Slides_SB3.ppt

MSc GBE Course:
Genes: from sequence to function
Brief Introduction to
Systems Biology
Sven Bergmann
Department of Medical Genetics
University of Lausanne
Rue de Bugnon 27 - DGM 328
CH-1005 Lausanne
Switzerland
work: ++41-21-692-5452
cell: ++41-78-663-4980
http://serverdgm.unil.ch/bergmann

Course Overview
• Basics: What is Systems Biology?
• Standard analysis tools for large
datasets
• Advanced analysis tools
• Systems approach to “small”
networks

What is Systems Biology?
• To understand biology at the system level, we
must examine the structure and dynamics of
cellular and organismal function, rather than
the characteristics of isolated parts of a cell or
organism. Properties of systems, such as
robustness, emerge as central issues, and
understanding these properties may have an
impact on the future of medicine.
Hiroaki Kitano

• To me, systems biology seeks to explain
biological phenomenon not on a gene by gene
basis, but through the interaction of all the cellular
and biochemical components in a cell or an
organism. Since, biologists have always sought to
understand the mechanisms sustaining living
systems, solutions arising from systems biology
have always been the goal in biology. Previously,
however, we did not have the knowledge or the
tools.
Edison T Liu
Genome Institute of Singapore

• addresses the analysis of entire biological systems
• interdisciplinary approach to the investigation of all the
components and networks contributing to a biological
system
• [involves] new dynamic computer modeling programs
which ultimately might allow us to simulate entire
organisms based on their individual cellular components
• Strategy of Systems Biology is dependent on interactive
cycles of predictions and experimentation.
• Allow[s Biology] to move from the ranks of a descriptive
science to an exact science.
(Quotes from SystemsX.ch website)

 identify elements (genes, molecules, cells, …)
 ascertain their relationships (co-expressed, interacting, …)
 integrate information to obtain view of system as a whole
Large (genomic) systems
• many uncharacterized
elements
• relationships unknown
• computational analysis should:
 improve annotation
 reveal relations
 reduce complexity
Small systems
• elements well-known
• many relationships established
• quantitative modeling of
systems properties like:
 Dynamics
 Robustness
 Logics

Part 1: Basics
Motivation:
• What is a “systems biology approach”?
• Why to take such an approach?
• How can one study systems properties?
Practical Part:
• First look at a set of genomic expression data
• How to have a global look at such datasets?
• Distributions, mean-values, standard deviations, z-
scores
• T-tests and other statistical tests
• Correlations and similarity measures
• Simple Clustering

First look at a set of genomic
expression data

DNA microarray experiments monitor expression
levels of thousands of genes simultaneously:
• allows for studying the genome-wide transcriptional
response of a cell to interior and exterior changes
• provide us with a first step towards understanding
gene function and regulation on a global scale
test
control

Microarrays generate massive data

Log-ratios of expression values
Log ratios indicate differential expression!
)
log(
)
log(
)
log( control
test
control
test E
E
E
/
E
r 


0
+
-
control
test E
E  control
test E
E 
control
test E
~
E

Consolidate data from multiple chips into
one table and use color-coding
Many KOs
(conditions)
1 2 3 4 5
1000
2000
3000
4000
5000
6000
-4
-3
-2
-1
0
1
2
3
4
genes
conditions
log-
ratio
r
Knock Out (KO)

Rosetta data: The real world …
50 100 150 200 250 300
1000
2000
3000
4000
5000
6000 -6
-4
-2
0
2
4
6
genes
conditions
Most genes exhibit little differential expression!
r

-4 -2 0 2 4 6 8
0
500
1000
1500
2000
2500
Histogram shows distribution
ade1 deletion mutant exhibits small differential expression
in most genes!
#
)
log( control
test E
/
E
r 

158 160 162 164 166 168
1000
2000
3000
4000
5000
6000 -6
-4
-2
0
2
4
6
Rosetta data: Zooming in …
Only few genes exhibit large differential expression!
genes
conditions

