cim: Clustered Image Maps (CIMs) ("heat maps")
In mixOmics: Omics Data Integration Project
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
This function generates color-coded Clustered Image Maps (CIMs) ("heat maps") to
represent "high-dimensional" data sets.
Usage
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cim(mat,
color = NULL,
row.names = TRUE,
col.names = TRUE,
row.sideColors = NULL,
col.sideColors = NULL,
row.cex = NULL,
col.cex = NULL,
threshold = 0,
cluster = "both",
dist.method = c("euclidean", "euclidean"),
clust.method = c("complete", "complete"),
cut.tree = c(0, 0),
transpose = FALSE,
symkey = TRUE,
keysize = c(1, 1),
keysize.label = 1,
zoom = FALSE,
title = NULL,
xlab = NULL,
ylab = NULL,
margins = c(5, 5),
lhei = NULL,
lwid = NULL,
comp=NULL,
center = TRUE,
scale = FALSE,
mapping = "XY",
legend= NULL,
save = NULL,
name.save = NULL)
Arguments
mat
numeric matrix of values to be plotted. Alternatively, an object of class inheriting from "pca", "spca", "ipca", "sipca", "rcc", "pls", "spls", "plsda", "splsda", "mlspls" or "mlsplsda" (where "ml" stands for multilevel).
color
a character vector of colors such as that generated by terrain.colors,
topo.colors, rainbow, color.jet or similar functions.
row.names, col.names
logical, should the name of rows and/or columns of mat be shown? If TRUE
(defaults) rownames(mat) and/or colnames(mat) are used. Possible character vectors with
row and/or column labels can be used.
row.sideColors
(optional) character vector of length nrow(mat) containing the color
names for a vertical side bar that may be used to annotate the rows of mat.
col.sideColors
(optional) character vector of length ncol(mat) containing
the color names for a horizontal side bar that may be used to annotate the columns of mat.
row.cex, col.cex
positive numbers, used as cex.axis in for the row or column
axis labeling. The defaults currently only use number of rows or columns, respectively.
mapping
character string indicating whether to map "X",
"Y" or "XY"-association matrix. See Details.
cluster
character string indicating whether to cluster "none", "row",
"column" or "both". Defaults to "both".
dist.method
character vector of length two. The distance measure used in
clustering rows and columns. Possible values are "correlation" for Pearson
correlation and all the distances supported by dist, such as
"euclidean", etc.
clust.method
character vector of length two. The agglomeration method to be used for rows and columns.
Accepts the same values as in hclust such as "ward", "complete", etc.
cut.tree
numeric vector of length two with components in [0,1]. The height proportions where
the trees should be cut for rows and columns, if these are clustered.
comp
atomic or vector of positive integers. The components to adequately account
for the data association. For a non sparse method, the similarity matrix is computed based on the variates and loading vectors of those specified components. For a sparse approach, the similarity matric is computed based on the variables selected on those specified components. See example. Defaults to comp = 1:object$ncomp.
transpose
logical indicating if the matrix should be transposed for plotting.
Defaults to FALSE.
center
either a logical value or a numeric vector of length equal to the
number of columns of mat. See scale function.
scale
either a logical value or a numeric vector of length equal to the
number of columns of mat. See scale function.
threshold
numeric between 0 and 1. Variables with correlations below this threshold in absolute value are not plotted. To use only when mapping is "XY".
symkey
boolean indicating whether the color key should be made symmetric about 0.
Defaults to TRUE.
keysize
vector of length two, 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.
zoom
logical. Whether to use zoom for interactive zoom. See Details.
title, xlab, ylab
title, x- and y-axis titles; default to none.
margins
numeric vector of length two containing the margins (see par(mar))
for column and row names respectively.
lhei, lwid
arguments passed to layout to divide the device up into two
(or three if a side color is drawn) rows and two columns, with the row-heights lhei
and the column-widths lwid.
legend
A list indicating the legend for each group, the color vector, title of the legend and cex.
save
should the plot be saved? If so, argument to be set to either 'jpeg', 'tiff', 'png' or 'pdf'.
name.save
character string for the name of the file to be saved.
Details
One matrix Clustered Image Map (default method) is a 2-dimensional visualization
of a real-valued matrix (basically image(t(mat))) with rows and/or
columns reordered according to some hierarchical clustering method to identify
interesting patterns. Generated dendrograms from clustering are added to the left
side and to the top of the image. By default the used clustering method for rows
and columns is the complete linkage method and the used distance measure
is the distance euclidean.
