Class "BootstrapClusterTest"

Description

Performs a nonparametric bootstrap (sampling with replacement) test to determine whether the clusters found by an unsupervised method appear to be robust in a given data set.

Usage

BootstrapClusterTest(data, FUN, subsetSize, nTimes=100, verbose=TRUE, ...)

Arguments

Objects from the Class

Objects should be created using the BootstrapClusterTest function, which performs the requested bootstrap on the clusters. Following the standard R paradigm, the resulting object can be summarized and plotted to determine the results of the test.

Slots

f:

A function that, given a data matrix, returns a vector of cluster assignments. Examples of functions with this behavior are cutHclust, cutKmeans, cutPam, and cutRepeatedKmeans.

subsetSize:

The number of rows to be included in each bootstrap sample.

nTimes:

An integer, the number of bootstrap samples that were collected.

call:

An object of class call, which records how the object was produced.

result:

Object of class matrix containing, for each pair of columns in the original data, the number of times they belonged to the same cluster of a bootstrap sample.

Extends

Class ClusterTest, directly. See that class for descriptions of the inherited methods image and hist.

Methods

summary

signature(object = BootstrapClusterTest): Write out a summary of the object.

Author(s)

Kevin R. Coombes krc@silicovore.com

References

Kerr MK, Churchill GJ.
Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments.
PNAS 2001; 98:8961-8965.

See Also

ClusterTest, PerturbationClusterTest

Examples

showClass("BootstrapClusterTest")

## simulate data from two different groups
d1 <- matrix(rnorm(100*30, rnorm(100, 0.5)), nrow=100, ncol=30, byrow=FALSE)
d2 <- matrix(rnorm(100*20, rnorm(100, 0.5)), nrow=100, ncol=20, byrow=FALSE)
dd <- cbind(d1, d2)
cols <- rep(c('red', 'green'), times=c(30,20))
colnames(dd) <- paste(cols, c(1:30, 1:20), sep='')
## peform your basic hierarchical clustering...
hc <- hclust(distanceMatrix(dd, 'pearson'), method='complete')

## bootstrap the clusters arising from hclust
bc <- BootstrapClusterTest(dd, cutHclust, nTimes=200, k=3, metric='pearson')
summary(bc)

## look at the distribution of agreement scores
hist(bc, breaks=101)

## let heatmap compute a new dendrogram from the agreement
image(bc, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)

## plot the agreement matrix with the original dendrogram
image(bc, dendrogram=hc, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)

## bootstrap the results of PAM
pamc <- BootstrapClusterTest(dd, cutPam, nTimes=200, k=3)
image(pamc, dendrogram=hc, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)

## contrast the behavior when all the data comes from the same group
xx <- matrix(rnorm(100*50, rnorm(100, 0.5)), nrow=100, ncol=50, byrow=FALSE)
hct <- hclust(distanceMatrix(xx, 'pearson'), method='complete')
bct <- BootstrapClusterTest(xx, cutHclust, nTimes=200, k=4, metric='pearson')
summary(bct)
image(bct, dendrogram=hct, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)

## cleanup
rm(d1, d2, dd, cols, hc, bc, pamc, xx, hct, bct)

Class "ClusterTest"

Description

This is a base class for tests that attempt to determine whether the groups found by an unsupervised clustering method are statistically significant.

Usage

## S4 method for signature 'ClusterTest'
image(x, dendrogram, ...)

Arguments

Objects from the Class

Objects can be created by calls of the form new("ClusterTest", ...). Most users, however, will only create objects from one of the derived classes such as BootstrapClusterTest or PerturbationClusterTest.

Slots

call:

An object of class call, which shows how the object was constructed.

result:

A symmetric matrix containing the results of the cluster reproducibility test. The size of the matrix corresponds to the number of samples (columns) in the data set on which the test was performed. The result matrix should contain "agreement" values between 0 and 1, representing for each pair of samples the fraction of times that they were collected into the same cluster.

Methods

hist

signature(x = "ClusterTest"): Produces a histogram of the agreement fractions. When a true group structure exists, one expects a multimodal distribution,with one group of agreements near 0 (for pairs belonging to different clusters) and one group of agreements near 1 (for pairs belonging to the same cluster).

image

signature(x = "ClusterTest"): Uses the heatmap function to display the agreement matrix. The optional dendrogram argument should be used to display the extent to which the agreement matrix matches the results of hierarchical clustering using the full data set. This method invisibly returns the result of a call to heatmap; thus, you can use keep.dendro=TRUE to recover the cluster assignments from the dendrograms.

summary

signature(object = "ClusterTest"): Write out a summary of the object.

