Description Usage Arguments Details Value Warning Author(s) References See Also Examples
Display relevance associations network for (regularized) canonical
correlation analysis and (sparse) PLS regression. The function avoids the
intensive computation of Pearson correlation matrices on large data set by
calculating instead a pairwise similarity matrix directly obtained from the
latent components of our integrative approaches (CCA, PLS, block.pls
methods). The similarity value between a pair of variables is obtained by
calculating the sum of the correlations between the original variables and
each of the latent components of the model. The values in the similarity
matrix can be seen as a robust approximation of the Pearson correlation (see
González et al. 2012 for a mathematical demonstration and exact formula).
The advantage of relevance networks is their ability to simultaneously
represent positive and negative correlations, which are missed by methods
based on Euclidean distances or mutual information. Those networks are
bipartite and thus only a link between two variables of different types can
be represented. The network can be saved in a .glm format using the
igraph
package, the function write.graph
and extracting the
output object$gR
, see details.
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  network(
mat,
comp = NULL,
blocks = c(1, 2),
cutoff = 0,
row.names = TRUE,
col.names = TRUE,
block.var.names = TRUE,
color.node = NULL,
shape.node = NULL,
cex.node.name = 1,
color.edge = color.GreenRed(100),
lty.edge = "solid",
lwd.edge = 1,
show.edge.labels = FALSE,
cex.edge.label = 1,
show.color.key = TRUE,
symkey = TRUE,
keysize = c(1, 1),
keysize.label = 1,
breaks,
interactive = FALSE,
layout.fun = NULL,
save = NULL,
name.save = NULL
)

mat 
numeric matrix of values to be represented. Alternatively,
an object from one of the following models: 
comp 
atomic or vector of positive integers. The components to
adequately account for the data association. Defaults to 
blocks 
a vector indicating the block variables to display. 
cutoff 
numeric value between 
row.names, col.names 
character vector containing the names of X and Yvariables. 
block.var.names 
either a list of vector components for variable names in each block or FALSE for no names. If TRUE, the columns names of the blocks are used as names. 
color.node 
vector of length two, the colors of the X and Y nodes (see Details). 
shape.node 
character vector of length two, the shape of the X and Y nodes (see Details). 
cex.node.name 
the font size for the node labels. 
color.edge 
vector of colors or character string specifying the colors
function to using to color the edges, set to default to

lty.edge 
character vector of length two, the line type for the edges (see Details). 
lwd.edge 
vector of length two, the line width of the edges (see Details). 
show.edge.labels 
logical. If 
cex.edge.label 
the font size for the edge labels. 
show.color.key 
boolean. If 
symkey 
boolean indicating whether the color key should be made
symmetric about 0. Defaults to 
keysize 
numeric value indicating the size of the color key. 
keysize.label 
vector of length 1, indicating the size of the labels and title of the color key. 
breaks 
(optional) either a numeric vector indicating the splitting
points for binning 
interactive 
logical. If 
layout.fun 
a function. It specifies how the vertices will be placed on the graph. See help(layout) in the igraph package. Defaults to layout.fruchterman.reingold. 
save 
should the plot be saved ? If so, argument to be set either to

