\name{runCompareIcaSets}
\alias{runCompareIcaSets}
\title{runCompareIcaSets}
\usage{
runCompareIcaSets(icaSets, labAn,
type.corr = c("pearson", "spearman"), cutoff_zval = 0,
level = c("genes", "features", "samples"),
fileNodeDescr = NULL, fileDataGraph = NULL,
plot = TRUE, title = "", col, cutoff_graph = NULL,
useMax = TRUE, tkplot = FALSE)
}
\arguments{
\item{icaSets}{List of \code{\link{IcaSet}} objects, e.g
results of ICA decompositions obtained on several
datasets.}
\item{labAn}{Vector of names for each icaSet, e.g the the
names of the datasets on which were calculated the
decompositions.}
\item{type.corr}{Type of correlation to compute, either
\code{'pearson'} or \code{'spearman'}.}
\item{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
\code{level} is one of \code{c("features","genes")}.}
\item{level}{Data level of the \code{IcaSet} objects on
which is applied the correlation. It must correspond to a
data level shared by the IcaSet objects: \code{'samples'}
if they were applied to common samples (correlations are
computed between matrix \code{A}), \code{'features'} if
they were applied to common features (correlations are
computed between matrix \code{S}), \code{'genes'} if they
share gene IDs after annotation into genes (correlations
are computed between matrix \code{SByGene}).}
\item{fileNodeDescr}{File where node descriptions are
saved (useful when the user wants to visualize the graph
using Cytoscape).}
\item{fileDataGraph}{File where graph description is
saved (useful when the user wants to visualize the graph
using Cytoscape).}
\item{plot}{if \code{TRUE} (default) plot the correlation
graph}
\item{title}{title of the graph}
\item{col}{vector of colors indexed by elements of labAn;
if missing, colors will be automatically attributed}
\item{cutoff_graph}{the cutoff used to select pairs that
will be included in the graph}
\item{useMax}{if \code{TRUE}, the graph is restricted to
edges that correspond to maximum correlation between
components, see details}
\item{tkplot}{If TRUE, performs interactive plot with
function \code{tkplot}, else uses \code{plot.igraph}}
}
\value{
A list consisting of \describe{ \item{dataGraph:}{a
data.frame defining the correlation graph}
\item{nodeAttrs:}{a data.frame describing the node of the
graph,} \item{graph:}{the graph as an object of class
\code{igraph},} \item{graphid}{the id of the graph
plotted with \code{tkplot}}. }
}
\description{
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.
}
\details{
This function calls four functions:
\code{\link{compareAn}} which computes the correlations,
\code{\link{compareAn2graphfile}} which builds the graph,
\code{\link{nodeAttrs}} which builds the node description
data, and \code{\link{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
\code{fileDataGraph} and \code{fileNodeDescr}, in order
to import the graph and its node descriptions as a .txt
file in Cytoscape.
When \code{labAn} is missing, each element i of
\code{icaSets} is labeled as 'Ani'.
The user must carefully choose the data level used in the
comparison: If \code{level='samples'}, the correlations
are based on the mixing matrices of the ICA
decompositions (of dimension samples x components).
\code{'A'} will be typically chosen when the ICA
decompositions were computed on the same dataset, or on
datasets that include the same samples. If
\code{level='features'} is chosen, the correlation is
calculated between the source matrices (of dimension
features x components) of the ICA decompositions.
\code{'S'} will be typically used when the ICA
decompositions share common features (e.g same
microarrays). If \code{level='genes'}, the correlations
are calculated on the attributes \code{'SByGene'} which
store the projections of the annotated features.
\code{'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.
\code{cutoff_zval} is only used when \code{level} is one
of \code{c('features','genes')}, in order to restrict the
correlation to the contributing features or genes.
When \code{cutoff_zval} is specified, for each pair of
components, genes or features that are included in the
circle of center 0 and radius \code{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 \code{useMax} is \code{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 \code{cutoff_graph} is
specified, only correlations exceeding this value are
taken into account to build the graph. For example, if
\code{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 \describe{
\item{dataGraph:}{\code{dataGraph} data.frame that
describes the correlation graph,}
\item{nodeAttrs:}{\code{nodeAttrs} data.frame that
describes the node of the graph}
\item{graph}{\code{graph} the graph as an igraph-object,}
\item{graphid:}{\code{graphid} the id of the graph
plotted using tkplot.} }
}
\examples{
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)
\dontrun{
## load the microarray-based 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")
}
}
\author{
Anne Biton
}
\seealso{
\code{\link{compareAn2graphfile}},
\code{\link{compareAn}}, \code{\link{cor2An}},
\code{\link{plotCorGraph}}
}