Author(s)

Kevin R. Coombes krc@silicovore.com

References

Kerr MK, Churchill GJ.
Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments.
PNAS 2001; 98:8961-8965.

See Also

BootstrapClusterTest, PerturbationClusterTest, heatmap

Examples

showClass("ClusterTest")

## simulate data from two different classes
d1 <- matrix(rnorm(100*30, rnorm(100, 0.5)), nrow=100, ncol=30, byrow=FALSE)
d2 <- matrix(rnorm(100*20, rnorm(100, 0.5)), nrow=100, ncol=20, byrow=FALSE)
dd <- cbind(d1, d2)

## cluster the data
hc <- hclust(distanceMatrix(dd, 'pearson'), method='average')

## make a fake reproducibility matrix
fraud <- function(x) {
  new('ClusterTest', result=abs(cor(x)), call=match.call())
}

fake <- fraud(dd)
summary(fake)

hist(fake)

image(fake) # let heatmap compute a new dendrogram from the agreements

image(fake, dendrogram=hc) # use the actual dendrogram from the data

image(fake, dendrogram=hc, col=blueyellow(64)) # change the colors

## cleanup
rm(fake, fraud, hc, dd, d1, d2)

Class "GenePCA"

Description

Perform principal components analysis on the genes (rows) from a microarray or proteomics experiment.

Usage

GenePCA(geneData)
## S4 method for signature 'GenePCA,missing'
plot(x, splitter=0)

Arguments

Details

This is a preliminary attempt at a class for principal components analysis of genes, parallel to the SamplePCA class for samples. The interface will (one hopes) improve markedly in the next version of the library.

Value

The GenePCA function constructs and returns a valid object of the GenePCA class.

Objects from the Class

Objects should be created using the GenePCA generator function.

Slots

scores:

A matrix of size PxN, where P is the number of rows and N the number fo columns in the input, representing the projections of the input rows onto the first N principal components.

variances:

A numeric vector of length N; the amount of the total variance explained by each principal component.

components:

A matrix of size NxN containing each of the first P principal components as columns.

Methods

plot

signature(x = GenePCA, y = missing): Plot the genes in the space of the first two principal components.

Author(s)

Kevin R. Coombes krc@silicovore.com

See Also

SamplePCA, princomp

Examples

showClass("GenePCA")

## simulate samples from three different groups, with generic genes
d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
dd <- cbind(d1, d2, d3)

## perform PCA in gene space
gpc <- GenePCA(dd)

## plot the results
plot(gpc)

## cleanup
rm(d1, d2, d3, dd, gpc)

Class "Mosaic"

Description

Produce “Eisen” plots of microarray or proteomics data.

Usage

Mosaic(data,
       sampleMetric="pearson",
       sampleLinkage="average",
       geneMetric="euclid",
       geneLinkage="average",
       usecor=FALSE,
       center=FALSE,
       name="My mosaic")
## S4 method for signature 'Mosaic'
pltree(x, colors, labels, ...)
## S4 method for signature 'Mosaic,missing'
plot(x, main=x@name, center=FALSE,
  scale=c("none", "row", "column"), limits=NULL,
  sampleColors=NULL, sampleClasses=NULL,
  geneColors=NULL, geneClasses=NULL, ...)

Arguments

Details

One of the earliest papers in the microarray literature used independent clustering of the genes (rows) and samples (columns) to produce dendrograms that were plotted along with a red-green heat map of the centered expression values. Since that time, literally thousand of additional papers have published variations on these red-green images. R includes a function, heatmap that builds such figures. However, that function is general purpose and has numerous optional parameters to tweak the display. The purpose of the Mosaic class is to provide a simplified object-oriented wrapper around heatmap, which as a side benefit allows us to keep track of the distance metrics and linkage rules that were used to produce the resulting figure.

Value

The Mosaic function constructs and returns a valid object of the Mosaic class.

Objects from the Class

Objects should be created with the Mosaic function.

