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Microarray Data Analysis
Class discovery and Class prediction:
Clustering and Discrimination
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Gene expression profiles

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Many genes
show definite
changes of
expression
between
conditions
These
patterns are
called gene
profiles
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Motivation (1):
The problem of finding patterns

It is common to have hybridizations where
conditions reflect temporal or spatial aspects.
 Yeast
cycle data
 Tumor data evolution after chemotherapy
 CNS data in different part of brain

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Interesting genes may be those showing
patterns associated with changes.
Our problem seems to be distinguishing
interesting or real patterns from meaningless
variation, at the level of the gene
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Finding patterns: Two approaches

If patterns already exist 
Profile comparison (Distance analysis)
 Find
the genes whose expression fits specific,
predefined patterns.
 Find the genes whose expression follows the
pattern of predefined gene or set of genes.

If we wish to discover new patterns 
Cluster analysis (class discovery)
 Carry
out some kind of exploratory analysis to
see what expression patterns emerge;
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Motivation (2): Tumor classification

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A reliable and precise classification of tumours is
essential for successful diagnosis and treatment of
cancer.
Current methods for classifying human malignancies rely
on a variety of morphological, clinical, and molecular
variables.
In spite of recent progress, there are still uncertainties in
diagnosis. Also, it is likely that the existing classes are
heterogeneous.
DNA microarrays may be used to characterize the
molecular variations among tumors by monitoring gene
expression on a genomic scale. This may lead to a
more reliable classification of tumors.
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Tumor classification, cont

There are three main types of statistical problems
associated with tumor classification:
1. The identification of new/unknown tumor classes
using gene expression profiles - cluster analysis;
2. The classification of malignancies into known
classes - discriminant analysis;
3. The identification of “marker” genes that
characterize the different tumor classes - variable
selection.
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Cluster and Discriminant analysis

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These techniques group, or equivalently classify,
observational units on the basis of
measurements.
They differ according to their aims, which in turn
depend on the availability of a pre-existing basis
for the grouping.
 In
cluster analysis (unsupervised learning, class
discovery) , there are no predefined groups or labels for
the observations,
 Discriminant analysis (supervised learning, class
prediction) is based on the existence of groups (labels)
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Clustering microarray data

Cluster can be applied to genes (rows),
mRNA samples (cols), or both at once.
 Cluster
samples to identify new cell or tumour
subtypes.
 Cluster rows (genes) to identify groups of coregulated genes.
 We can also cluster genes to reduce
redundancy e.g. for variable selection in
predictive models.
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Advantages of clustering

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Clustering leads to readily interpretable figures.
Clustering strengthens the signal when
averages are taken within clusters of genes
(Eisen).
Clustering can be helpful for identifying patterns
in time or space.
Clustering is useful, perhaps essential, when
seeking new subclasses of cell samples (tumors,
etc).
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Applications of clustering
 Alizadeh
et al (2000) Distinct types of diffuse
large B-cell lymphoma identified by gene
expression profiling.
Three subtypes of lymphoma (FL, CLL and
DLBCL) have different genetic signatures.
(81 cases total)
 DLBCL group can be partitioned into two
subgroups with significantly different survival. (39
DLBCL cases)

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Clusters
on both
genes
and
arrays
Taken from
Nature February, 2000
Paper by Allizadeh. A et al
Distinct types of diffuse large
B-cell lymphoma identified by
Gene expression profiling,
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Discovering tumor subclasses

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DLBCL is clinically
heterogeneous
Specimens were
clustered based on their
expression profiles of GC
B-cell associated genes.
Two subgroups were
discovered:
 GC B-like DLBCL
 Activated B-like
DLBCL
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Basic principles of clustering
Aim: to group observations that are “similar” based on
predefined criteria.
Issues: Which genes / arrays to use?
Which similarity or dissimilarity measure?
Which clustering algorithm?

It is advisable to reduce the number of genes
from the full set to some more manageable
number, before clustering. The basis for this
reduction is usually quite context specific, see
later example.
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Measures of dissimilarity
Correlation
 Distance
Manhattan
Euclidean
Mahalanobis distance
Many more ….