-8 -6 -4 -2 0 2 4 6 8
0
100
200
300
400
500
600
700
Histogram shows distribution
ssn6 deletion mutant exhibits large differential expression
in many genes!
#
)
log( control
test E
/
E
r 

-8 -6 -4 -2 0 2 4 6 8
0
100
200
300
400
500
600
700
Quantification of distribution
Mean and Standard Deviation (Std) characterize distribution
#
)
log( control
test E
/
E
r 
Mean: μ= =x
Outliers
Std: =

-4 -2 0 2 4 6 8
0
500
1000
1500
2000
2500
-8 -6 -4 -2 0 2 4 6 8
0
100
200
300
400
500
600
700
Comparing distributions
Are the expression values of ade1 different from those of ssn6?
# #
)
log( control
test E
/
E
r  )
log( control
test E
/
E
r 
ade1 ssn1
μ = 0.2366
 = 1.9854
μ = -5.5 · 10-5
 = 0.1434 ?

Student’s T-test
t-statistic: difference between means in units of average error
Significance can be translated into p-value (probability) assuming normal distributions
http://www.physics.csbsju.edu/stats/t-test.html

History: W. S. Gossett [1876-1937]
• The t-test was developed by W. S. Gossett, a statistician
employed at the Guinness brewery. However, because
the brewery did not allow employees to publish their
research, Gossett's work on the t-test appears under the
name "Student" (and the t-test is sometimes referred to
as "Student's t-test.") Gossett was a chemist and was
responsible for developing procedures for ensuring the
similarity of batches of Guinness. The t-test was
developed as a way of measuring how closely the yeast
content of a particular batch of beer corresponded to the
brewery's standard.
http://ccnmtl.columbia.edu/projects/qmss/t_about.html

Pearson correlations (Graphic)
Comparing profiles X and Y (not distributions!):
What is the tendency that high/low values in X match high/low values in Y?
http://davidmlane.com/hyperstat/A34739.html
Y
=
(y
i
)
X = (xi)
Each dot is
a pair (xi,yi)

Pearson correlations: Formulae
(simple version using z-scores)
(complicated version)
r

Similarity according to all conditions
(“Democratic vote”)
Clustering-
coefficient
conditions
1 2 3 4 5
1
2
r12 ~ -1
gene
1 2 3 4 5
1
2 r12 ~ 0
gene
1 2 3 4 5
1
2 r12 ~ 1
gene
Pearson correlations: Intuition

Pearson correlations: Caution!
High correlation does not necessarily mean co-linearity!
r=0.8 r=0.8
r=0.8 r=0.8

(Hierarchical Agglomerative) Clustering
Join most correlated samples and replace correlations
to remaining samples by average, then iterate …
http://gepas.bioinfo.cipf.es/cgi-bin/tutoX?c=clustering/clustering.config

Clustering of the real expression data

K-means Clustering
2. Assign each data point to
closest centroid
1. Start with random
positions of centroids ( )
“guess” k=3 (# of clusters)
http://en.wikipedia.org/wiki/K-means_algorithm

Hierachical Clustering
Plus:
• Shows (re-orderd) data
• Gives hierarchy
Minus:
• Does not work well for many genes
(usually apply cut-off on fold-change)
• Similarity over all genes/conditions
• Clusters do not overlap