Slots

data:

The matrix containing the numerical data

samples:

A dendrogram of class hclust produced by clustering the biological samples (columns of data).

genes:

A dendrogram of class hclust produced by clustering the genes (columns of data).

sampleMetric:

A character string; the distance metric used to cluster the samples.

sampleLinkage:

A character string; the linkage rule used to cluster the samples.

geneMetric:

A character string; the distance metric used to cluster the genes.

geneLinkage:

A character string; the linkage rule used to cluster the genes.

call:

An object of class call recording how the object was constructed.

name:

A character string; the name of this object.

Methods

plot

signature(x = Mosaic, y = missing): Produce the “Eisen” plot, using heatmap.

pltree

signature(x = Mosaic): Plot the sample class dendrogram in the object.

summary

signature(object = Mosaic): Write out a summary of the object.

Author(s)

Kevin R. Coombes krc@silicovore.com, P. Roebuck proebuck@mdanderson.org

References

Eisen MB, Spellman PT, Brown PO, Botstein D.
Cluster analysis and display of genome-wide expression patterns.
Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8.

See Also

cutree, hclust, heatmap

Examples

showClass("Mosaic")

## simulate data from three different sample groups
d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
dd <- cbind(d1, d2, d3)
kind <- factor(rep(c('red', 'green', 'blue'), each=10))

## prepare the Mosaic object
m <- Mosaic(dd,
            sampleMetric='pearson',
            geneMetric='spearman',
            center=TRUE,
            usecor=TRUE)
summary(m)

## The default plot with red-green color map
plot(m, col=redgreen(64))

## change to a blue-yellow color map, and mark the four top splits in the
## sample direction with a color bar along the top
plot(m, col=blueyellow(128), sampleClasses=4,
     sampleColors=c('red', 'green', 'blue', 'black'))

## This time, mark the three classes that we know are there
plot(m, col=blueyellow(128), sampleClasses=kind,
     sampleColors=c('red', 'green', 'blue'))

plot(m, col=blueyellow(128),
     geneClasses=3, geneColors=c('red', 'green', 'black'))

## In addition, mark the top 5 splits in the gene dendrogram
plot(m,
     col=blueyellow(128),
     sampleClasses=kind,
     sampleColors=c('red', 'green', 'black'),
     geneClasses=5,
     geneColors=c('cyan', 'magenta', 'royalblue', 'darkgreen', 'orange'))

## plot the sample dendrogram by itself
cols <- as.character(kind)
pltree(m, labels=1:30, colors=cols)

## cleanup
rm(d1, d2, d3, dd, kind, cols, m)

Class "PCanova"

Description

Implements the PCANOVA method for determining whether a putative group structure is truly reflected in multivariate data set.

Usage

PCanova(data, classes, labels, colors, usecor=TRUE)

## S4 method for signature 'PCanova,missing'
plot(x, tag='', mscale=1, cex=1, ...)

Arguments

Details

The PCANOVA method was developed as part of the submission that won the award for best presentation at the 2001 conference on the Critical Assessment of Microarray Data Analysis (CAMDA; https://bipress.boku.ac.at/camda-play/). The idea is to perform the equivalent of an analysis of variance (ANOVA) in principal component (PC) space. Let X(i,j) denote the jth column vector belonging to the ith group of samples. We can model this as X(i,j) = mu + tau(i) + E(i,j), where mu is the overall mean vector, tau(i) is the “effects” vector for the ith group, and E(i,j) is the vector of residual errors. We can perform principal components analysis on the full matrix X containing all the columns X(i,j), on the matrix containing all the group mean vectors mu + tau(i), and on the residual matrix containing all the E(i,j) vectors. PCANOVA develops a measure (“PC correlation”) for comparing these three sets of principal components. If the PC correlation is close to 1, then two principal component bases are close together; if the PC correlation is close to zero, then two principal components bases are dissimilar. Strong group structures are recognizable because the PC correlation between the total-matrix PC space and the group-means PC space is much larger than the PC correlation between the total-matrix PC space and the residual PC space. Weak or nonexistent group structures are recognizable because the relative sizes of the PC correlations is reversed.

Value

The PCanova function returns an object of the PCanova class.

Objects from the Class

Objects should be created by calling the PCanova generator function.

