Download Paper Review: Identification of genes required for

Literature Review
of Microarray Data Mining
Xin Anders
March 24th, 2006
Gene Expression
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Genes are coding DNA segments which specify the
composition and structure of proteins.
DNA is transcribed into mRNA which in turn translates
the information into proteins.
The process of transcribing DNA information into
mRNA is known as gene expression.
The advances in microarray technologies revolutionized
the traditional one-gene-by-one-gene approach by
making it possible to study tens of thousands of genes
at once.
Microarray Technologies
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There are two types of microarray platforms: spotted
arrays (historically called cDNA arrays) and
photolithographic synthetic arrays (i.e. Affymetrix).
The fundamental difference between these two
platforms lies in the experiment setups: two-dyeslabeling versus one-dye labeling and co-hybridization
versus individual hybridization.
Although different data pre-processing are required for
these two platforms, most downstream data analyses
are similar for them. This review will focus on talking
about downstream data analyses.
Spotted Arrays
Figure 1. A diagram of a typical spotted
arrays experiment.
Visualization of up-regulation
and down regulation in one go.
No absolute gene expression
levels.
Source: wikipedia.com
Gene Chip (Affymetrix)
Figure 2. Each gene/EST is represented by various
probe sets scattered in the GeneChip. (A) Each probe
is made by up to 20 couple of oligos. (B) Each probe
set is made by perfect match (PM) and miss match
Source: Saviozzi S. et al. 2004
Statistical Analysis and Data
Mining Techniques
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Gene selection - identify differential gene expressions to
a particular biological problems.
Exploratory data analysis – extract (dis)similarities of
the gene expression levels (patterns) among all samples.
Discrimination analysis – train a classifier using gene
expression profiles to assign any new example to a
respective class.
Pathway analysis – find how genes interact as part of
pathways.
Gene functional annotations – associate functional
meaning to genes.
Differentially Expressed
Genes
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Traditionally, a fixed cut-off threshold is used to infer
the increase or decrease of gene expression for a singleslide experiment.
Statistical methods based on replicate array data for
ranking genes are better.
Perform an experiment as biological triplicates to
increase data reliabilities (Lee ML et al. 2000, Saviozzi
et al. 2004).
Statistical Tools to Rank
Genes form Replicated Data
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Generally, for a limited number of replicates,
parametric (student t-test) or non-parametric (MannWhitney test) is good.
However, when multiple hypotheses are tested in the
case of thousands of genes on a single microarray chip,
the false positives (Type I error) can increase sharply
with the number of hypotheses.
a 10,000 gene array with a P value set to 0.05
____> 10,000 * 0.05 (500) genes can be inferred
even though none is differentially expressed.
Statistical Tools to Rank
Genes form Replicated Data
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It is often accepted to have few false positives if the
majority of true positives are chosen (Leung YF 2003).
SAM (Significance Analysis of Microarrays) developed
by Tusher et al. is such a technique that it uses the
above concept as a tool to assist in determining a cutoff after performing adjusted t-tests.
SAM
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SAM measures the strength between gene expression
and the response variable (e.g. irradiated versus unirradiated) by using repeated permutations of the data
and assimilating a set of gene-specific adjusted t-tests.
The user can set the acceptable false discovery rate
(FDR), significant threshold, and fold change threshold.
A SAM Example
Experiment Setups:
2 states: Unirradiated (U) versus Irradiated (I)
2 biological duplicates: 1 and 2
2 technical duplicates: A and B
8 hybridizations
U1A, U1B, U2A, U2B
I1A, I1B, I2A, I2B
Source: Tusher VG et al. 2000
A SAM Example
Relative difference for the gene i is
d(i) = (meanI(i) – meanU(i))/(s(i) + s0)
s(i) is the standard deviation of repeated expression
measurement:
s(i)  a{m sqr ( xm (i)  xI (i))  n sqr ( xn (i)  xU (i))}
Genes are ranked by the magnitude of d(i) so that d(1)
is the largest relative difference, d(2) is the second
largest relative difference and so on.
Source: Tusher VG et al. 2000
A SAM Example
8 hybridizations
U1A, U1B, U2A, U2B
I1A, I1B, I2A, I2B
Permutations balanced on biologic
duplicates are generated.
U1A I1A U2A I2A
U1B I1B U2B I2B
…
Calculate the observed
relative difference d(i)
Calculate dp(i) for
each permutation
dE(i): average over
the balanced
permutations
Source: Tusher VG et al. 2000
A SAM Example
Now we have:
Observed relative difference d(i)
Expected relative difference dE(i) calculated from the
permutations
A threshold can be chosen to yield
significant genes.
Source: Tusher VG et al. 2000
A SAM Example
Now we have:
N significant genes
We want to determine the false discovery rate (FDR):
1. Horizontal cutoffs are defined as the smallest d(i) and
the least negative d(i) for significantly induced and
depressed respectively.
2. For each permutation, the number of false significant genes is
Counted.
3. The estimated number of false significant genes F is the average
Of the number of false significant genes in all permutations.
4. FDR can be calculated as F/N.
Source: Tusher VG et al. 2000
SAM
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SAM clearly outperforms fold test, t-test and the
ANOVA based bootstrap method (Marchal K. et al
2002).
The number of permutations is affected by the number
of replicates and the user should perform the full set of
permutations.
Usually, a significant cutoff is chosen to give less than
one false positive (Saviozzi et al. 2004).
Statistical Analysis and Data
Mining Techniques