In "pca", "spca", "ipca", "sipca", "plsda",
"splsda" and multilevel variants methods the mat matrix is object$X.
For the remaining methods, if mapping = "X" or mapping = "Y" the
mat matrix is object$X or object$Y respectively. If mapping = "XY":
in rcc method, the matrix mat is created where element (j,k)
is the scalar product value between every pairs of vectors in dimension
length(comp) representing the variables X_j and Y_k on the
axis defined by Z_i with i in comp, where Z_i is the
equiangular vector between the i-th X and Y canonical variate.
in pls, spls and multilevel spls methods, if object$mode is
"regression", the element (j,k) of the matrix mat
is given by the scalar product value between every pairs of vectors in dimension
length(comp) representing the variables X_j and Y_k on the axis
defined by U_i with i in comp, where U_i is the i-th
X variate. If object$mode is "canonical" then X_j and
Y_k are represented on the axis defined by U_i and V_i respectively.
By default four components will be displayed in the plot. At the top left is the
color key, top right is the column dendogram, bottom left is the row dendogram,
bottom right is the image plot. When sideColors are provided, an
additional row or column is inserted in the appropriate location. This layout can
be overriden by specifiying appropriate values for lwid and lhei.
lwid controls the column width, and lhei controls the row height.
See the help page for layout for details on how to use these arguments.
For visualization of "high-dimensional" data sets, a nice zooming tool was created.
zoom = TRUE open a new device, one for CIM, one for zoom-out region and
define an interactive 'zoom' process: click two points at imagen map region by
pressing the first mouse button. It then draws a rectangle around the selected
region and zoom-out this at new device. The process can be repeated to zoom-out
other regions of interest.
The zoom process is terminated by clicking the second button and selecting 'Stop'
from the menu, or from the 'Stop' menu on the graphics window.
Value
A list containing the following components:
M
the mapped matrix used by cim.
rowInd, colInd
row and column index permutation vectors as returned
by order.dendrogram.
ddr, ddc
object of class "dendrogram" which describes the row and
column trees produced by cim.
mat.cor
the correlation matrix used for the heatmap. Available only when mapping = "XY".
row.names, col.names
character vectors with row and column labels used.
row.sideColors, col.sideColors
character vector containing the color
names for vertical and horizontal side bars used to annotate the rows and columns.
Author(s)
Ignacio González, Francois Bartolo, Kim-Anh Lê Cao.
References
Eisen, M. B., Spellman, P. T., Brown, P. O. and Botstein, D. (1998).
Cluster analysis and display of genome-wide expression patterns.
Proceeding of the National Academy of Sciences of the USA 95, 14863-14868.
Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace Jr., A. J.,
Kohn, K. W., Fojo, T., Bates, S. E., Rubinstein, L. V., Anderson, N. L.,
Buolamwini, J. K., van Osdol, W. W., Monks, A. P., Scudiero, D. A., Sausville, E. A.,
Zaharevitz, D. W., Bunow, B., Viswanadhan, V. N., Johnson, G. S., Wittes, R. E.
and Paull, K. D. (1997). An information-intensive approach to the molecular
pharmacology of cancer. Science 275, 343-349.
González I., Lê Cao K.A., Davis M.J., Déjean S. (2012). Visualising associations between
paired 'omics' data sets. BioData Mining; 5(1).
mixOmics article:
Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752
See Also
heatmap,
hclust, plotVar,
network and
http://mixomics.org/graphics/ for more details on all options available.