Slots

orig.pca:

A matrix containing the scores component from PCA performed on the total matrix. All principal components analyses are performed using the SamplePCA class.

class.pca:

A matrix containing the scores component from PCA performed on the matrix of group-mean vectors.

resid.pca:

A matrix containing the scores component from PCA performed on the matrix of residuals.

mixed.pca:

A matrix containing the projections of all the original vectoprs into the principal component space computed from the matrix of group mean vectors.

xc:

An object produced by performing hierarchical clustering on the total data matrix, using hclust with pearson distance and average linkage.

hc:

An object produced by performing hierarchical clustering on the matrix of group means, using hclust with pearson distance and average linkage.

rc:

An object produced by performing hierarchical clustering on the matrix of residuals, using hclust with pearson distance and average linkage.

n:

An integer; the number of samples.

class2orig:

The numeric vector of PC correlations relating the total-matrix PCA to the group-means PCA.

class2resid:

The numeric vector of PC correlations relating the residual PCA to the group-means PCA.

orig2resid:

The numeric vector of PC correlations relating the total-matrix PCA to the residual PCA.

labels:

A character vector of plot labels to indicate the group membership of samples.

classes:

A character vector of labels identifying the distinct groups.

colors:

A character vector of color names used to indicate the group membership fo samples in plots.

call:

An object of class call that records how the object was constructed.

Methods

plot

signature(x = PCanova, y = missing): Plot the results of the PCANOVA test on the data. This uses par to set up a 2x2 layout of plots. The first three plots show the sample vectors (color-coded and labeled) in the space spanned by the first two principal components for each of the there PCAs. The final plot shows the three sets of PC correlations. Colors in the first three plots are determined by the colors slot of the object, which was set when the object was created. Colors in the PC correlation plot are determined by the current values of oompaColor$OBSERVED, oompaColor$EXPECTED, and oompaColor$PERMTEST

pltree

signature(x = PCanova): Produce dendrograms of the three hierarchical clusters of the samples, based on all the data, the group means, and the residuals. Since this method uses par to put all three dendrograms in the same window, it cannot be combined with other plots.

summary

signature(object = PCanova): Write out a summary of the object.

BUGS

[1] The projection of the sample vectors into the principal component space of the group-means is off by a scale factor. The mscale parameter provides a work-around.

[2] The pltree method fails if you only supply two groups; this may be a failure in hclust if you only provide two objects to cluster.

Author(s)

Kevin R. Coombes krc@silicovore.com

References

Examples of the output of PCANOVA applied to the NCI60 data set can be found at http:/silicovore.com/camda01.html. The full description has not been published (out of laziness on the part of the author of this code). The only description that has appeared in print is an extremely brief description that can be found in the proceedings of the CAMDA 2001 conference.

See Also

SamplePCA

Examples

showClass("PCanova")

## simulate data from three groups
d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
dd <- cbind(d1, d2, d3)
## colors that match the groups
cols <- rep(c('red', 'green', 'blue'), each=10)

## compute the PCanova object
pan <- PCanova(dd, c('red', 'green', 'blue'), cols, cols)
summary(pan)

## view the PC plots
plot(pan)

## view the dendrograms
pltree(pan, line=-0.5)

## compare the results when there is no underlying group structure
dd <- matrix(rnorm(100*50, rnorm(100, 0.5)), nrow=100, ncol=50, byrow=FALSE)
cols <- rep(c('red', 'green', 'blue', 'orange', 'cyan'), each=10)
pan <- PCanova(dd, unique(cols), cols, cols)
plot(pan, mscale=1/sqrt(10))

pltree(pan, line=-0.5)

## cleanup
rm(d1, d2, d3, dd, cols, pan)

The PerturbationClusterTest Class

Description

Performs a parametric bootstrap test (by adding independent Gaussian noise) to determine whether the clusters found by an unsupervised method appear to be robust in a given data set.

Usage

PerturbationClusterTest(data, FUN, nTimes=100, noise=1, verbose=TRUE, ...)

Arguments

Objects from the Class

Objects should be created using the PerturbationClusterTest function, which performs the requested bootstrap on the clusters. Following the standard R paradigm, the resulting object can be summarized and plotted to determine the results of the test.

Slots

f:

A function that, given a data matrix, returns a vector of cluster assignments. Examples of functions with this behavior are cutHclust, cutKmeans, cutPam, and cutRepeatedKmeans.

noise:

The standard deviation of the Gaussian noise added during each bootstrap sample.

nTimes:

An integer, the number of bootstrap samples that were collected.

call:

An object of class call, which records how the object was produced.

result:

Object of class matrix containing, for each pair of columns in the original data, the number of times they belonged to the same cluster of a bootstrap sample.