name.save 
character string giving the name of the saved file. 
network
allows to infer largescale association networks between the
X and Y datasets in rcc
or spls
. The output is a
graph where each X and Yvariable corresponds to a node and the
edges included in the graph portray associations between them.
In rcc
, to identify XY pairs showing relevant
associations, network
calculate a similarity measure between X
and Y variables in a pairwise manner: the scalar product value
between every pairs of vectors in dimension length(comp)
representing
the variables X and Y on the axis defined by Z_i with
i in comp
, where Z_i is the equiangular vector between
the ith X and Y canonical variate.
In spls
, if object$mode
is regression
, the similarity
measure between X and Y variables is given by the scalar product
value between every pairs of vectors in dimension length(comp)
representing the variables X and Y on the axis defined by
U_i with i in comp
, where U_i is the ith
X variate. If object$mode
is canonical
then X and
Y are represented on the axis defined by U_i and V_i
respectively.
Variable pairs with a high similarity measure (in absolute value) are considered as relevant. By changing the cutoff, one can tune the relevance of the associations to include or exclude relationships in the network.
interactive=TRUE
open two device, one for association network, one
for scrollbar, and define an interactive process: by clicking either at each
end ( or +) of the scrollbar or at middle portion of this.
The position of the slider indicate which is the ‘cutoff’ value associated
to the display network.
The network can be saved in a .glm format using the igraph package,
the function write.graph
and extracting the output obkect$gR
.
The interactive process is terminated by clicking the second button and
selecting Stop
from the menu, or from the Stop
menu on the graphics
window.
The color.node
is a vector of length two, of any of the three kind of
R
colors, i.e., either a color name (an element of colors()
),
a hexadecimal string of the form "#rrggbb"
, or an integer i
meaning palette()[i]
. color.node[1]
and color.node[2]
give the color for filled nodes of the X and Yvariables
respectively. Defaults to c("white", "white")
.
color.edge
give the color to edges with colors corresponding to the
values in mat
. Defaults to color.GreenRed(100)
for negative
(green) and positive (red) correlations. We also propose other palettes of
colors, such as color.jet
and color.spectral
, see help on
those functions, and examples below. Other palette of colors from the stats
package can be used too.
shape.node[1]
and shape.node[2]
provide the shape of the nodes
associate to X and Yvariables respectively. Current acceptable
values are "circle"
and "rectangle"
. Defaults to
c("circle", "rectangle")
.
lty.edge[1]
and lty.egde[2]
give the line type to edges with
positive and negative weight respectively. Can be one of "solid"
,
"dashed"
, "dotted"
, "dotdash"
, "longdash"
and
"twodash"
. Defaults to c("solid", "solid")
.
lwd.edge[1]
and lwd.edge[2]
provide the line width to edges
with positive and negative weight respectively. This attribute is of type
double with a default of c(1, 1)
.
network
return a list containing the following components:
M 
the correlation matrix used by 
gR 
a

If the number of variables is high, the generation of the network generation can take some time.
Ignacio González, KimAnh Lê Cao, AL J Abadi
Mathematical definition: González I., Lê Cao KA., Davis, M.J. and Déjean, S. (2012). Visualising associations between paired omics data sets. J. Data Mining 5:19. http://www.biodatamining.org/content/5/1/19/abstract
Examples and illustrations:
Rohart F, Gautier B, Singh A, Lê Cao KA. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752
Relevance networks:
Butte, A. J., Tamayo, P., Slonim, D., Golub, T. R. and Kohane, I. S. (2000). Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proceedings of the National Academy of Sciences of the USA 97, 1218212186.
Moriyama, M., Hoshida, Y., Otsuka, M., Nishimura, S., Kato, N., Goto, T., Taniguchi, H., Shiratori, Y., Seki, N. and Omata, M. (2003). Relevance Network between Chemosensitivity and Transcriptome in Human Hepatoma Cells. Molecular Cancer Therapeutics 2, 199205.
plotVar
, cim
,
color.GreenRed
, color.jet
,
color.spectral
and http: //www.mixOmics.org for more details.
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  ## network representation for objects of class 'rcc'
data(nutrimouse)
X < nutrimouse$lipid
Y < nutrimouse$gene
nutri.res < rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)
## Not run:
# may not work on the Linux version, use Windows instead
# sometimes with Rstudio might not work because of margin issues,
# in that case save it as an image
jpeg('example1network.jpeg', res = 600, width = 4000, height = 4000)
network(nutri.res, comp = 1:3, cutoff = 0.6)
dev.off()
## Changing the attributes of the network
# sometimes with Rstudio might not work because of margin issues,
# in that case save it as an image
jpeg('example2network.jpeg')
network(nutri.res, comp = 1:3, cutoff = 0.45,
color.node = c("mistyrose", "lightcyan"),
shape.node = c("circle", "rectangle"),
color.edge = color.jet(100),
lty.edge = "solid", lwd.edge = 2,
show.edge.labels = FALSE)
dev.off()
## interactive 'cutoff'
network(nutri.res, comp = 1:3, cutoff = 0.55, interactive = TRUE)
## select the 'cutoff' and "see" the new network
## network representation for objects of class 'spls'
data(liver.toxicity)
X < liver.toxicity$gene
Y < liver.toxicity$clinic
toxicity.spls < spls(X, Y, ncomp = 3, keepX = c(50, 50, 50),
keepY = c(10, 10, 10))
# sometimes with Rstudio might not work because of margin issues,
# in that case save it as an image
jpeg('example3network.jpeg')
network(toxicity.spls, comp = 1:3, cutoff = 0.8,
color.node = c("mistyrose", "lightcyan"),
shape.node = c("rectangle", "circle"),
color.edge = color.spectral(100),
lty.edge = "solid", lwd.edge = 1,
show.edge.labels = FALSE, interactive = FALSE)
dev.off()
## End(Not run)

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