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Outline: microarray data analysis


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Outline: microarray data analysis

Gene expression Microarrays


normalization scatter plots Inferential statistics

t-test ANOVA

Exploratory (descriptive) statistics distances


principal components analysis (PCA)


Descriptive statistics

Microarray data are highly dimensional: there are

many thousands of measurements made from a small number of samples.

Descriptive (exploratory) statistics help you to find meaningful patterns in the data.

A first step is to arrange the data in a matrix.

Next, use a distance metric to define the relatedness of the different data points. Two commonly used

distance metrics are:

-- Euclidean distance

-- Pearson coefficient of correlation

Page 203


What is a cluster?

A cluster is a group that has homogeneity (internal

cohesion) and separation (external isolation). The

relationships between objects being studied are

assessed by similarity or dissimilarity measures.



 Clustering is one of the most important unsupervised learning processes that organizing objects into groups whose members are similar in some way.

 Clustering finds structures in a collection of unlabeled data.

 A cluster is a collection of objects which are similar between them and are dissimilar to the objects

belonging to other clusters.


Motivation I

• Microarray data quality checking

– Does replicates cluster together?

– Does similar conditions, time points, tissue

types cluster together?


Motivation II

• Cluster genes  Prediction of functions of

unknown genes by known ones


Functional significant gene clusters

Two-way clustering

Gene clusters

Sample clusters


Motivation II

• Cluster genes  Prediction of functions of unknown genes by known ones

• Cluster samples  Discover clinical

characteristics (e.g. survival, marker

status) shared by samples.


Bhattacharjee et al. (2001) Human lung carcinomas mRNA expression

profiling reveals distinct adenocarcinoma


Proc. Natl. Acad. Sci.

USA, Vol. 98, 13790- 13795.


Calculate the similarity between all possible

combinations of two profiles

Two most similar clusters are grouped together to form

a new cluster

Calculate the similarity between the new cluster and

all remaining clusters.

Hierarchical Clustering


• Similarity

• Clustering



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