Extends

Class ClusterTest, directly. See that class for descriptions of the inherited methods image and hist.

Methods

summary

signature(object = PerturbationClusterTest): Write out a summary of the object.

Author(s)

Kevin R. Coombes krc@silicovore.com

References

Kerr MK, Churchill GJ.
Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments.
PNAS 2001; 98:8961-8965.

See Also

BootstrapClusterTest, ClusterTest

Examples

showClass("PerturbationClusterTest")

## simulate data from two different groups
d1 <- matrix(rnorm(100*30, rnorm(100, 0.5)), nrow=100, ncol=30, byrow=FALSE)
d2 <- matrix(rnorm(100*20, rnorm(100, 0.5)), nrow=100, ncol=20, byrow=FALSE)
dd <- cbind(d1, d2)
cols <- rep(c('red', 'green'), times=c(30,20))
colnames(dd) <- paste(cols, c(1:30, 1:20), sep='')
## peform your basic hierarchical clustering...
hc <- hclust(distanceMatrix(dd, 'pearson'), method='complete')

## bootstrap the clusters arising from hclust
bc <- PerturbationClusterTest(dd, cutHclust, nTimes=200, k=3, metric='pearson')
summary(bc)

## look at the distribution of agreement scores
hist(bc, breaks=101)

## let heatmap compute a new dendrogram from the agreement
image(bc, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)

## plot the agreement matrix with the original dendrogram
image(bc, dendrogram=hc, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)

## bootstrap the results of K-means
kmc <- PerturbationClusterTest(dd, cutKmeans, nTimes=200, k=3)
image(kmc, dendrogram=hc, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)

## contrast the behavior when all the data comes from the same group
xx <- matrix(rnorm(100*50, rnorm(100, 0.5)), nrow=100, ncol=50, byrow=FALSE)
hct <- hclust(distanceMatrix(xx, 'pearson'), method='complete')
bct <- PerturbationClusterTest(xx, cutHclust, nTimes=200, k=4, metric='pearson')
summary(bct)
image(bct, dendrogram=hct, col=blueyellow(64), RowSideColors=cols, ColSideColors=cols)

## cleanup
rm(d1, d2, dd, cols, hc, bc, kmc, xx, hct, bct)

Class "SamplePCA"

Description

Perform principal components analysis on the samples (columns) from a microarray or proteomics experiment.

Usage

SamplePCA(data, splitter=0, usecor=FALSE, center=TRUE)
## S4 method for signature 'SamplePCA,missing'
plot(x, splitter=x@splitter, col, main='', which=1:2, ...)

Arguments

.

Details

The main reason for developing the SamplePCA class is that the princomp function is very inefficient when the number of variables (in the microarray setting, genes) far exceeds the number of observations (in the microarray setting, biological samples). The princomp function begins by computing the full covariance matrix, which gets rather large in a study involving tens of thousands of genes. The SamplePCA class, by contrast, uses singular value decomposition (svd) on the original data matrix to compute the principal components.

The base functions screeplot, which produces a barplot of the percentage of variance explained by each component, and plot, which produces a scatter plot comparing two selected components (defaulting to the first two), have been generalized as methods for the SamplePCA class. You can add sample labels to the scatter plot using either the text or identify methods. One should, however, note that the current implementaiton of these methods only works when plotting the first two components.

Value

The SamplePCA function constructs and returns an object of the SamplePCA class. We assume that the input data matrix has N columns (of biological samples) and P rows (of genes).

The predict method returns a matrix whose size is the number of columns in the input by the number of principal components.

Objects from the Class

Objects should be created using the SamplePCA function. In the simplest case, you simply pass in a data matrix and a logical vector, splitter, assigning classes to the columns, and the constructor performs principal components analysis on the column. The splitter is ignored by the constructor and is simply saved to be used by the plotting routines. If you omit the splitter, then no grouping structure is used in the plots.

If you pass splitter as a factor instead of a logical vector, then the plotting routine will distinguish all levels of the factor. The code is likely to fail, however, if one of the levels of the factor has zero representatives among the data columns.

We can also perform PCA on ExpressionSet objects from the BioConductor libraries. In this case, we pass in an ExpressionSet object along with a character string containing the name of a factor to use for splitting the data.