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Two basic types of methods
Partitioning
Hierarchical
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Partitioning methods
Partition the data into a pre-specified number k of
mutually exclusive and exhaustive groups.
Iteratively reallocate the observations to clusters
until some criterion is met, e.g. minimize within
cluster sums of squares.
Examples:
 k-means, self-organizing maps (SOM), PAM, etc.;
 Fuzzy: needs stochastic model, e.g. Gaussian
mixtures.
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Hierarchical methods
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Hierarchical clustering methods produce a tree
or dendrogram.
They avoid specifying how many clusters are
appropriate by providing a partition for each k
obtained from cutting the tree at some level.
The tree can be built in two distinct ways
 bottom-up: agglomerative clustering;
 top-down: divisive clustering.
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Agglomerative methods
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Start with n clusters.
At each step, merge the two closest clusters
using a measure of between-cluster dissimilarity,
which reflects the shape of the clusters.
Between-cluster dissimilarity measures
 Mean-link: average of pairwise dissimilarities
 Single-link: minimum of pairwise
dissimilarities.
 Complete-link: maximum& of pairwise
dissimilarities.
 Distance between centroids
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Distance between centroids
Complete-link
Single-link
Mean-link
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Divisive methods
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Start with only one cluster.
At each step, split clusters into two parts.
Split to give greatest distance between two new
clusters
Advantages.
 Obtain the main structure of the data, i.e.
focus on upper levels of dendogram.
Disadvantages.
 Computational difficulties when considering all
possible divisions into two groups.
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Illustration of points
In two dimensional
space
Agglomerative
1 5 2 3 4
1,2,3,4,5
4
3
1,2,5
5
1
3,4
1,5
2
1
5
2 3
4
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Tree re-ordering?
Agglomerative
1 5 2 3 4
2 1 53 4
1,2,3,4,5
4
3
1,2,5
5
1
3,4
1,5
2
1
5
2 3
4
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Partitioning or Hierarchical?
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Partitioning:
 Advantages
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Optimal for certain criteria.
Genes automatically
assigned to clusters
 Disadvantages
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Need initial k;
Often require long
computation times.
All genes are forced into a
cluster.
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Hierarchical
 Advantages
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Faster computation.
Visual.
 Disadvantages
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Unrelated genes are
eventually joined
Rigid, cannot correct
later for erroneous
decisions made earlier.
Hard to define clusters.
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Hybrid Methods
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Mix elements of Partitioning and
Hierarchical methods
 Bagging

Dudoit & Fridlyand (2002)
 HOPACH

van der Laan & Pollard (2001)
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Three generic clustering problems
Three important tasks (which are generic) are:
1. Estimating the number of clusters;
2. Assigning each observation to a cluster;
3. Assessing the strength/confidence of
cluster assignments for individual
observations.
Not equally important in every problem.
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Estimating number of clusters
using silhouette
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Define silhouette width of the observation as :
S = (b-a)/max(a,b)
Where a is the average dissimilarity to all the points in the cluster
and b is the minimum distance to any of the objects in the other
clusters.
Intuitively, objects with large S are well-clustered while the ones with
small S tend to lie between clusters.
How many clusters: Perform clustering for a sequence of the
number of clusters k and choose the number of components
corresponding to the largest average silhouette.
Issue of the number of clusters in the data is most relevant for novel
class discovery, i.e. for clustering samples
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Estimating number of clusters
using the bootstrap
There are other resampling (e.g. Dudoit and Fridlyand,
2002) and non-resampling based rules for estimating the
number of clusters (for review see Milligan and Cooper
(1978) and Dudoit and Fridlyand (2002) ).
The bottom line is that none work very well in complicated
situation and, to a large extent, clustering lies outside a
usual statistical framework.
It is always reassuring when you are able to characterize a
newly discovered clusters using information that was not
used for clustering.
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Limitations
Cluster analyses:
 Usually outside the normal framework of statistical
inference;
 less appropriate when only a few genes are likely to
change.
 Needs lots of experiments
 Always possible to cluster even if there is nothing going
on.
 Useful for learning about the data, but does not provide
biological truth.
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Examples
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The data
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We will use the dataset presented in van't Veer et
al. (2002) which is available at
http://www.rii.com/publications/2002/vantveer.htm.
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These data come from a study of gene expression in 91
breast cancer node-negative tumors.
The training samples consisted of 78 tumors, 44 of which
did not recur within 5 years of diagnosis and 34 did.
Among the test samples, 7 have not recurred within 5
years and 12 did.
The data were collected on two color Agilant oligo arrays
containing about 24K genes.
.
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Preprocessing
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The data has been filtered using procedures described in
the original manuscript.
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Only genes showing 2-fold differential expression and p-value for
a gene being expressed < 0.01 in more than 5 samples are
retained.
There are 4,348 such genes.
Missing values were imputed using k-nearest neighbors
imputation procedure (Troyanskaya, et al, 2001).

