Gene Expression: Clustering

overratedbeltAI and Robotics

Nov 25, 2013 (3 years and 10 months ago)

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Masha Kazakov, Michal Rabani, 2/5/04


Gene Expression: Clustering

Abstract


Microarray technology is rapidly becoming a standard technique used in research laboratories all
across the world. This technology allows simultaneous profiling of the expression l
evels of tens of
thousands of genes, and potentially whole genomes in a single experiment. This unique power
provides scientists with an opportunity to look at the transcriptional profile of biologic systems,
processes in an unbiased fashion.

This amount o
f information cannot be analyzed without some
computational methods
. Therefore, a major computational task is to understand the structure of the
data that arises from this technology.


Gene clustering is a tool for arranging genes according to similarity i
n their expression patterns.
Classifying genes into clusters can lead to interesting biological insights. Patterns

seen in genome
-
wide expression experiments can give indications about unknown
regulatory elements
. Moreover,
genes with similar functions clu
ster together. Thus clustering genes of known functions with poorly
characterized genes may provide a simple means of gaining insights into the functions of these
uncharacterized genes
1
. Patterns

seen in genome
-
wide expression data can give indications abo
ut
the status of cellular processes and information about unknown biological pathways
2, 3
. In addition,
c
luster analysis is used for data reduction and visualization
1
.


We will focus on one of many clustering methods
-

hierarchical clustering, which is com
monly
used. Here relationships among genes are represented by a tree whose branch lengths reflect the
degree of similarity between the objects, as assessed by a pairwise similarity function
4
. This
method is useful to represent varying degrees of similarity

and more distant relationships among
groups of closely related genes.


To illustrate the method and it's power in analyzing biological data, we will review two
experiments in which pairwise average linkage clustering algorithm (hierarchical clustering) wa
s
applied to gene expression data collected from yeast cells
2, 3
.


The first is a genome
-
wide experiment in
Saccharomyces cerevisiae, designed to identify genes
whose regulation is cell
-
cycle dependent and to classify them
2
. It illustrates how understandi
ng of
cellular processes can be extracted from a set of microarray experiments followed by gene
clustering
. Furthermore it shows how new regulatory elements can be discovered using clustering
methods.


The second experiment deals with Saccharomyces cerevi
siae adaptation to environmental changes.
This experiment demonstrate how clustering enables us to find the relevant genes and characterize
biological pathways
3
.



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