Slots

scores:

A matrix of size NxN, where N is the number of columns in the input, representing the projections of the input columns onto the first N principal components.

variances:

A numeric vector of length N; the amount of the total variance explained by each principal component.

components:

A matrix of size PxN (the same size as the input matrix) containing each of the first P principal components as columns.

splitter:

A logical vector or factor of length N classifying the columns into known groups.

usecor:

A logical value; was the data standardized?

shift:

A numeric vector of length P; the mean vector of the input data, which is used for centering by the predict method.

scale:

A numeric vector of length P; the standard deviation of the input data, which is used for scaling by the predict method.

call:

An object of class call that records how the object was created.

Methods

plot

signature(x = SamplePCA, y = missing): Plot the samples in a two-dimensional principal component space.

predict

signature(object = SamplePCA): Project new data into the principal component space.

screeplot

signature(x = SamplePCA): Produce a bar chart of the variances explained by each principal component.

summary

signature(object = SamplePCA): Write out a summary of the object.

identify

signature(object = SamplePCA): interactively identify points in the plot of a SamplePCA object.

text

signature(object = SamplePCA): Add sample identifiers to the scatter plot of a SamplePCA object, using the base text function.

Author(s)

Kevin R. Coombes krc@silicovore.com

See Also

princomp, GenePCA

Examples

showClass("SamplePCA")

## simulate data from three different groups
d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
dd <- cbind(d1, d2, d3)
kind <- factor(rep(c('red', 'green', 'blue'), each=10))
colnames(dd) <- paste(kind, rep(1:10, 3), sep='')

## perform PCA
spc <- SamplePCA(dd, splitter=kind)

## plot the results
plot(spc, col=levels(kind))

## mark the group centers
x1 <- predict(spc, matrix(apply(d1, 1, mean), ncol=1))
points(x1[1], x1[2], col='red', cex=2)
x2 <- predict(spc, matrix(apply(d2, 1, mean), ncol=1))
points(x2[1], x2[2], col='green', cex=2)
x3 <- predict(spc, matrix(apply(d3, 1, mean), ncol=1))
points(x3[1], x3[2], col='blue', cex=2)

## check out the variances
screeplot(spc)

## cleanup
rm(d1, d2, d3, dd,kind, spc, x1, x2, x3)

Heatmap with control over the aspect ratio

Description

This function replaces the heatmap function in the stats package with a function with additional features. First, the user can specify an aspect ratio instead of being forced to accept a square image even when the matrix is not square. Second, the user can overlay horizontal or vertical lines to mark out important regions.

Usage

aspectHeatmap(x,
              Rowv=NULL,
              Colv=if (symm) "Rowv" else NULL,
              distfun=dist,
              hclustfun=hclust,
              reorderfun=function(d, w) reorder(d, w),
              add.expr,
              symm=FALSE,
              revC=identical(Colv, "Rowv"),
              scale=c("row", "column", "none"),
              na.rm=TRUE,
              margins=c(5, 5),
              ColSideColors,
              RowSideColors,
              cexRow=0.2 + 1/log10(nr),
              cexCol=0.2 + 1/log10(nc),
              labRow=NULL,
              labCol=NULL,
              main=NULL,
              xlab=NULL,
              ylab=NULL,
              keep.dendro=FALSE,
              verbose=getOption("verbose"),
              hExp=1,
              wExp=1,
              vlines=NULL,
              hlines=NULL,
              lasCol=2,
              ...)

Arguments

Details

The new arguments hExp and wExp are "expansion factors" for the height and width of the figure, respectively. They are used to alter the arguments passed to the layout function internally to heatmap.

The new arguments hlines and vlines are used to specify points at which horizontal or vertical lines, respectively, should be overlaid on the image.

Value

The same as the heatmap function.

Author(s)

Kevin R. Coombes krc@silicovore.com

See Also

heatmap

Examples

nC <- 30
nR <- 1000
fakeData <- matrix(rnorm(nC*nR), ncol=nC, nrow=nR)
aspectHeatmap(fakeData, scale='none', hExp=2, wExp=1.4, margins=c(6,3))
aspectHeatmap(fakeData, scale='none', hExp=2, wExp=1.4, margins=c(6,3),
              vlines=15.5, hlines=c(100, 300))

Cluster a Dataset Three Ways

Description

Produces and plots dendrograms using three similarity measures: Euclidean distance, Pearson correlation, and Manhattan distance on dichotomized data.

