Predicting differences in gene regulatory
systems by state space models
R. Yamaguchi1, S. Imoto1 , M. Yamauchi1 , M. Nagasaki1 ,R. Yoshida2 , T. Shimamura1 , Y. Hatanaka1 , K. Ueno1 ,T. Higuchi2 , N. Gotoh1, and S. Miyano1
1. Institute of Medical Science, University of Tokyo
2. Institute of Statistical Mathematics

Presentation at GIW2008, December 2, 2008.
Reference: Yamaguchi et al., Genome Informatics, 21: 101‐113, (2008).

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Outline• Background– Finding Differentially Regulated Genes• Method– State Space Models• Analysis– Time Course Gene Expression Data with DifferentConditions• Summary and Future Works

Yamaguchi et al., GIW2008.

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Differentially‐Expressed andDifferentially‐Regulated Genes
Identical Regulation

Differential Regulation

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Yamaguchi et al., GIW2008.

Differential Regulations by Drug Dosing
Identical Regulation

Differential Regulation

Biomarkers
Drug Targets
etc.
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Predicting Case Data by Control’s System toDiscriminate the two Situations
A Thought Experiment: If we know the Control’s System, we use it to predict the Case data.
Case is predictablefrom Control’s System

Case is unpredictable
From Control’s System

We use a statistical model for inferring gene regulatory systems.
Yamaguchi et al., GIW2008.

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State Space Model for Modeling Gene Regulatory Systems
SSM
Module Net

SSM (θ )θ = {H , F , R, x0 }Time‐SeriesDatap genes

Short‐ andHigh‐dimensional
Data

Gene Net

Prediction

N obs  p
N obs Time points
Yoshida et al., CSB, (2005)
Yamaguchi et al., IEEE SPM, (2007)
Hirose et al., Bioinformatics, (2008)
Yamaguchi et al., GIW2008.

6Strategy to Predict Differentially Regulated Genes

Candidates of Differentially Regulated Genes:
Unpredictable genes in the Case data by the Control’s SSM.
Yamaguchi et al., GIW2008.

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Significance Test for Prediction Errors
The ith gene’s profile

yi ,n CASE observationgio

ε i ,n|n −1

p i ,nooo

yi ,n|n −1 = E ( yn | Y( n −1) )predicted by SSM(CTRL)

N ( yi ,n|n −1 , σ i ,n|n −1 )Time
Integration { pi , n }, n ∈ Ν obs
Finding Significantly
Differentially Regulated
Genes
Yamaguchi et al., GIW2008.

by MetaGP
http://metagp.ism.ac.jp/

Integrated p‐value pi for the ith gene ( i =1,…,p)8

Application to Real Data
• EGFR pathway• Data– Time Course Gene Expression Microarray Data
• 19 time points during 48 hours– Human Small Airway Epithelial Cell (SAEC)– Two Conditions (Different Drugs)• EGF Dosed (Control Data)• EGF + Gefitinib Dosed (Case Data)– Gene Set• 500 genes extracted based on Coefficient of Variation• SSM Analysis– Predict Case (EGF+GFT) with SSM for Control (EGF)
to find differentially regulated genes
Yamaguchi et al., GIW2008.

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Time course of Genes (probes) on the EGFR pathway
EGF (Control)

Yamaguchi et al., GIW2008.

EGF+GFT (Case)10Finding Differentially Regulated Genes
Predicted
DataPrediction
SystemControl(EGF)

Control(EGF)

Case(EGF+GFT)

Control(EGF)

Bar Color: 500 genes
Unpredictable in Case (EGF+GFT)
by Control’s System (EGF)
AND
Well Predictable in Control (EGF)
by Control’s System (EGF)

Each gene is characterized by thetwo p‐values (in black and red).
We selected genes
with small p (<0.01) in red
and with large p (>0.5) in black

Yamaguchi et al., GIW2008.

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X : EGF obs (Control)
O : EGF+GFT obs (Case)‐‐‐ : EGF pred by SSM(EGF): EGF+GFT pred by SSM(EGF)

Selected Genes

Prediction of
EGF‐GFT by SSM(EGF) (red solid line)
deviate from the observations (circles)
Yamaguchi et al., GIW2008.

Prediction of
EGF‐GFT by SSM(EGF) (red solid line)
trace the observations (circles)
12Estimated Gene Networks with SSMs
Diff gene(gene 162)

Diff gene(gene 192)
Control(EGF)

Yamaguchi et al., GIW2008.

Predicteddisappearanceof parent nodes
by drug dosingCase(EGF+GFT)

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Summary and Future Works• Method to Find Differentially Regulated Genes– State Space Model– Predicting Case Data with Control’s System• Unpredictable Genes: Differentially Regulated Genes• Real Data Analysis– SAEC dosed with EGF and EGF‐GFT– Consistent View of Predicted disappearance of parent
nodes by drug dosing• Future Works– Finding Drug Targets– Finding Biomarkers
Yamaguchi et al., GIW2008.

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