Gene selection - identify differential gene expressions to
a particular biological problems.
Exploratory data analysis – extract (dis)similarities of
the gene expression levels (patterns) among all samples.
Discriminant analysis – train a classifier using gene
expression profiles to assign any new example to a
respective class.
Pathway analysis – find how genes interact as part of
pathways.
Gene functional annotations – associate functional
meaning to genes.
Exploratory Data Analysis
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In a more complex experiment, it is essential to extract
gene expression patterns among all samples.
Exploratory data analysis, also known as unsupervised
data analysis, is essentially a grouping technique that
aims to find genes with similar behaviors and doesn’t
require prior response measurements for the items to be
grouped.
Commonly used clustering techniques include:
hierachical clustering, self organization maps, k-means
clustering, and principal component analysis.
Expression Matrix
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To interpret the results from multiple experiments,
creating an expression matrix is a common visual
representation technique.
Each column of the matrix represents a single
experiment and each row of the matrix represents a
particular gene. Coloring the matrix provides an
intuitive visual representation.
Experiment 1, 2, 3
Gene 1, 2
Each member is log2(ratio). If a
value is 0, the color is black. A
positive value is red and a
negative value is green.
Before Clustering The Data
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The data may need to be rescaled to prevent dominating
values from obscuring other important difference.
Decide what kind of distance measurement should be
used.
Hierarchical Clustering
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It is an agglomerative approach in which single
expression profiles are joined to form groups, which are
further joined until the completion of the process.
Initially, each cluster contains a single gene.
First, the pairwise distance is calculated for all genes.
Second, two most similar genes g1 and g2 form a new
cluster {g1, g2}.
Third, the distance is calculated between all other
clusters and the new cluster.
Repeat step 2-3 until all objects are in one cluster.
Hierarchical Clustering

There are different methods to calculate the distances
between the growing clusters and the other remaining
clusters.
1. Single-linkage clustering;
2. Complete-linkage clustering;
3. Average-linkage clustering;
4. Weighted pair-group average;
5. Within-group clustering;
6. Ward’s method.
Single Linkage Clustering
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The distance between two clusters i and j is calculated
as the minimum distance between a member of i and a
member of j.
This method tends to produce loose clusters and often
result in “chaining” – a sequential addition of single
samples into an existing cluster.
Complete Linkage Clustering
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The distance between two clusters i and j is calculated
as the greatest distance between a member of i and a
member of j.
This method tends to produce compact clusters and
clusters are often similar in size.
Average Linkage Clustering
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The distance between clusters is calculated with average
values.
There are many ways to calculate the average value.
The most common one is unweighted pair-group
method average (UPGMA).
In UPGMA, the distance between each point in one
cluster and all points in another cluster is calculated for
the average value. The two clusters with the lowest
average value are joined to form a new cluster.
Average Linkage Clustering
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Weighted pair-group average is identical to UPGMA
except that the size of the respective cluster is used as a
weight. This is useful when the cluster size is greatly
varied.
Within-group clustering is similar to UPGMA except
that the cluster average is used instead of all individual
elements from a cluster.
Ward’s method determines whether to include a cluster
by calculating the total sum of squared deviations from
the mean of a cluster and joining clusters in such a way
that it produces the smallest possible increase in the
sum of square errors.
Hierarchical Clustering
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Typically, average linkage clustering is used for gene
expression data.
As clusters grow in size, the expression vector
representing the cluster may no longer represent any
gene in the cluster.
Furthermore, if a mistake is introduced early in the
process, it can’t be corrected.
K-mean/median Clustering
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K-mean/median clustering is a good alternative to
hierarchical clustering if there is advanced knowledge
about the number of the clusters should be represented
in the data.
K-means/medians Clustering
1. Specify the fixed number (k) of clusters;
2. Randomly assign genes to clusters;
3. Calculate the mean/median expression vector for
each cluster which is used to calculate the
distance between clusters;
4. Shuffle genes among clusters so that each gene is
now in a cluster whose mean/median
expression vector is closest to that gene’s
expression vector.
5. Repeat Steps 3 and 4 until genes can’t be shuffled any
more.
Self-Organization Map
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Self-organization map (SOM) assigns genes to a series of
partition on the basis of the similarity of their
expression vectors to reference vectors that are defined
for each partition.
Before genes can be assigned to partitions, the user
defines a geometric configuration for the partitions.
Random vectors are generated for each partition and
then are trained so that the data are most effectively
separated.
Principal Component Analysis
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Some of the data might contain redundant information.
Principal component analysis (PCA) picks out patterns
in the data while reducing the effective dimensionality
without significant loss of information.
PCA is difficult to be used alone but powerful when
combined with another classification technique such as
k-means clustering and SOM.
Statistical Analysis and Data
Mining Techniques