Usage

cluster3(data, eps=logb(1, 2), name="", labels=dimnames(data)[[2]])

Arguments

Value

Invisibly returns the data object on which it was invoked.

Author(s)

Kevin R. Coombes krc@silicovore.com

See Also

hclust

Examples

## simulate data from two different classes
d1 <- matrix(rnorm(100*30, rnorm(100, 0.5)), nrow=100, ncol=30, byrow=FALSE)
d2 <- matrix(rnorm(100*20, rnorm(100, 0.5)), nrow=100, ncol=20, byrow=FALSE)
dd <- cbind(d1, d2)
## cluster it 3 ways
par(mfrow=c(2,2))
cluster3(dd)
par(mfrow=c(1,1))

Distance Matrix Computation

Description

This function computes and returns the distance matrix determined by using the specified distance metric to compute the distances between the columns of a data matrix.

Usage

distanceMatrix(dataset, metric, ...)

Arguments

Details

This function differs from dist in two ways, both of which are motivated by common practice in the analysis of microarray or proteomics data. First, it computes distances between column vectors instead of between row vectors. In a typical microarray experiment, the data is organized so the rows represent genes and the columns represent different biological samples. In many applications, relations between the biological samples are more interesting than relationships between genes. Second, distanceMatrix adds additional distance metrics based on correlation.

pearson

The most common metric used in the microarray literature is the pearson distance, which can be computed in terms of the Pearson correlation coefficient as (1-cor(dataset))/2.

uncentered correlation

This metric was introduced in the Cluster and TreeView software from the Eisen lab at Stanford. It is computed using the formulas for Pearson correlation, but assuming that both vectors have mean zero.

spearman

The spearman metric used the same formula, but substitutes the Spearman rank correlation for the Pearson correlation.

absolute pearson

The absolute pearson metric used the absolute correlation coefficient; i.e., (1-abs(cor(dataset))).

sqrt pearson

The sqrt pearson metric used the square root of the pearson distance metric; i.e., sqrt(1-cor(dataset)).

weird

The weird metric uses the Euclidean distance between the vectors of correlation coefficients; i.e., dist(cor(dataset)).

Value

A distance matrix in the form of an object of class dist, of the sort returned by the dist function or the as.dist function.

BUGS

It would be good to have a better name for the weird metric.

Author(s)

Kevin R. Coombes krc@silicovore.com

See Also

dist, as.dist

Examples

dd <- matrix(rnorm(100*5, rnorm(100)), nrow=100, ncol=5)
distanceMatrix(dd, 'pearson')
distanceMatrix(dd, 'euclid')
distanceMatrix(dd, 'sqrt')
distanceMatrix(dd, 'weird')
rm(dd) # cleanup

Class "hclust"

Description

Creates an S4 class equivalent to the S3 hclust class returned by the hclust function.

Author(s)

Kevin R. Coombes krc@silicovore.com

See Also

cutree, hclust, heatmap

Get the List of Classes From A Clustering Algorithm

Description

Unsupervised clustering algorithms, such as partitioning around medoids (pam), K-means (kmeans), or hierarchical clustering (hclust) after cutting the tree, produce a list of class assignments along with other structure. To simplify the interface for the BootstrapClusterTest and PerturbationClusterTest, we have written these routines that simply extract these cluster assignments.

Usage

cutHclust(data, k, method = "average", metric = "pearson")
cutPam(data, k)
cutKmeans(data, k)
cutRepeatedKmeans(data, k, nTimes)

repeatedKmeans(data, k, nTimes)

Arguments

Details

Each of the clustering routines used here has a different structure for storing cluster assignments. The kmeans function stores the assignments in a ‘cluster’ attribute. The pam function uses a ‘clustering’ attribute. For hclust, the assignments are produced by a call to the cutree function.

It has been observed that the K-means algorithm can converge to different solutions depending on the starting values of the group centers. We also include a routine (repeatedKmeans) that runs the K-means algorithm repeatedly, using different randomly generated staring points each time, saving the best results.

Value

Each of the cut... functions returns a vector of integer values representing the cluster assignments found by the algorithm.

