cim: Clustered Image Maps (CIMs) ("heat maps")

In mixOmicsTeam/mixOmics: Omics Data Integration Project

Clustered Image Maps (CIMs) ("heat maps")

Description

This function generates color-coded Clustered Image Maps (CIMs) ("heat maps") to represent "high-dimensional" data sets.

Usage

cim(
  mat = NULL,
  color = NULL,
  row.names = TRUE,
  col.names = TRUE,
  row.sideColors = NULL,
  col.sideColors = NULL,
  row.cex = NULL,
  col.cex = NULL,
  cutoff = 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,
  blocks = 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", "mlsplsda", "block.pls" or "block.spls" (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.
cutoff	numeric between 0 and 1. Variables with correlations below this threshold in absolute value are not plotted. To use only when mapping is "XY".
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.
transpose	logical indicating if the matrix should be transposed for plotting. Defaults to FALSE.
symkey	Logical 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.
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.
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.
mapping	character string indicating whether to map "X", "Y" or "XY"-association matrix. Can also be "multiblock" when class(mat) == "block.pls" OR "block.spls". See Details.
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.
blocks	integer or character vector. Used when class(mat) == "block.pls" OR "block.spls". Dictates which blocks will be visualised. See Details.

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.

The blocks parameter controls which blocks are to be included when class(mat) == "block.pls" OR "block.spls". This can be a character or a integer vector.

If using a multiblock object then mapping can be set to "multiblock". When done so, this will emulate the function of cimDiablo(), such that rows will denote each sample and all features included in blocks will be shown as columns, coloured by which block they inherit from. In this case, blocks can include any number of input blocks. If mapping = "X", "Y" OR "XY", then it functions similarly to if a mixo_pls object was being used. blocks has to be of length 2 in this scenario.

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, Al J Abadi

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

## default method: shows cross correlation between 2 data sets
#------------------------------------------------------------------
data(nutrimouse)
X <- nutrimouse$lipid
Y <- nutrimouse$gene

cim(cor(X, Y), cluster = "none")


## Not run: 
## CIM representation for objects of class 'rcc'
#------------------------------------------------------------------

nutri.rcc <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)

cim(nutri.rcc, xlab = "genes", ylab = "lipids", margins = c(5, 6))

#-- interactive 'zoom' available as below

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()

## CIM representation for objects of class 'spca' (also works for sipca)
#------------------------------------------------------------------

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"))


## CIM representation for objects of class '(s)pls'
#------------------------------------------------------------------

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))


## CIM representation for objects of class '(s)plsda'
#------------------------------------------------------------------
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)


## 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))


# 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)

mixOmicsTeam/mixOmics documentation built on Oct. 26, 2023, 6:48 a.m.