Download a local-score approa

Improving gene signatures by the identification
of differentially expressed modules in molecular
networks : a local-score approach.
Marine Jeanmougin
JOBIM 2012, Rennes – July 4th, 2012
1
Outline
1
Introduction
Microarray experiments
Identification of molecular signatures
Motivations
2
DIsease Associated Module Selection (DiAMS)
Global approach
Local-score statistic for module ranking
Evaluation process
3
Results and application
Quantitative results
Application to Estrogen Receptor status in breast cancer
2
Microarray experiments
Objectives of microarray experiments
differential analysis
Signature of genes
Expression level of thousands of transcripts
Biological purpose
I Signature: genes involved in a phenotype of interest
I Medical applications: diagnosis, prognosis, treatment efficacy
3
Identification of molecular signatures
Differential analysis
Model
(c)
Xig :expression level of the ith sample for gene g under condition c such as:
(c)
(c)
E(Xig ) = µg
Under the assumption of homoscedasticity between conditions:
V(Xig ) = (σg )2
(c)
Hypothesis testing strategy
For two conditions, the null hypothesis to test comes down to
(
(1)
(2)
H0,g : µg = µg
(1)
(2)
H1,g : µg 6= µg
B Classical approach: t-statistic
Issues for gene-specific variance estimation
4
Identification of molecular signatures
Differential analysis
Model
(c)
Xig :expression level of the ith sample for gene g under condition c such as:
(c)
(c)
E(Xig ) = µg
Under the assumption of homoscedasticity between conditions:
V(Xig ) = (σg )2
(c)
Hypothesis testing strategy
For two conditions, the null hypothesis to test comes down to
(
(1)
(2)
H0,g : µg = µg
(1)
(2)
H1,g : µg 6= µg
B Classical approach: t-statistic
Issues for gene-specific variance estimation
4
Identification of molecular signatures
Differential analysis
Model
(c)
Xig :expression level of the ith sample for gene g under condition c such as:
(c)
(c)
E(Xig ) = µg
Under the assumption of homoscedasticity between conditions:
V(Xig ) = (σg )2
(c)
Hypothesis testing strategy
For two conditions, the null hypothesis to test comes down to
(
(1)
(2)
H0,g : µg = µg
(1)
(2)
H1,g : µg 6= µg
B Classical approach: t-statistic
Issues for gene-specific variance estimation
4
Identification of molecular signatures
Differential analysis
Limma: a shrinkage approach (Smyth, 2004)
Jeanmougin et al. 2010, PLoS ONE
Empirical Bayes variance estimate
Sglimma =
d0 S02 + dg Sg2
,
d0 + dg
I S02 : prior variance from the scale-inverse-chi-square distribution
fixed with an empirical Bayes approach
I Sg2 : usual unbiased estimator of the variance (σg )2
I d0 , dg : residual degrees of freedom for S02 and for the linear model for
gene g
Test statistic:
(1)
tglimma =
(2)
x̄·g − x̄·g
q
Sglimma n1 +
1
1
n2
.
5
Motivations
Limitations of classical approaches
I Low reproducibility
Ein-Dor et al. 2005, Outcome signature genes in breast cancer: is there a
unique set? Bioinformatics
I Difficulty to achieve a clear biological interpretation
Improving gene signatures
I Genes causing the same phenotype are likely to interact together
Gandhi, T.K. et al. 2006, Nature Genetics
I Identification of genes that are functionally related (i.e. modules)
Functional relationship
network
Expression data
6
Outline
1
Introduction
Microarray experiments
Identification of molecular signatures
Motivations
2
DIsease Associated Module Selection (DiAMS)
Global approach
Local-score statistic for module ranking
Evaluation process
3
Results and application
Quantitative results
Application to Estrogen Receptor status in breast cancer
7
Global approach
Goal
Select functional modules presenting unexpected
accumulation of high-scoring genes
Input parameters
I PPI network (strong manifestation of functional relations)
I Gene scores from limma statistic
DiAMS: a 3-step process
1
Preprocessing
2
Local-score approach for module ranking
3
Selection of significant modules
8
Global approach
Step 1 - Preprocessing
High-dimensional network
I Impossibility of exploring the huge space of possible gene subnetworks
Hierarchical clustering
I Captures much information about network topology
