables and cases that are projected in the same direction
from the origin have a positive correlation (ie those genes are upregulated in those microarray samples)
Please refer to the help on bga
for further discussion on graphing and visualisation
functions in MADE4.
An object of the class cia
which contains a list of length 4.
call 
list of input arguments, df1 and df2 
coinertia 
A object of class "coinertia", subclass 
coa1 
Returns an object of class "coa" or "nsc", with subclass

coa2 
Returns an object of class "coa" or "nsc", with subclass 
Aedin Culhane
Culhane AC, et al., 2003 Cross platform comparison and visualisation of gene expression data using coinertia analysis. BMC Bioinformatics. 4:59
See also coinertia
, plot.cia
.
CIA and multiple CIA is also implemented in Bioconductor packages omicade4 and mogsa
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  data(NCI60)
print("This will take a few minutes, please wait...")
if (require(ade4, quiet = TRUE)) {
# Example data are "G1_Ross_1375.txt" and "G5_Affy_1517.txt"
coin < cia(NCI60$Ross, NCI60$Affy)
}
attach(coin)
summary(coin)
summary(coin$coinertia)
# $coinertia$RV will give the RVcoefficient, the greater (scale 01) the better
cat(paste("The RV coefficient is a measure of global similarity between the datasets.\n",
"The two datasets analysed are very similar. ",
"The RV coefficient of this coinertia analysis is: ", coin$coinertia$RV,"\n", sep= ""))
plot(coin)
plot(coin, classvec=NCI60$classes[,2], clab=0, cpoint=3)

Loading required package: ade4
Loading required package: RColorBrewer
Loading required package: gplots
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
Loading required package: scatterplot3d
[1] "This will take a few minutes, please wait..."
Length Class Mode
call 3 none call
coinertia 18 coinertia list
coa1 11 transpo list
coa2 11 transpo list
Coinertia analysis
Class: coinertia dudi
Call: coinertia(dudiX = coa1, dudiY = coa2, scannf = cia.scan, nf = cia.nf)
Total inertia: 4.876e05
Eigenvalues:
Ax1 Ax2 Ax3 Ax4 Ax5
2.266e05 9.904e06 4.342e06 2.335e06 1.576e06
Projected inertia (%):
Ax1 Ax2 Ax3 Ax4 Ax5
46.476 20.312 8.904 4.789 3.233
Cumulative projected inertia (%):
Ax1 Ax1:2 Ax1:3 Ax1:4 Ax1:5
46.48 66.79 75.69 80.48 83.71
(Only 5 dimensions (out of 59) are shown)
Eigenvalues decomposition:
eig covar sdX sdY corr
1 2.266249e05 0.004760514 0.08187720 0.06339248 0.9171769
2 9.904435e06 0.003147131 0.06862156 0.04851708 0.9452782
Inertia & coinertia X (coa1):
inertia max ratio
1 0.006703875 0.007109399 0.9429596
12 0.011412793 0.011693125 0.9760259
Inertia & coinertia Y (coa2):
inertia max ratio
1 0.004018607 0.004184413 0.9603752
12 0.006372514 0.006530483 0.9758105
RV:
0.7859656
The RV coefficient is a measure of global similarity between the datasets.
The two datasets analysed are very similar. The RV coefficient of this coinertia analysis is: 0.785965616408392
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