Gene selection - identify differential gene expressions to
a particular biological problems.
Exploratory data analysis – extract (dis)similarities of
the gene expression levels (patterns) among all samples.
Discrimination analysis – train a classifier using gene
expression profiles to assign any new sample to a
respective class.
Pathway analysis – find how genes interact as part of
pathways.
Gene functional annotations – associate functional
meaning to genes.
Discrimination Analysis
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
It is also known as supervised data analysis, which
trains a classifier algorithm using gene expression
profiles to classify samples.
This has great promise in clinical diagnostics and has
been used successfully in several recent studies.
Clinical Diagnostics with
Supervised Learning
T.R. Golub’s group at Whitehead Institute/MIT had
several successful cases for certain cancers’ class
prediction.
Shipp MA et al. (2002) Diffuse large B-cell lymphoma outcome prediction by gene
expression profiling and supervised machine learning. Nat. Med. 8, 68-74.
Pomeroy SL et al. (2002) Prediction of central nervous system embryonal tumour outcome
based on gene expression. Nature 415, 436-442.
An Example of Clinical
Diagnostics
Experiment setup:
Known classification for Cancer1 (AML) and Cancer2 (ALL)
Known samples: 27 ALL, 11 AML
Affymetrix chips (6817 genes)
 Find a set of informative genes whose gene expression patterns
were strongly correlated with the class distinction to be predicted.
 Build a classifier based on the set of informative genes.
Source: T. R Golub et al. Molecular classfication of cancer:
Class discovery and class prediction by gene expression monitoring. Science 286
(1999) 531-537.
An Example of Clinical
Diagnostics
Neighborhood analysis.
An Example of Clinical
Diagnostics
Class predictor.
Discrimination Analysis
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The challenge for supervised data analysis is to
generalize a classifier for all situations.
Over-training on the same dataset would result in overfitting.
Different cross-validation (e.g. leave-one-out) methods
can be used to establish a balance between accuracy and
generalizability.
Statistical Analysis and Data
Mining Techniques





Gene selection - identify differential gene expressions to
a particular biological problems.
Exploratory data analysis – extract (dis)similarities of
the gene expression levels (patterns) among all samples.
Discrimination analysis – train a classifier using gene
expression profiles to assign any new example to a
respective class.
Pathway analysis – find how genes interact as part of
pathways.
Gene functional annotations – associate functional
meaning to genes.
Pathway Analysis
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Genes never work alone in a biological system.
Analyzing microarray data in a pathway perspective
can lead a higher level of understanding of the system.
A natural extension of clustering analysis: if genes are
assigned to the same cluster, they may be involved in a
same signal pathway. By analyzing the promoters of
genes, a higher level of network may be unveiled (Pilpel
Y 2001).
Various models are used to construct networks for
microarray data. Bayesian network and Boolean
network are two commonly used models.
A Genetic Regulatory System
Source: HD Jong. Modeling and simulation of genetic regulatory systems: a literature
review.
J. Comp. Biol. 9 (2002) 67-103
A Simple Example of Bayesian
Network
A graph, conditional probability distributions for the random
Variables, the joint probability distribution, and conditional
Independency.
Source: HD Jong. Modeling and simulation of genetic regulatory systems: a literature
review.
J. Comp. Biol. 9 (2002) 67-103
A Simple Example of Boolean
Network
For example, given a state vector 000 at t = 0, the system will move
to a state 011 at the next time point t = 1
The induction of a gene is a deterministic function of the state of
a group of other genes.
Source: HD Jong. Modeling and simulation of genetic regulatory systems: a literature
review.
J. Comp. Biol. 9 (2002) 67-103
Pathway Analysis
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A free software called Pathway Processor developed by
the Bauer Center for Genomics at Harvard can map
expression data onto metabolic pathways and evaluate
which metabolic pathways are most affected. Fisher
Exact test is used to score pathways according to the
probability that as many or more genes in a pathway
would be altered in a given experiment than by chance
alone.
Statistical Analysis and Data
Mining Techniques