I Enables to go easily through the structure
I Screen the entire network without constraints on module sizes
”Walktrap” approach
• Random walks strategy
• Distance (similarity measure of vertices)
• Ward’s criterion
Pons and Latapy 2006 JGAA
9
Global approach
Step 2 - Local-score approach for module ranking
N1 g1 g2 g3 g4 g5 g6 Iterative module ranking
1
Score each module Nk (by summing gene scores)
2
Identify the highest scoring module (local-score statistic)
3
Remove it
4
Repeat setps 1) to 3) until all disjoint modules have been enumerated
10
Global approach
Step 2 - Local-score approach for module ranking
N2 N1 g1 g2 g3 g4 g5 g6 Iterative module ranking
1
Score each module Nk (by summing gene scores)
2
Identify the highest scoring module (local-score statistic)
3
Remove it
4
Repeat setps 1) to 3) until all disjoint modules have been enumerated
10
Global approach
Step 2 - Local-score approach for module ranking
N3 N2 N1 g1 g2 g3 g4 g5 g6 Iterative module ranking
1
Score each module Nk (by summing gene scores)
2
Identify the highest scoring module (local-score statistic)
3
Remove it
4
Repeat setps 1) to 3) until all disjoint modules have been enumerated
10
Global approach
Step 2 - Local-score approach for module ranking
N3 N4 N2 N1 g1 g2 g3 g4 g5 g6 Iterative module ranking
1
Score each module Nk (by summing gene scores)
2
Identify the highest scoring module (local-score statistic)
3
Remove it
4
Repeat setps 1) to 3) until all disjoint modules have been enumerated
10
Global approach
Step 2 - Local-score approach for module ranking
N5 N3 N4 N2 N1 g1 g2 g3 g4 g5 g6 Iterative module ranking
1
Score each module Nk (by summing gene scores)
2
Identify the highest scoring module (local-score statistic)
3
Remove it
4
Repeat setps 1) to 3) until all disjoint modules have been enumerated
10
Global approach
Step 2 - Local-score approach for module ranking
N5 N3 N4 N2 N1 g1 g2 g3 g4 g5 g6 Iterative module ranking
1
Score each module Nk (by summing gene scores)
2
Identify the highest scoring module (local-score statistic)
3
Remove it
4
Repeat setps 1) to 3) until all disjoint modules have been enumerated
10
Global approach
Step 2 - Local-score approach for module ranking
N5 N3 N2 N4 N1 g1 g2 g3 g4 g5 g6 Iterative module ranking
1
Score each module Nk (by summing gene scores)
2
Identify the highest scoring module (local-score statistic)
3
Remove it
4
Repeat setps 1) to 3) until all disjoint modules have been enumerated
10
Global approach
Step 2 - Local-score approach for module ranking
N5 N3 N2 N4 N1 g1 g2 g3 g4 g5 g6 Iterative module ranking
1
Score each module Nk (by summing gene scores)
2
Identify the highest scoring module (local-score statistic)
3
Remove it
4
Repeat setps 1) to 3) until all disjoint modules have been enumerated
10
Global approach
Step 3 - Selection of significant modules
Goal
Assess the global significance of each module
Monte-Carlo approach
1 – Permutation of sample labels
2 – Distribution under H0
3 – p-value computation
Selection of modules at 5% FDR level.
11
Outline
1
Introduction
Microarray experiments
Identification of molecular signatures
Motivations
2
DIsease Associated Module Selection (DiAMS)
Global approach
Local-score statistic for module ranking
Evaluation process
3
Results and application
Quantitative results
Application to Estrogen Receptor status in breast cancer
12
Module scoring
Distribution of scores in function of p-values
10
Individual gene scoring
6
8
The gene score is given by:
4
2
scores
νg = − log(pg ) − δ,
0
I pg : gene p-value from limma,
-2
I δ, a constant such as E (νg ) ≤ 0.
0.0
0.2
0.4
0.6
0.8
1.0
pvalues
Local-score statistic
Definition: value of the highest-scoring module.
Given H, a hierarchical community structure, the local-score statistic is defined
as:


X
L = max 
νg  ,
H⊆H
such as H is a subtree of H.
g∈H
13
Module scoring
Distribution of scores in function of p-values
10
Individual gene scoring
6
8
The gene score is given by:
4
2
scores
νg = − log(pg ) − δ,
0
I pg : gene p-value from limma,
δ
-2
I δ, a constant such as E (νg ) ≤ 0.
0.0
0.2
0.4
0.6
0.8
1.0
pvalues
Local-score statistic
Definition: value of the highest-scoring module.