Examples
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## default method: shows cross correlation between 2 data sets
#------------------------------------------------------------------
data(nutrimouse)
X <- nutrimouse$lipid
Y <- nutrimouse$gene
cim(cor(X, Y), cluster = "none")
## CIM representation for objects of class 'rcc'
#------------------------------------------------------------------
## Not run:
nutri.rcc <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)
cim(nutri.rcc, xlab = "genes", ylab = "lipids", margins = c(5, 6))
## End(Not run)
#-- interactive 'zoom' available as below
## Not run:
cim(nutri.rcc, xlab = "genes", ylab = "lipids", margins = c(5, 6),
zoom = TRUE)
#-- select the region and "see" the zoom-out region
#-- cim from X matrix with a side bar to indicate the diet
diet.col <- palette()[as.numeric(nutrimouse$diet)]
cim(nutri.rcc, mapping = "X", row.names = nutrimouse$diet,
row.sideColors = diet.col, xlab = "lipids",
clust.method = c("ward", "ward"), margins = c(6, 4))
#-- cim from Y matrix with a side bar to indicate the genotype
geno.col = color.mixo(as.numeric(nutrimouse$genotype))
cim(nutri.rcc, mapping = "Y", row.names = nutrimouse$genotype,
row.sideColors = geno.col, xlab = "genes",
clust.method = c("ward", "ward"))
#-- save the result as a jpeg file
jpeg(filename = "test.jpeg", res = 600, width = 4000, height = 4000)
cim(nutri.rcc, xlab = "genes", ylab = "lipids", margins = c(5, 6))
dev.off()
## End(Not run)
## CIM representation for objects of class 'spca' (also works for sipca)
#------------------------------------------------------------------
## Not run:
data(liver.toxicity)
X <- liver.toxicity$gene
liver.spca <- spca(X, ncomp = 2, keepX = c(30, 30), scale = FALSE)
dose.col <- color.mixo(as.numeric(as.factor(liver.toxicity$treatment[, 3])))
# side bar, no variable names shown
cim(liver.spca, row.sideColors = dose.col, col.names = FALSE,
row.names = liver.toxicity$treatment[, 3],
clust.method = c("ward", "ward"))
## End(Not run)
## CIM representation for objects of class '(s)pls'
#------------------------------------------------------------------
## Not run:
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
liver.spls <- spls(X, Y, ncomp = 3,
keepX = c(20, 50, 50), keepY = c(10, 10, 10))
# default
cim(liver.spls)
# transpose matrix, choose clustering method
cim(liver.spls, transpose = TRUE,
clust.method = c("ward", "ward"), margins = c(5, 7))
# Here we visualise only the X variables selected
cim(liver.spls, mapping="X")
# Here we should visualise only the Y variables selected
cim(liver.spls, mapping="Y")
# Here we only visualise the similarity matrix between the variables by spls
cim(liver.spls, cluster="none")
# plotting two data sets with the similarity matrix as input in the funciton
# (see our BioData Mining paper for more details)
# Only the variables selected by the sPLS model in X and Y are represented
cim(liver.spls, mapping="XY")
# on the X matrix only, side col var to indicate dose
dose.col <- color.mixo(as.numeric(as.factor(liver.toxicity$treatment[, 3])))
cim(liver.spls, mapping = "X", row.sideColors = dose.col,
row.names = liver.toxicity$treatment[, 3])
# CIM default representation includes the total of 120 genes selected, with the dose color
# with a sparse method, show only the variables selected on specific components
cim(liver.spls, comp = 1)
cim(liver.spls, comp = 2)
cim(liver.spls, comp = c(1,2))
cim(liver.spls, comp = c(1,3))
## End(Not run)
## CIM representation for objects of class '(s)plsda'
#------------------------------------------------------------------
## Not run:
data(liver.toxicity)
X <- liver.toxicity$gene
# Setting up the Y outcome first
Y <- liver.toxicity$treatment[, 3]
#set up colors for cim
dose.col <- color.mixo(as.numeric(as.factor(liver.toxicity$treatment[, 3])))
liver.splsda <- splsda(X, Y, ncomp = 2, keepX = c(40, 30))
cim(liver.splsda, row.sideColors = dose.col, row.names = Y)
## End(Not run)
## CIM representation for objects of class splsda 'multilevel'
# with a two level factor (repeated sample and time)
#------------------------------------------------------------------
data(vac18.simulated)
X <- vac18.simulated$genes
design <- data.frame(samp = vac18.simulated$sample)
Y = data.frame(time = vac18.simulated$time,
stim = vac18.simulated$stimulation)
res.2level <- splsda(X, Y = Y, ncomp = 2, multilevel = design,
keepX = c(120, 10))
#define colors for the levels: stimulation and time
stim.col <- c("darkblue", "purple", "green4","red3")
stim.col <- stim.col[as.numeric(Y$stim)]
time.col <- c("orange", "cyan")[as.numeric(Y$time)]