Overview of “modular” analysis tools
• Cheng Y and Church GM. Biclustering of expression data.
(Proc Int Conf Intell Syst Mol Biol. 2000;8:93-103)
• Getz G, Levine E, Domany E. Coupled two-way clustering analysis of gene
microarray data. (Proc Natl Acad Sci U S A. 2000 Oct 24;97(22):12079-84)
• Tanay A, Sharan R, Kupiec M, Shamir R. Revealing modularity and organization
in the yeast molecular network by integrated analysis of highly heterogeneous
genomewide data. (Proc Natl Acad Sci U S A. 2004 Mar 2;101(9):2981-6)
• Sheng Q, Moreau Y, De Moor B. Biclustering microarray data by Gibbs sampling.
(Bioinformatics. 2003 Oct;19 Suppl 2:ii196-205)
• Gasch AP and Eisen MB. Exploring the conditional coregulation of yeast gene
expression through fuzzy k-means clustering.
(Genome Biol. 2002 Oct 10;3(11):RESEARCH0059)
• Hastie T, Tibshirani R, Eisen MB, Alizadeh A, Levy R, Staudt L, Chan WC, Botstein
D, Brown P. 'Gene shaving' as a method for identifying distinct sets of genes
with similar expression patterns. (Genome Biol. 2000;1(2):RESEARCH0003.)
… and many more! http://serverdgm.unil.ch/bergmann/Publications/review.pdf

How to “hear” the relevant genes?
Song A
Song B

Inside CTWC: Iterations
Depth Genes Samples
Init G1 S1
1 G1(S1) G2,G3,…G5 S1(G1) S2,S3
2 G1(S2)
G1(S3)
G6,G7,….G13
G14,…G21
S1(G2)
…
S1(G5)
S4,S5,S6
S10,S11
None
3 G2(S1)…G2(S3)
…
G5(S1)…G5(S3)
G22…
…
…G97
S2(G1)…S2(G5)
S3(G1)…S3(G5)
S12,…
…S51
4 G1(S4)
…
G1(S11)
G98,..G105
…
G151,..G160
S1(G6)
…
S1(G21)
S52,...
S67
5 G2(S4)...G2(S11)
…
G5(S4)...G5(S11)
G161…
…
…G216
S2(G6)...S2(G21)
S3(G6)…S3(G21)
S68…
…S113
Two-way clustering

• No need for correlations!
• decomposes data into “transcription modules”
• integrates external information
• allows for interspecies comparative analysis
One example in more detail:
The (Iterative) Signature Algorithm:
J Ihmels, G Friedlander, SB, O Sarig, Y Ziv & N Barkai Nature Genetics (2002)

5 10 15 20 25 30 35 40 45 50
10
20
30
40
50
60
70
80
90
100
How to find related items?
items
customers
re-
commended
items
your
choice
customers
with
similar
choice

5 10 15 20 25 30 35 40 45 50
10
20
30
40
50
60
70
80
90
100
How to find related genes?
genes
conditions
similarly
expressed
genes
your
guess
relevant
conditions
J Ihmels, G Friedlander, SB, O Sarig, Y Ziv & N Barkai Nature Genetics (2002)

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{ C
C
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c
c
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
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:
{ G
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Signature Algorithm: Score definitions

initial guesses
(genes)
thresholding:
condition scores
How to find related genes? Scores and thresholds!

gene
scores
condition scores
thresholding:
How to find related genes? Scores and thresholds!

Iterative Signature Algorithm
INPUT OUTPUT
OUTPUT = INPUT
“Transcription Module”
SB, J Ihmels & N Barkai Physical Review E (2003)

Identification of transcription modules
using many random “seeds”
random
“seeds”
Transcription
modules
Independent
identification:
Modules may
overlap!

New Tools: Module Visualization
http://serverdgm.unil.ch/bergmann/Fibroblasts/visualiser.html

Gene enrichment analysis
The hypergeometric distribution f(M,A,K,T) gives the probability
that K out of A genes with a particular annotation match with a
module having M genes if there are T genes in total.
http://en.wikipedia.org/wiki/Hypergeometric_distribution

Decomposing expression data into
annotated transcriptional modules
identified >100
transcriptional
modules in
yeast:
high functional
consistency!
many functional
links “waiting” to
be verified
experimentally
J Ihmels, SB & N Barkai Bioinformatics 2005

Higher-order structure
correlated
anti-
correlated
C

Slides_SB3.ppt

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