Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/compareAnalysis.R
This function encompasses the comparison of several IcaSet objects using correlations and the plot of the corresponding correlation graph. The IcaSet objects are compared by calculating the correlation between either projection values of common features or genes, or contributions of common samples.
1 2 3 4 5 6 
icaSets 
List of 
labAn 
Vector of names for each icaSet, e.g the the names of the datasets on which were calculated the decompositions. 
type.corr 
Type of correlation to compute, either

cutoff_zval 
Either NULL or 0 (default) if all
genes are used to compute the correlation between the
components, or a threshold to compute the correlation
using the genes that have at least a scaled projection
higher than cutoff_zval. Will be used only when

level 
Data level of the 
fileNodeDescr 
File where node descriptions are saved (useful when the user wants to visualize the graph using Cytoscape). 
fileDataGraph 
File where graph description is saved (useful when the user wants to visualize the graph using Cytoscape). 
plot 
if 
title 
title of the graph 
col 
vector of colors indexed by elements of labAn; if missing, colors will be automatically attributed 
cutoff_graph 
the cutoff used to select pairs that will be included in the graph 
useMax 
if 
tkplot 
If TRUE, performs interactive plot with
function 
This function calls four functions:
compareAn
which computes the correlations,
compareAn2graphfile
which builds the graph,
nodeAttrs
which builds the node description
data, and plotCorGraph
which uses tkplot to
plot the graph in an interactive device.
If the user wants to see the correlation graph in
Cytoscape, he must fill the arguments
fileDataGraph
and fileNodeDescr
, in order
to import the graph and its node descriptions as a .txt
file in Cytoscape.
When labAn
is missing, each element i of
icaSets
is labeled as 'Ani'.
The user must carefully choose the data level used in the
comparison: If level='samples'
, the correlations
are based on the mixing matrices of the ICA
decompositions (of dimension samples x components).
'A'
will be typically chosen when the ICA
decompositions were computed on the same dataset, or on
datasets that include the same samples. If
level='features'
is chosen, the correlation is
calculated between the source matrices (of dimension
features x components) of the ICA decompositions.
'S'
will be typically used when the ICA
decompositions share common features (e.g same
microarrays). If level='genes'
, the correlations
are calculated on the attributes 'SByGene'
which
store the projections of the annotated features.
'SByGene'
will be typically chosen when ICA were
computed on datasets from different technologies, for
which comparison is possible only after annotation into a
common ID, like genes.
cutoff_zval
is only used when level
is one
of c('features','genes')
, in order to restrict the
correlation to the contributing features or genes.
When cutoff_zval
is specified, for each pair of
components, genes or features that are included in the
circle of center 0 and radius cutoff_zval
are
excluded from the computation of the correlation.
It must be taken into account by the user that if cutoff_zval is different from NULL or zero, the computation will be much slowler since each pair of component is treated individually.
Edges of the graph are built based on the correlation values between the components. Absolute values of correlations are used since components have no direction.
If useMax
is TRUE
each component will be
linked to only one component of each other IcaSet that
corresponds to the most correlated component among all
components of the same IcaSet. If cutoff_graph
is
specified, only correlations exceeding this value are
taken into account to build the graph. For example, if
cutoff
is 1, only relationships between components
that correspond to a correlation value higher than 1 will
be included. Absolute correlation values are used since
the components have no direction.
The contents of the returned list are
dataGraph
data.frame that
describes the correlation graph,
nodeAttrs
data.frame that
describes the node of the graph
graph
the graph as an igraphobject,
graphid
the id of the graph
plotted using tkplot.
A list consisting of
a data.frame defining the correlation graph
a data.frame describing the node of the graph,
the graph as an object of class
igraph
,
the id of the graph
plotted with tkplot
.
Anne Biton
compareAn2graphfile
,
compareAn
, cor2An
,
plotCorGraph
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 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55  dat1 < data.frame(matrix(rnorm(10000),ncol=10,nrow=1000))
rownames(dat1) < paste("g", 1:1000, sep="")
colnames(dat1) < paste("s", 1:10, sep="")
dat2 < data.frame(matrix(rnorm(10000),ncol=10,nrow=1000))
rownames(dat2) < paste("g", 1:1000, sep="")
colnames(dat2) < paste("s", 1:10, sep="")
## run ICA
resJade1 < runICA(X=dat1, nbComp=3, method = "JADE")
resJade2 < runICA(X=dat2, nbComp=3, method = "JADE")
## build params
params < buildMineICAParams(resPath="toy/")
## build IcaSet objects
icaSettoy1 < buildIcaSet(params=params, A=data.frame(resJade1$A), S=data.frame(resJade1$S),
dat=dat1, alreadyAnnot=TRUE)$icaSet
icaSettoy2 < buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(resJade2$S),
dat=dat2, alreadyAnnot=TRUE)$icaSet
## compare IcaSet objects
## use tkplot=TRUE to get an interactive graph
rescomp < runCompareIcaSets(icaSets=list(icaSettoy1, icaSettoy2), labAn=c("toy1","toy2"),
type.corr="pearson", level="genes", tkplot=FALSE)
## Not run:
## load the microarraybased gene expression datasets
## of breast tumors
library(breastCancerMAINZ)
library(breastCancerVDX)
data(mainz)
data(vdx)
## Define a function used to build two examples of IcaSet objects
## and annotate the probe sets into gene Symbols
treat < function(es, annot="hgu133a.db") {
es < selectFeatures_IQR(es,10000)
exprs(es) < t(apply(exprs(es),1,scale,scale=FALSE))
colnames(exprs(es)) < sampleNames(es)
resJade < runICA(X=exprs(es), nbComp=10, method = "JADE", maxit=10000)
resBuild < buildIcaSet(params=buildMineICAParams(), A=data.frame(resJade$A), S=data.frame(resJade$S),
dat=exprs(es), pData=pData(es), refSamples=character(0),
annotation=annot, typeID= typeIDmainz,
chipManu = "affymetrix", mart=mart)
icaSet < resBuild$icaSet
}
## Build the two IcaSet objects
icaSetMainz < treat(mainz)
icaSetVdx < treat(vdx)
## compare the IcaSets
runCompareIcaSets(icaSets=list(icaSetMainz, icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", level="genes")
## End(Not run)

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