Title: | Classes and Methods for "Class Discovery" with Microarrays or Proteomics |
---|---|
Description: | Defines the classes used for "class discovery" problems in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class discovery primarily consists of unsupervised clustering methods with attempts to assess their statistical significance. |
Authors: | Kevin R. Coombes |
Maintainer: | Kevin R. Coombes <[email protected]> |
License: | Apache License (== 2.0) |
Version: | 3.4.5 |
Built: | 2024-11-10 02:58:44 UTC |
Source: | https://github.com/r-forge/oompa |
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.
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, ...)
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, ...)
x |
See documentation for heatmap. |
Rowv |
See documentation for heatmap. |
Colv |
See documentation for heatmap. |
distfun |
See documentation for heatmap. |
hclustfun |
See documentation for heatmap. |
reorderfun |
See documentation for heatmap. |
add.expr |
See documentation for heatmap. |
symm |
See documentation for heatmap. |
revC |
See documentation for heatmap. |
scale |
See documentation for heatmap. |
na.rm |
See documentation for heatmap. |
margins |
See documentation for heatmap. |
ColSideColors |
See documentation for heatmap. |
RowSideColors |
See documentation for heatmap. |
cexRow |
See documentation for heatmap. |
cexCol |
See documentation for heatmap. |
labRow |
See documentation for heatmap. |
labCol |
See documentation for heatmap. |
main |
See documentation for heatmap. |
xlab |
See documentation for heatmap. |
ylab |
See documentation for heatmap. |
keep.dendro |
See documentation for heatmap. |
verbose |
See documentation for heatmap. |
hExp |
height expansion factor (default is |
wExp |
width expansion factor (default is |
vlines |
vector of positions at which to draw vertical lines |
hlines |
vector of positions at which to draw horizontal lines |
lasCol |
axis label style (las) for columns |
... |
additional arguments passed along to the image command |
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.
The same as the heatmap function.
Kevin R. Coombes [email protected]
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))
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))
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.
BootstrapClusterTest(data, FUN, subsetSize, nTimes=100, verbose=TRUE, ...)
BootstrapClusterTest(data, FUN, subsetSize, nTimes=100, verbose=TRUE, ...)
data |
A data matrix, numerical data frame, or
|
FUN |
A |
... |
Additional arguments passed to the classifying function, |
subsetSize |
An optional integer argument. If present,
each iteration of the bootstrap selects |
nTimes |
The number of bootstrap samples to collect. |
verbose |
A logical flag |
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.
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.
Class ClusterTest
, directly. See that class for
descriptions of the inherited methods image
and hist
.
signature(object = BootstrapClusterTest)
:
Write out a summary of the object.
Kevin R. Coombes [email protected]
Kerr MK, Churchill GJ.
Bootstrapping cluster analysis: Assessing the reliability of
conclusions from microarray experiments.
PNAS 2001; 98:8961-8965.
ClusterTest
,
PerturbationClusterTest
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)
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)
Produces and plots dendrograms using three similarity measures: Euclidean distance, Pearson correlation, and Manhattan distance on dichotomized data.
cluster3(data, eps=logb(1, 2), name="", labels=dimnames(data)[[2]])
cluster3(data, eps=logb(1, 2), name="", labels=dimnames(data)[[2]])
data |
A matrix, numeric data.frame, or
|
eps |
A numerical value; the threshold at which to dichotomize the data |
name |
A character string to label the plots |
labels |
A vector of character strings used to label the items in the dendrograms. |
Invisibly returns the data
object on which it was invoked.
Kevin R. Coombes [email protected]
## 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))
## 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))
This is a base class for tests that attempt to determine whether the groups found by an unsupervised clustering method are statistically significant.
## S4 method for signature 'ClusterTest' image(x, dendrogram, ...)
## S4 method for signature 'ClusterTest' image(x, dendrogram, ...)
x |
An object of the |
dendrogram |
An object with S3 class |
... |
Additional graphical parameters to be passed to the
standard |
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
.
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.
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).
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.
signature(object = "ClusterTest")
: Write out a
summary of the object.
Kevin R. Coombes [email protected]
Kerr MK, Churchill GJ.
Bootstrapping cluster analysis: Assessing the reliability of
conclusions from microarray experiments.
PNAS 2001; 98:8961-8965.
BootstrapClusterTest
,
PerturbationClusterTest
,
heatmap
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)
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)
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.
distanceMatrix(dataset, metric, ...)
distanceMatrix(dataset, metric, ...)
dataset |
A numeric matrix or an |
metric |
A character string defining the distance metric. This
can be |
... |
Additional parameters to be passed on to
|
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)).
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.
It would be good to have a better name for the weird
metric.
Kevin R. Coombes [email protected]
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
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
Perform principal components analysis on the genes (rows) from a microarray or proteomics experiment.
GenePCA(geneData) ## S4 method for signature 'GenePCA,missing' plot(x, splitter=0)
GenePCA(geneData) ## S4 method for signature 'GenePCA,missing' plot(x, splitter=0)
geneData |
A data matrix, with rows interpreted as genes and columns as samples |
x |
a |
splitter |
A logical vector classifying the genes. |
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.