Gene selection - identify differential gene expressions to
a particular biological problems.
Exploratory data analysis – extract (dis)similarities of
the gene expression levels (patterns) among all samples.
Discrimination analysis – train a classifier using gene
expression profiles to assign any new example to a
respective class.
Pathway analysis – find how genes interact as part of
pathways.
Gene functional annotations – associate functional
meaning to genes.
Gene Functional Annotation
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In order to know whether some specific biological
process is strongly affected by transcriptional
expression, we have to associate functional meaning to
genes by using gene functional annotations.
Researchers rely on robust gene annotations to link
functional to transcriptional profiling.
Gene Ontology (GO) is a commonly used control
vocabulary for describing the roles of genes and gene
products in any organism.
Gene Ontology

GO is divided into three categories: molecular function,
biological process, and cellular component.
[Term]
id: GO:0000786
name: nucleosome
namespace: cellular_component
def: "A complex comprised of DNA wound around a
multisubunit core and associated proteins, which
forms the primary packing unit of DNA into higher
order structures." [GOC:elh]
is_a: GO:0043234 ! protein complex
relationship: part_of GO:0000785 ! chromatin
Gene Ontology
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GO terms are organized in directed acyclic graphs,
which differ from hierarchies in that a child term can
have many parent terms.
Monosaccharide
biosynthesis
Hexose metabolism
Hexose biosynthesis
Gene Ontology
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GO terms become associated with their appropriate
gene products through collaborating databases. These
databases annotate genes with GO terms, providing
references and indicating what kind of evidence is
available to support the annotations.
References
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Aas Km(2001). Microarray data mining: a survey. Norsk Regnesentral:
Norwegian Computing Center.
Dudoit S. et al. (2000). Comparison of discrimination methods for the
classification of tumors using gene expression data. Technical report no. 576,
University of Claifornia, Berkely.
Saviozzi S. et al. (2004). Microarray data analysis and mining. Methods Mol. Med,
94: 67-89.
Lee ML et al. (2000). Importance of replication in microarray gene expression
studies: statistical methods and evidence from repetitive cDNA hybridizations.
Proc. Natl. Acad. Sci. USA, 97: 9834-39.
Leung YF and Cavalieri D (2003). Fundamentals of cDNA microarray data
analysis. Trends Genet., 19(11): 649-59.
Tusher VG et al. (2001). Significance analysis of microarrays applied to ionizing
radiation response. Proc. Natl. Acad. Sci. USA, 98: 5116-21.
Marchal K et al. (2002). Comparison of different methodologies to identify
differentially expressed genes in two-example cDNA microarrays. J. Bio Systems,
10: 409-430.
Eisen MB et al. (1998).Cluster Analysis and display of genome-wide expression
patterns. Proc. Natl. Acad. Sci. USA, 96: 2907-2912.
References
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Tavazoie S et al. (1999). Systematic determination of genetic network
architecture. Nat. Genet. 22, 281-285.
Raychaudhuri S (2000). Principal components analysis to summarize microarray
experiments: application to sporulation time series. Pac. Symp. Biocomput. 455466.
Tamayo P et al. (1999). Interperting patterns of gene expression with selforganizing maps: methods and application to hematopoietic differentiation. Proc.
Natl. Aca. Sci. 96: 2907-2912.
Quackenbush J (2001). Computational analysis of microarray data. Nat. Rev.
Genet., 2L 418-27.
Pomeroy SL et al. (2002). Prediction of central nervous system embryonal tumor
outcome based on gene expression. Nature 415: 436-442.
Shipp MA et al. (2002). Diffuse large B-cell lymphoma outcome prediction by
gene-expression profiling and supervised machine learning. Nat. Med. 8, 68-74.
Golub et al. (1999). Molecular classification of cancer: class discovery and class
prediction by gene expression monitoring. Science 286: 531-537.
Pilpel Y et al. (2001). Identifying regulatory networks by combinatorial analysis of
promoter elements. Nat. Genet. 29: 153-159.
De Jong, H (2002). Modeling and simulation of genetic regulatory systems: a
literature review. J. Comput. Biol. 9: 67-103.
Pavlidi P et al. (2004). Using the gene ontology for microarray data mining: a
comparison of methods and application to age effects in human prefrontal cortex.
Neuro. Res. 29: 1213-22.
Li SH et al. (2004). Microarray data mining using gene ontology. Medinfo 2004.
References
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Goble CA et al. (2001). Transparent access to multiple bioinformatics information
sources. IBM Systems Journal 40: 532- 551.