Given H, a hierarchical community structure, the local-score statistic is defined
as:


X
L = max 
νg  ,
H⊆H
such as H is a subtree of H.
g∈H
13
Outline
1
Introduction
Microarray experiments
Identification of molecular signatures
Motivations
2
DIsease Associated Module Selection (DiAMS)
Global approach
Local-score statistic for module ranking
Evaluation process
3
Results and application
Quantitative results
Application to Estrogen Receptor status in breast cancer
14
Evaluation process
Power and false-postive rate study
Tree structure
15
Evaluation process
Power and false-postive rate study
1. Simulation of significant nodes
Tree structure
15
Evaluation process
Power and false-postive rate study
1. Simulation of significant nodes
Tree structure
2. Simulation of the gene expression matrix
H0
Gene expression
matrix
H1
15
Evaluation process
Power and false-postive rate study
1. Simulation of significant nodes
Tree structure
2. Simulation of the gene expression matrix
H0
Gene expression
matrix
H1
3. Power and False-Positive (FP) rate evaluation
Signature
15
Evaluation process
Reproducibility
Tree structure
H0
H1
Gene expression
matrix
Signature
16
Evaluation process
Reproducibility
Tree structure
H0
H0
Sub-sampling
H1
Gene expression
matrix
H1
Subsampled
expression matrix
Signature
16
Evaluation process
Reproducibility
Tree structure
H0
H0
Sub-sampling
H1
H1
Subsampled
expression matrix
Gene expression
matrix
Reproducibility ?
Signature
Signature
16
Outline
1
Introduction
Microarray experiments
Identification of molecular signatures
Motivations
2
DIsease Associated Module Selection (DiAMS)
Global approach
Local-score statistic for module ranking
Evaluation process
3
Results and application
Quantitative results
Application to Estrogen Receptor status in breast cancer
17
Quantitative results
False−Positive rate study
0.07
0.06
False−Positive rate
0.05
0.04
Selection method
DiAMS
Limma
0.03
0.02
0.01
0
5
10
20
30
40
50
Sample size (n1 = n2)
False-positive rate study - Estimated false-positive rate over the 1,000 simulations.
Plain black line: the 5% level. The dashed black lines: 95% confidence intervals.
18
Quantitative results
Power study
1.0
●
●
●
●
●
●
0.8
0.6
Selection method
Power
●
●
●
●
DiAMS
Limma
0.4
●
0.2
●
●
0.0
●
●
0.5
●
1.0
1.5
2.0
2.5
3.0
Difference of means (∆)
Power study - The mean of power values over the 1,000 simulations are
calculated at a 0.05 FDR level.
19
Quantitative results
Reproducibility study
100%
80%
Reproducibility
60%
Selection method
DiAMS
Limma
40%
20%
0%
5
10
20
30
40
50
Sample size (n1 = n2)
20
Application
Breast cancer in a few words
I An heterogeneous disease (5 subtypes)
I Presence (ER+)/absence (ER-) of Estrogen Receptors: an essential
parameter of tumor characterization.
Data
Affymetrix U133-Plus2.0 arrays:
I 537 patients (446 ER+ vs. 91 ER− )
I 54,675 probes
Topological data
PPI network from HPRD and String:
I 13,611 proteins
I ∼ 600, 000 interactions
21
Application
Results
I 27 221 initial modules
I 14 significant modules (FDR 1%)
I 159 genes
Interpretation
Module
Size
Molecular / cellular function
1
38
2
1
(GATA3)
3
35
Breast cancer regulation by Stathmin1*
(*oncoprotein which takes part in the preventive progression of ER +
tumors)
4
1
(AGR3)
Involved in ER-responsive breast tumors (Fletcher et al. 2002)
5
7
Amino-acid metabolism
Strong association with ER status (Voduc et al. 2008)
PI3K/AKT signaling (cell death and cellular growth)
Aryl Hydrocarbon Receptor signaling (*AHR represses ER)
22
Discussion
Summary
I DiAMS: local-score approach for the selection of disease associated
modules of genes
I Proved quantitative results on:
• power gains,
• reproducibility improvements,
in comparison to the classical approach.
I Limitation: coverage and quality of PPI databases
Perspectives
I Investigate the predictive performance of DiAMS
I Assess the reproducibility on real datasets.
23
Acknowledgements
Statistic & Genome Laboratory
Christophe Ambroise
Michèle, Carène, Claudine, Catherine, Camille, Etienne,
Pierre, Gilles, Cecile, Maurice, Marie-Luce, Anne-Sophie,
Cyril, Justin, Van-Hanh, Yolande, Sarah, Marius, Bernard et
Julien.
Pharnext
Mickaël Guedj
Serguei Nabirotchkin
Ilya Chumakov
Jan, Caroline, Fabrice, Mickaël, Matthieu, Jonas, Sory.
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