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There, for each gene containing at leats one missing value we
find 5 genes most highly correlated with it and take average of
their value for the sample in which a value for a given gene is
missing.
The missing value is replaced with the average.
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R data
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The filtered gene expression levels are stored in
a 4348 × 97 matrix named bcdata with rows
corresponding to genes and columns to mRNA
samples.
Additionally, the labels are contained in the 97element vector surv.resp with 0 indicating good
outcome (no recurrence within 5 years after
diagnosis) and 1 indicating bad outcome
(recurrence within 5 years after diagnosis).
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Hierarchical clustering (1)
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Start performing a hierarchical clustering on the mRNA
samples
using correlation as similarity function and
complete linkage agglomeration
library(stats)
x1<-as.dist(1 - cor(bcdata)
clust.cor <- hclust(x1), method="complete")
plot(clust.cor, cex = 0.6)
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Hierarchical clustering (1)
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Hierarchical clustering (2)
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Perform a hierarchical clustering on the mRNA samples
using Euclidean distance and
average linkage agglomeration.
Results can differ significantly.
X2<-dist(t(bcdata)
clust.euclid <- hclust(x2), method =
"average")
plot(clust.euclid, cex = 0.6)
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20
37:good
66:bad73:bad
34:good
97:bad
77:bad
94:bad
55:bad
78:bad
75:bad
43:good
64:bad
96:bad41:good
23:good
39:good
72:bad
15:good
4:good
63:bad
32:good
22:good
28:good
56:bad
88:good
2:good
33:good
1:good
6:good
49:bad
58:bad
47:bad
9:good
13:good
3:good
81:bad
83:good
14:good
42:good
26:good
92:bad
61:bad
70:bad
18:good
31:good
10:good
51:bad
5:good
86:good
76:bad
19:good
36:good
29:good
46:bad
30:good
27:good
82:good
84:good
17:good
7:good
21:good
59:bad
89:bad
35:good
85:good
95:bad
60:bad
69:bad
62:bad
52:bad
11:good
79:bad
16:good
40:good
38:good
25:good
45:bad 53:bad
68:bad
57:bad
93:bad
80:bad
8:good
71:bad
24:good
67:bad
12:good
65:bad
74:bad
44:good
48:bad
50:bad
20:good
87:good
90:bad
91:bad
10
30
Height
40
54:bad
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Hierarchical clustering (2)
Cluster Dendrogram
dist(t(bcdata))
hclust (*, "average")
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Comparison between orderings
Sample
1:good
2:good
6:good
7:good
14:good
19:good
21:good
23:good
30:good
39:good
41:good
42:good
49:bad
58:bad
61:bad
70:bad
72:bad
88:good
92:bad
3:good
9:good
13:good
47:bad
83:good
4:good
11:good
16:good
40:good
52:bad
63:bad
CORR.GROUP EUC.GROUP
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
IN THIS CASE WE
OBSERVE THAT:
 Clustering
based on
correlation and
complete linkage
dsitributes samples
more uniformly
between groups
 Euclidean-average
linkage combination
yields one huge group
and many small one
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Partitioning around medoids
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If we assume a fixed number of clusters we can
use a partitioning method such as PAM
It is a robust version of k-means which clusters
data around the medoids
library(cluster)
x3<-as.dist(1-cor(bcdata))
clust.pam <- pam(x3, 5, diss =TRUE)
clusplot(clust.pam, labels = 5,
col.p = clust.pam$clustering)
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PAM clustering (2)
0.2 0.4 0.6
2
4
3
1
-0.2
5
-0.6
Component 2
clusplot(pam(x = x3, k = 5, diss = TRUE))
-0.5
0.0
0.5
Component 1
These two components explain 24.97 % of the point variability.
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