The repeatedKmeans function returns a list x with three components. The component x$kmeans is the result of the call to the kmeans function that produced the best fit to the data. The component x$centers is a matrix containing the list of group centers that were used in the best call to kmeans. The component x$withinss contains the sum of the within-group sums of squares, which is used as the measure of fitness.

Author(s)

Kevin R. Coombes krc@silicovore.com

See Also

cutree, hclust, kmeans, pam

Examples

# simulate data from three different groups
d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
dd <- cbind(d1, d2, d3)

cutKmeans(dd, k=3)
cutKmeans(dd, k=4)

cutHclust(dd, k=3)
cutHclust(dd, k=4)

cutPam(dd, k=3)
cutPam(dd, k=4)

cutRepeatedKmeans(dd, k=3, nTimes=10)
cutRepeatedKmeans(dd, k=4, nTimes=10)

# cleanup
rm(d1, d2, d3, dd)

Using Mahalanobis Distance and PCA for Quality Control

Description

Compute the Mahalanobis distance of each sample from the center of an N-dimensional principal component space.

Usage

mahalanobisQC(spca, N)

Arguments

Details

The theory says that, under the null hypothesis that all samples arise from the same multivariate normal distribution, the distance from the center of a D-dimensional principal component space should follow a chi-squared distribution with D degrees of freedom. This theory lets us compute p-values associated with the Mahalanobis distances for each sample. This method can be used for quality control or outlier identification.

Value

Returns a data frame containing two columns, with the rows corresponding to the columns of the original data set on which PCA was performed. First column is the chi-squared statistic, with N degrees of freedom. Second column is the associated p-value.

Author(s)

Kevin R. Coombes krc@silicovore.com

References

Coombes KR, et al.
Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization. Clin Chem 2003; 49:1615-23.

Examples

library(oompaData)
data(lungData)
spca <- SamplePCA(na.omit(lung.dataset))
mc <- mahalanobisQC(spca, 2)
mc[mc$p.value < 0.01,]

Plot Dendrograms with Color-Coded Labels

Description

Provides an interface to the plot method for hclust that makes it easier to plot dendrograms with labels that are color-coded, usually to indicate the different levels of a factor.

Usage

plotColoredClusters(hd, labs, cols, cex = 0.8, main = "", line = 0, ...)
pcc(hd, colors=NULL, ...)

Arguments

.

Details

The plotColoredClusters function is used to implement the pltree methods of the Mosaic class and the PCanova class. It simply bundles a two step process (first plotting the dendrogram with no labels, followed by writing the labels in the right places with the desired colors) into a single unit.

The pcc function also produces dendrograms with colored annotations. However, instead of coloring the labels based on a single factor, it produces color bars for any number of factors. The colors argument should be a list with named components, where each component should correspond to a factor and a color scheme. Specifically, the components must themselves be lists with two components named fac (and containing the factor) and col (containing a named vector specifying colors for each level of the factor).

Value

The function has no useful return value; it merely produces a plot.

See Also

hclust, Mosaic, PCanova, par

Examples

# simulate data from three different groups
d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
dd <- cbind(d1, d2, d3)

# perform hierarchical clustering using correlation
hc <- hclust(distanceMatrix(dd, 'pearson'), method='average')
cols <- rep(c('red', 'green', 'blue'), each=10)
labs <- paste('X', 1:30, sep='')

# plot the dendrogram with color-coded groups
plotColoredClusters(hc, labs=labs, cols=cols)

# simulate another dataset
fakedata <- matrix(rnorm(200*30), ncol=30)
colnames(fakedata) <- paste("P", 1:30, sep='')
# define two basic factors, with colors
faccol <- list(fac=factor(rep(c("A", "B"), each=15)),
               col=c(A='red', B='green'))
fac2col <- list(fac=factor(rep(c("X", "Y", "Z"), times=10)),
               col=c(X='cyan', Y='orange', Z='purple'))
# add another factor that reverses the colors
BA <- faccol
BA$col <- c(A='blue', B='yellow')
# assemble the list of factors
colors <- list(AB=faccol, XYZ=fac2col, "tricky long name"=fac2col,
               another=BA)
# cluster the samples
hc <- hclust(distanceMatrix(fakedata, "pearson"), "ward")
# plot the results
pcc(hc, colors)


#cleanup
rm(d1, d2, d3, dd, hc, cols, labs)
rm(fakedata, faccol, fac2col, BA, colors)