# The row side bar indicates the two levels of the facteor, stimulation and time.
# the sample names have been motified on the plot.
cim(res.2level, row.sideColors = cbind(stim.col, time.col),
row.names = paste(Y$time, Y$stim, sep = "_"),
col.names = FALSE,
#setting up legend:
legend=list(legend = c(levels(Y$time), levels(Y$stim)),
col = c("orange", "cyan", "darkblue", "purple", "green4","red3"),
title = "Condition", cex = 0.7)
)
## CIM representation for objects of class spls 'multilevel'
#------------------------------------------------------------------
data(liver.toxicity)
repeat.indiv <- c(1, 2, 1, 2, 1, 2, 1, 2, 3, 3, 4, 3, 4, 3, 4, 4, 5, 6, 5, 5,
6, 5, 6, 7, 7, 8, 6, 7, 8, 7, 8, 8, 9, 10, 9, 10, 11, 9, 9,
10, 11, 12, 12, 10, 11, 12, 11, 12, 13, 14, 13, 14, 13, 14,
13, 14, 15, 16, 15, 16, 15, 16, 15, 16)
# sPLS is a non supervised technique, and so we only indicate the sample repetitions
# in the design (1 factor only here, sample)
# sPLS takes as an input 2 data sets, and the variables selected
design <- data.frame(sample = repeat.indiv)
res.spls.1level <- spls(X = liver.toxicity$gene,
Y=liver.toxicity$clinic,
multilevel = design,
ncomp = 2,
keepX = c(50, 50), keepY = c(5, 5),
mode = 'canonical')
stim.col <- c("darkblue", "purple", "green4","red3")
# showing only the Y variables, and only those selected in comp 1
cim(res.spls.1level, mapping="Y",
row.sideColors = stim.col[factor(liver.toxicity$treatment[,3])], comp = 1,
#setting up legend:
legend=list(legend = unique(liver.toxicity$treatment[,3]), col=stim.col,
title = "Dose", cex=0.9))
## Not run:
# showing only the X variables, for all selected on comp 1 and 2
cim(res.spls.1level, mapping="X",
row.sideColors = stim.col[factor(liver.toxicity$treatment[,3])],
#setting up legend:
legend=list(legend = unique(liver.toxicity$treatment[,3]), col=stim.col,
title = "Dose", cex=0.9))
# These are the cross correlations between the variables selected in X and Y.
# The similarity matrix is obtained as in our paper in Data Mining
cim(res.spls.1level, mapping="XY")
## End(Not run)
Example output
Loading required package: MASS
Loading required package: lattice
Loading required package: ggplot2
Loaded mixOmics 6.1.3
Visit http://www.mixOmics.org for more details about our methods.
Any bug reports or comments? Notify us at mixomics at math.univ-toulouse.fr or https://bitbucket.org/klecao/package-mixomics/issues
Thank you for using mixOmics!
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
pdf
2
Splitting the variation for 2 level factors.
Splitting the variation for 1 level factor.
Splitting the variation for 1 level factor.
mixOmics documentation built on June 1, 2018, 5:06 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
This function generates color-coded Clustered Image Maps (CIMs) ("heat maps") to represent "high-dimensional" data sets.
Usage
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 | cim(mat,
color = NULL,
row.names = TRUE,
col.names = TRUE,
row.sideColors = NULL,
col.sideColors = NULL,
row.cex = NULL,
col.cex = NULL,
threshold = 0,
cluster = "both",
dist.method = c("euclidean", "euclidean"),
clust.method = c("complete", "complete"),
cut.tree = c(0, 0),
transpose = FALSE,
symkey = TRUE,
keysize = c(1, 1),
keysize.label = 1,
zoom = FALSE,
title = NULL,
xlab = NULL,
ylab = NULL,
margins = c(5, 5),
lhei = NULL,
lwid = NULL,
comp=NULL,
center = TRUE,
scale = FALSE,
mapping = "XY",
legend= NULL,
save = NULL,
name.save = NULL)
|
Arguments
mat |
numeric matrix of values to be plotted. Alternatively, an object of class inheriting from |
color |
a character vector of colors such as that generated by |
row.names, col.names |
logical, should the name of rows and/or columns of |
row.sideColors |
(optional) character vector of length |
col.sideColors |
(optional) character vector of length |
row.cex, col.cex |
positive numbers, used as |
mapping |
character string indicating whether to map |
cluster |
character string indicating whether to cluster |
dist.method |
character vector of length two. The distance measure used in
clustering rows and columns. Possible values are |
clust.method |
character vector of length two. The agglomeration method to be used for rows and columns.