The GenePCA
function constructs and returns a valid object of
the GenePCA
class.
Objects should be created using the GenePCA
generator function.
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.
signature(x = GenePCA, y = missing)
: Plot the
genes in the space of the first two principal components.
Kevin R. Coombes [email protected]
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)
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)
Creates an S4 class equivalent to the S3 hclust class returned by the
hclust
function.
Kevin R. Coombes [email protected]
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.
cutHclust(data, k, method = "average", metric = "pearson") cutPam(data, k) cutKmeans(data, k) cutRepeatedKmeans(data, k, nTimes) repeatedKmeans(data, k, nTimes)
cutHclust(data, k, method = "average", metric = "pearson") cutPam(data, k) cutKmeans(data, k) cutRepeatedKmeans(data, k, nTimes) repeatedKmeans(data, k, nTimes)
data |
A numerical data matrix |
k |
The number of classes desired from the algorithm |
method |
Any valid linkage method that can be passed to the
|
metric |
Any valid distance metric that can be passed to the
|
nTimes |
An integer; the number of times to repeat the K-means algorithm with a different random starting point |
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.
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.
Kevin R. Coombes [email protected]
# 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)
# 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)
Compute the Mahalanobis distance of each sample from the center of an N-dimensional principal component space.
mahalanobisQC(spca, N)
mahalanobisQC(spca, N)
spca |
object of class |
N |
integer scalar specifying the number of components to use when assessing QC. |
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.
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.
Kevin R. Coombes [email protected]
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.
library(oompaData) data(lungData) spca <- SamplePCA(na.omit(lung.dataset)) mc <- mahalanobisQC(spca, 2) mc[mc$p.value < 0.01,]
library(oompaData) data(lungData) spca <- SamplePCA(na.omit(lung.dataset)) mc <- mahalanobisQC(spca, 2) mc[mc$p.value < 0.01,]
Produce “Eisen” plots of microarray or proteomics data.
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, ...)
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, ...)
data |
Either a data frame or matrix with numeric values or an
|
sampleMetric |
Any valid distance metric that can be passed to the
|
sampleLinkage |
Any valid linkage method that can be passed to the
|
geneMetric |
Any valid distance metric that can be passed to the
|
geneLinkage |
Any valid linkage method that can be passed to the
|
center |
logical scalar. If |
usecor |
logical scalar. If |
name |
character string specifying the name of this object |
x |
object of class |
scale |
Same as in |
colors |
An optional vector of character strings containing color names to be used when labeling the trees in the dendrogram. If provided, then the length should equal the number of columns in the original data matrix. |
labels |
An optional vector of character strings used to label the leaves in the dendrogram. If omitted, the column names are used. |
main |
character string specifying the main title for the plot |
limits |
An numeric vector. If provided, the data is truncated
for display purposes, both above and below, at the minimum and
maximum values of |
sampleColors |
An optional character vector containing colors that will be used to label different sample types with a color bar across the top of the heat map. |
sampleClasses |
A logical vector or factor used to classify the
samples into groups. Alternatively, an integer specifying the number
|
geneColors |
An optional character vector containing colors that will be used to label different gene types with a color bar along the side of the heat map. |
geneClasses |
A logical vector or factor used to classify the
genes into groups. Alternatively, an integer specifying the number
|
... |
Additional parameters for |
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.
The Mosaic
function constructs and returns a valid object of
the Mosaic
class.
Objects should be created with the Mosaic
function.
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.
signature(x = Mosaic, y = missing)
: Produce the
“Eisen” plot, using heatmap
.
signature(x = Mosaic)
: Plot the sample class
dendrogram in the object.
signature(object = Mosaic)
: Write out a
summary of the object.
Kevin R. Coombes [email protected], P. Roebuck [email protected]
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.
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)
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)
Implements the PCANOVA method for determining whether a putative group structure is truly reflected in multivariate data set.
PCanova(data, classes, labels, colors, usecor=TRUE) ## S4 method for signature 'PCanova,missing' plot(x, tag='', mscale=1, cex=1, ...)
PCanova(data, classes, labels, colors, usecor=TRUE) ## S4 method for signature 'PCanova,missing' plot(x, tag='', mscale=1, cex=1, ...)
data |
either data frame or matrix with numeric values, or an
|
labels |
character vector used to label points in plots. The
length of the |
classes |
A subset of the |
colors |
character vector containing color names; this should
be the same length as the vector of |
usecor |
logical scalar. If |
x |
object of class |
tag |
character string to name the object, used as part of the plot title. |
mscale |
A real number. This is a hack; for some reason, the projection of the sample vectors into the principal component space computed from the matrix of group means seems to be off by a factor approximately equal to the square root of the average number of samples per group. Until we sort out the correct formula, this term can be adjusted until the group means appear to be in the correct place in the plots. |
cex |
Character expansion factor used only in the plot legend on the plot of PC correlations. |
... |
additional graphical parameters passed on to |
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.
