cia(df1, df2, cia.nf=2, cia.scan=FALSE, nsc=TRUE,...)
"plot"(x, nlab = 10, axis1 = 1, axis2 = 2, genecol = "gray25", genelabels1 = rownames(ciares$co), genelabels2 = rownames(ciares$li), ...)matrix, data.frame,
ExpressionSet or
marrayRaw-class.
If the input is gene expression data in a matrix or data.frame. The
rows and columns are expected to contain the variables (genes) and cases (array samples)
respectively.matrix, data.frame,
ExpressionSet or
marrayRaw-class.
If the input is gene expression data in a matrix or data.frame. The
rows and columns are expected to contain the variables (genes) and cases (array samples)
respectively.cia.nf can be selected interactively. Default value is FALSE.dudi.nsc.
The default=TRUE is highly recommended. If FALSE, COA dudi.coa
will be performed on df1, and row weighted COA dudi.rwcoa
will be performed on df2 using the row weights from df1.cia, containing the CIA projected coordinates to be plotted.cia which contains a list of length 4.
dudi. See
coinertiadudi. See dudi.coa or dudi.nscdudi.
See dudi.coa or dudi.nscCo-inertia analysis (CIA) is a multivariate method that identifies trends or co-relationships
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.
coinertia, plot.ciadata(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 RV-coefficient, the greater (scale 0-1) 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)
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