Accepts the same values as in |
cut.tree |
numeric vector of length two with components in [0,1]. The height proportions where the trees should be cut for rows and columns, if these are clustered. |
comp |
atomic or vector of positive integers. The components to adequately account
for the data association. For a non sparse method, the similarity matrix is computed based on the variates and loading vectors of those specified components. For a sparse approach, the similarity matric is computed based on the variables selected on those specified components. See example. Defaults to |
transpose |
logical indicating if the matrix should be transposed for plotting.
Defaults to |
center |
either a logical value or a numeric vector of length equal to the
number of columns of |
scale |
either a logical value or a numeric vector of length equal to the
number of columns of |
threshold |
numeric between 0 and 1. Variables with correlations below this threshold in absolute value are not plotted. To use only when mapping is "XY". |
symkey |
boolean indicating whether the color key should be made symmetric about 0.
Defaults to |
keysize |
vector of length two, 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. |
zoom |
logical. Whether to use zoom for interactive zoom. See Details. |
title, xlab, ylab |
title, x- and y-axis titles; default to none. |
margins |
numeric vector of length two containing the margins (see |
lhei, lwid |
arguments passed to |
legend |
A list indicating the legend for each group, the color vector, title of the legend and cex. |
save |
should the plot be saved? If so, argument to be set to either |
name.save |
character string for the name of the file to be saved. |
Details
One matrix Clustered Image Map (default method) is a 2-dimensional visualization
of a real-valued matrix (basically image(t(mat))) with rows and/or
columns reordered according to some hierarchical clustering method to identify
interesting patterns. Generated dendrograms from clustering are added to the left
side and to the top of the image. By default the used clustering method for rows
and columns is the complete linkage method and the used distance measure
is the distance euclidean.
In "pca", "spca", "ipca", "sipca", "plsda",
"splsda" and multilevel variants methods the mat matrix is object$X.
For the remaining methods, if mapping = "X" or mapping = "Y" the
mat matrix is object$X or object$Y respectively. If mapping = "XY":
in
rccmethod, the matrixmatis created where element (j,k) is the scalar product value between every pairs of vectors in dimensionlength(comp)representing the variables X_j and Y_k on the axis defined by Z_i with i incomp, where Z_i is the equiangular vector between the i-th X and Y canonical variate.in
pls,splsand multilevel spls methods, ifobject$modeis"regression", the element (j,k) of the matrixmatis given by the scalar product value between every pairs of vectors in dimensionlength(comp)representing the variables X_j and Y_k on the axis defined by U_i with i incomp, where U_i is the i-th X variate. Ifobject$modeis"canonical"then X_j and Y_k are represented on the axis defined by U_i and V_i respectively.
By default four components will be displayed in the plot. At the top left is the
color key, top right is the column dendogram, bottom left is the row dendogram,
bottom right is the image plot. When sideColors are provided, an
additional row or column is inserted in the appropriate location. This layout can
be overriden by specifiying appropriate values for lwid and lhei.
lwid controls the column width, and lhei controls the row height.
See the help page for layout for details on how to use these arguments.
For visualization of "high-dimensional" data sets, a nice zooming tool was created.
zoom = TRUE open a new device, one for CIM, one for zoom-out region and
define an interactive 'zoom' process: click two points at imagen map region by
pressing the first mouse button. It then draws a rectangle around the selected
region and zoom-out this at new device. The process can be repeated to zoom-out
other regions of interest.
The zoom process is terminated by clicking the second button and selecting 'Stop' from the menu, or from the 'Stop' menu on the graphics window.
Value
A list containing the following components:
M |
the mapped matrix used by |
rowInd, colInd |
row and column index permutation vectors as returned
by |
ddr, ddc |
object of class |
mat.cor |
the correlation matrix used for the heatmap. Available only when mapping = "XY". |
row.names, col.names |
character vectors with row and column labels used. |
row.sideColors, col.sideColors |
character vector containing the color names for vertical and horizontal side bars used to annotate the rows and columns. |
Author(s)
Ignacio González, Francois Bartolo, Kim-Anh Lê Cao.
References
Eisen, M. B., Spellman, P. T., Brown, P. O. and Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceeding of the National Academy of Sciences of the USA 95, 14863-14868.
Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace Jr., A. J., Kohn, K. W., Fojo, T., Bates, S. E., Rubinstein, L. V., Anderson, N. L., Buolamwini, J. K., van Osdol, W. W., Monks, A. P., Scudiero, D. A., Sausville, E. A., Zaharevitz, D. W., Bunow, B., Viswanadhan, V. N., Johnson, G. S., Wittes, R. E. and Paull, K. D. (1997). An information-intensive approach to the molecular pharmacology of cancer. Science 275, 343-349.
González I., Lê Cao K.A., Davis M.J., Déjean S. (2012). Visualising associations between paired 'omics' data sets. BioData Mining; 5(1).
mixOmics article:
Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752
See Also
heatmap,
hclust, plotVar,
network and
http://mixomics.org/graphics/ for more details on all options available.
Examples
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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | ## default method: shows cross correlation between 2 data sets
#------------------------------------------------------------------
data(nutrimouse)
X <- nutrimouse$lipid
Y <- nutrimouse$gene
cim(cor(X, Y), cluster = "none")
## CIM representation for objects of class 'rcc'
#------------------------------------------------------------------
## Not run:
nutri.rcc <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)
cim(nutri.rcc, xlab = "genes", ylab = "lipids", margins = c(5, 6))
## End(Not run)
#-- interactive 'zoom' available as below
## Not run:
cim(nutri.rcc, xlab = "genes", ylab = "lipids", margins = c(5, 6),
zoom = TRUE)
#-- select the region and "see" the zoom-out region
#-- cim from X matrix with a side bar to indicate the diet
diet.col <- palette()[as.numeric(nutrimouse$diet)]
cim(nutri.rcc, mapping = "X", row.names = nutrimouse$diet,
row.sideColors = diet.col, xlab = "lipids",
clust.method = c("ward", "ward"), margins = c(6, 4))
#-- cim from Y matrix with a side bar to indicate the genotype
geno.col = color.mixo(as.numeric(nutrimouse$genotype))
cim(nutri.rcc, mapping = "Y", row.names = nutrimouse$genotype,
row.sideColors = geno.col, xlab = "genes",
clust.method = c("ward", "ward"))
#-- save the result as a jpeg file
jpeg(filename = "test.jpeg", res = 600, width = 4000, height = 4000)
cim(nutri.rcc, xlab = "genes", ylab = "lipids", margins = c(5, 6))
dev.off()
## End(Not run)
## CIM representation for objects of class 'spca' (also works for sipca)
#------------------------------------------------------------------
## Not run:
data(liver.toxicity)
X <- liver.toxicity$gene
liver.spca <- spca(X, ncomp = 2, keepX = c(30, 30), scale = FALSE)
dose.col <- color.mixo(as.numeric(as.factor(liver.toxicity$treatment[, 3])))
# side bar, no variable names shown
cim(liver.spca, row.sideColors = dose.col, col.names = FALSE,
row.names = liver.toxicity$treatment[, 3],
clust.method = c("ward", "ward"))
## End(Not run)
## CIM representation for objects of class '(s)pls'
#------------------------------------------------------------------
## Not run:
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
liver.spls <- spls(X, Y, ncomp = 3,
keepX = c(20, 50, 50), keepY = c(10, 10, 10))
# default
cim(liver.spls)
# transpose matrix, choose clustering method
cim(liver.spls, transpose = TRUE,
clust.method = c("ward", "ward"), margins = c(5, 7))
# Here we visualise only the X variables selected
cim(liver.spls, mapping="X")
# Here we should visualise only the Y variables selected
cim(liver.spls, mapping="Y")
# Here we only visualise the similarity matrix between the variables by spls
cim(liver.spls, cluster="none")
# plotting two data sets with the similarity matrix as input in the funciton
# (see our BioData Mining paper for more details)
# Only the variables selected by the sPLS model in X and Y are represented
cim(liver.spls, mapping="XY")
# on the X matrix only, side col var to indicate dose
dose.col <- color.mixo(as.numeric(as.factor(liver.toxicity$treatment[, 3])))
cim(liver.spls, mapping = "X", row.sideColors = dose.col,
row.names = liver.toxicity$treatment[, 3])