The PCanova
function returns an object of the PCanova
class.
Objects should be created by calling the PCanova
generator function.
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.
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
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.
signature(object = PCanova)
: Write out a
summary of the object.
[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.
Kevin R. Coombes [email protected]
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.
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)
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)
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.
PerturbationClusterTest(data, FUN, nTimes=100, noise=1, verbose=TRUE, ...)
PerturbationClusterTest(data, FUN, nTimes=100, noise=1, verbose=TRUE, ...)
data |
A data matrix, numerical data frame, or
|
FUN |
A |
... |
Additional arguments passed to the classifying function, |
noise |
An optional numeric argument; the standard deviation of the mean zero Gaussian noise added to each measurement during each bootstrap. Defaults to 1. |
nTimes |
The number of bootstrap samples to collect. |
verbose |
A logical flag |
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.
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.
Class ClusterTest
, directly. See that class for
descriptions of the inherited methods image
and hist
.
signature(object = PerturbationClusterTest)
:
Write out a summary of the object.
Kevin R. Coombes [email protected]
Kerr MK, Churchill GJ.
Bootstrapping cluster analysis: Assessing the reliability of
conclusions from microarray experiments.
PNAS 2001; 98:8961-8965.
BootstrapClusterTest
,
ClusterTest
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)
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)
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.
plotColoredClusters(hd, labs, cols, cex = 0.8, main = "", line = 0, ...) pcc(hd, colors=NULL, ...)
plotColoredClusters(hd, labs, cols, cex = 0.8, main = "", line = 0, ...) pcc(hd, colors=NULL, ...)
hd |
An object with S3 class |
labs |
A vector of character strings used to label the leaves in the dendrogram |
.
cols |
A vector of color names suitable for passing to the
|
cex |
A numeric value; the character expansion parameter of
|
main |
A character string; the plot title |
line |
An integer determining how far away to plot the labels;
see |
colors |
A list; see details. |
... |
Any additional graphical parameters that can be supplied
when plotting an |
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).
The function has no useful return value; it merely produces a plot.
# 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$col <- c(A='blue', B='yellow') # assemble the list of factors colors <- list(AB=faccol, XYZ=fac2col, "tricky long name"=fac2col, another=BA) # cluster the samples hc <- hclust(distanceMatrix(fakedata, "pearson"), "ward") # plot the results pcc(hc, colors) #cleanup rm(d1, d2, d3, dd, hc, cols, labs) rm(fakedata, faccol, fac2col, BA, colors)
# simulate data from three different groups d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE) d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE) d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE) dd <- cbind(d1, d2, d3) # perform hierarchical clustering using correlation hc <- hclust(distanceMatrix(dd, 'pearson'), method='average') cols <- rep(c('red', 'green', 'blue'), each=10) labs <- paste('X', 1:30, sep='') # plot the dendrogram with color-coded groups plotColoredClusters(hc, labs=labs, cols=cols) # simulate another dataset fakedata <- matrix(rnorm(200*30), ncol=30) colnames(fakedata) <- paste("P", 1:30, sep='') # define two basic factors, with colors faccol <- list(fac=factor(rep(c("A", "B"), each=15)), col=c(A='red', B='green')) fac2col <- list(fac=factor(rep(c("X", "Y", "Z"), times=10)), col=c(X='cyan', Y='orange', Z='purple')) # add another factor that reverses the colors BA <- faccol BA$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)
Perform principal components analysis on the samples (columns) from a microarray or proteomics experiment.
SamplePCA(data, splitter=0, usecor=FALSE, center=TRUE) ## S4 method for signature 'SamplePCA,missing' plot(x, splitter=x@splitter, col, main='', which=1:2, ...)
SamplePCA(data, splitter=0, usecor=FALSE, center=TRUE) ## S4 method for signature 'SamplePCA,missing' plot(x, splitter=x@splitter, col, main='', which=1:2, ...)
data |
Either a data frame or matrix with numeric values or an
|
splitter |
If |
center |
A logical value; should the rows of the data matrix be centered first? |
usecor |
A logical value; should the rows of the data matrix be scaled to have standard deviation 1? |
x |
A |
col |
A list of colors to represent each level of the
|
main |
A character string; the plot title |
which |
A numeric vector of length two specifying which two principal components should be included in the plot. |
... |
Additional graphical parameters for |
.
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.
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 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.
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.
signature(x = SamplePCA, y = missing)
: Plot the
samples in a two-dimensional principal component space.
signature(object = SamplePCA)
: Project new
data into the principal component space.
signature(x = SamplePCA)
: Produce a bar
chart of the variances explained by each principal component.
signature(object = SamplePCA)
: Write out a
summary of the object.
signature(object = SamplePCA)
: interactively
identify points in the plot of a SamplePCA
object.
signature(object = SamplePCA)
: Add sample
identifiers to the scatter plot of a SamplePCA
object,
using the base text
function.
Kevin R. Coombes [email protected]
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)
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)