# CIM default representation includes the total of 120 genes selected, with the dose color
# with a sparse method, show only the variables selected on specific components
cim(liver.spls, comp = 1)
cim(liver.spls, comp = 2)
cim(liver.spls, comp = c(1,2))
cim(liver.spls, comp = c(1,3))
## End(Not run)
## CIM representation for objects of class '(s)plsda'
#------------------------------------------------------------------
## Not run:
data(liver.toxicity)
X <- liver.toxicity$gene
# Setting up the Y outcome first
Y <- liver.toxicity$treatment[, 3]
#set up colors for cim
dose.col <- color.mixo(as.numeric(as.factor(liver.toxicity$treatment[, 3])))
liver.splsda <- splsda(X, Y, ncomp = 2, keepX = c(40, 30))
cim(liver.splsda, row.sideColors = dose.col, row.names = Y)
## End(Not run)
## CIM representation for objects of class splsda 'multilevel'
# with a two level factor (repeated sample and time)
#------------------------------------------------------------------
data(vac18.simulated)
X <- vac18.simulated$genes
design <- data.frame(samp = vac18.simulated$sample)
Y = data.frame(time = vac18.simulated$time,
stim = vac18.simulated$stimulation)
res.2level <- splsda(X, Y = Y, ncomp = 2, multilevel = design,
keepX = c(120, 10))
#define colors for the levels: stimulation and time
stim.col <- c("darkblue", "purple", "green4","red3")
stim.col <- stim.col[as.numeric(Y$stim)]
time.col <- c("orange", "cyan")[as.numeric(Y$time)]
# The row side bar indicates the two levels of the facteor, stimulation and time.
# the sample names have been motified on the plot.
cim(res.2level, row.sideColors = cbind(stim.col, time.col),
row.names = paste(Y$time, Y$stim, sep = "_"),
col.names = FALSE,
#setting up legend:
legend=list(legend = c(levels(Y$time), levels(Y$stim)),
col = c("orange", "cyan", "darkblue", "purple", "green4","red3"),
title = "Condition", cex = 0.7)
)
## CIM representation for objects of class spls 'multilevel'
#------------------------------------------------------------------
data(liver.toxicity)
repeat.indiv <- c(1, 2, 1, 2, 1, 2, 1, 2, 3, 3, 4, 3, 4, 3, 4, 4, 5, 6, 5, 5,
6, 5, 6, 7, 7, 8, 6, 7, 8, 7, 8, 8, 9, 10, 9, 10, 11, 9, 9,
10, 11, 12, 12, 10, 11, 12, 11, 12, 13, 14, 13, 14, 13, 14,
13, 14, 15, 16, 15, 16, 15, 16, 15, 16)
# sPLS is a non supervised technique, and so we only indicate the sample repetitions
# in the design (1 factor only here, sample)
# sPLS takes as an input 2 data sets, and the variables selected
design <- data.frame(sample = repeat.indiv)
res.spls.1level <- spls(X = liver.toxicity$gene,
Y=liver.toxicity$clinic,
multilevel = design,
ncomp = 2,
keepX = c(50, 50), keepY = c(5, 5),
mode = 'canonical')
stim.col <- c("darkblue", "purple", "green4","red3")
# showing only the Y variables, and only those selected in comp 1
cim(res.spls.1level, mapping="Y",
row.sideColors = stim.col[factor(liver.toxicity$treatment[,3])], comp = 1,
#setting up legend:
legend=list(legend = unique(liver.toxicity$treatment[,3]), col=stim.col,
title = "Dose", cex=0.9))
## Not run:
# showing only the X variables, for all selected on comp 1 and 2
cim(res.spls.1level, mapping="X",
row.sideColors = stim.col[factor(liver.toxicity$treatment[,3])],
#setting up legend:
legend=list(legend = unique(liver.toxicity$treatment[,3]), col=stim.col,
title = "Dose", cex=0.9))
# These are the cross correlations between the variables selected in X and Y.
# The similarity matrix is obtained as in our paper in Data Mining
cim(res.spls.1level, mapping="XY")
## End(Not run)
|
Example output
Loading required package: MASS
Loading required package: lattice
Loading required package: ggplot2
Loaded mixOmics 6.1.3
Visit http://www.mixOmics.org for more details about our methods.
Any bug reports or comments? Notify us at mixomics at math.univ-toulouse.fr or https://bitbucket.org/klecao/package-mixomics/issues
Thank you for using mixOmics!
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
pdf
2
Splitting the variation for 2 level factors.
Splitting the variation for 1 level factor.
Splitting the variation for 1 level factor.
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