inferring nonstationary gene networks from temporal gene expression data

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Harvard Medical School Massachusetts Institute of Technology

Inferring Nonstationary Gene Networks from Temporal Gene Expression Data

Hsun-Hsien Chang1, Jonathan J. Smith2, Marco F. Ramoni1

1Children’s Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School 2Department of Mathematics, Massachusetts Institute of Technology

IEEE Workshop on Signal Processing SystemsOctober 7, 2010

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Background

• Genetic information flows from DNA to RNA through transcription.

• Modern microarray technologies are able to assess expression of 50K genes in parallel.

• Gene expression is the measure of RNA abundance in cells, revealing the gene activities.

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Clinical Applications

• Thanks to cost down, more samples can be collected in a single study. A new clinical application:– Monitor time-series gene expression in response to drugs,

treatments, vaccines, virus infection, etc.

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Multiple patients in distinct biological

conditions.

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Time-Series Gene Expression Analysis• Since genes interact each other in cells, an intriguing

analysis is to infer gene networks:– Detailed models (e.g., differential equations).

– Abstract models (e.g., Boolean networks).

– Probabilistic graphical models (e.g., dynamic Bayesian networks).

• Do not require densely sampled data. • Model expression levels by random variables to

handle noisy expression measurements and biological variability.

• Utilize the inferred networks to make prediction.

gene on gene off

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Data Representation by Bayesian Networks• Bayesian networks are directed acyclic graphs where:

– The network model can serve as a prediction tool.

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– Example: variables X and Y at time T modulate variable Z at time T+1.

• Dynamic Bayesian networks with arcs indicating temporal dependency.

– Nodes correspond to random variables (i.e., expressions of genes, clinical variables).

– Directed arcs encode conditional probabilities of the target (child) nodes on the source (parent) nodes.

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Network Inference Engine

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data at time T+1 depends only on the preceding time T.

• For a variable at a time T+1, search which set of variables at time T has the highest likelihood of modulating its value at T+1.

• Step-wise search algorithm.

Clinical variable

Genes

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Inference of Whole Dynamic Gene Network

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• Infer a transition network between every pair of times.

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Parallelize Learning Individual Transition Nets

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Parallelize Parent Searching of Individual Variables

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Step-by-Step Prediction

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Forecasting by Initial Data

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Clinical Study: HIV Viral Load Tracking• Global AIDS epidemic is one of the greatest threats to

human health, causing 2 million deaths every year.• Viral load (i.e., virus density in blood) is:

– associated with clinical outcomes. – an indicator of which treatment physicians should provide.

• If there is a tool to predict/forecast viral load trajectory, physicians could foresee how patients progress to AIDS and could allocate the best treatments upfront.

Enroll 1 2 4 12 24

viral load

...gene expre.

• Data: Fourteen (12 Africans, 2 Americans) untreated adult patients during acute infection.

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Dynamic Gene Network of HIV Viral Load

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Accuracy of HIV Viral Load Tracking

Fitted Validation (Accuracy)

Cross Validation (Robustness)

Dynamic Gene Network 97.8% 95.8%Viral Load Auto-Regression 90.1% 89.5%

• Prediction accuracy:

• Forecasting accuracy:Fitted Validation

(Accuracy)Cross Validation

(Robustness)Dynamic Gene Network 92.9% 91.8%

Viral Load Auto-Regression 88.7% 87.0%

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30 Genes Dynamically Interact with Viral LoadAMY1A: amylase, alpha 1a; salivary OTOF: otoferlin

TNFAIP6 : tumor necrosis factor, alpha-induced protein 6

KIR2DL3: killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 3

NBPF14: neuroblastoma breakpoint family, member 14 OSBP2: oxysterol binding protein 2

IRF7: interferon regulatory factor 7 CFD: complement factor d (adipsin)

HLA-DQA1: major histocompatibility complex, class ii, dq alpha 1

HLA-DRB1: major histocompatibility complex, class ii, dr beta 1

RPS23: ribosomal protein s23 GPR56: g protein-coupled receptor 56

IFI44L: interferon-induced protein 44-like CCL23: chemokine (c-c motif) ligand 23

KLRC2: killer cell lectin-like receptor subfamily c, member 2

ITIF3: interferon-induced protein with tetratricopeptide repeats 3

SOS1: son of sevenless homolog 1 (drosophila) G1P2: interferon, alpha-inducible protein (clone ifi-15k)

LOC652775: similar to ig kappa chain v-v region l7 precursor

CCL3L1: chemokine (c-c motif) ligand 3-like 1

MBP: myelin basic protein S100P: s100 calcium binding protein p

IFITM3: interferon induced transmembrane protein 3 (1-8u)

MX1: myxovirus (influenza virus) resistance 1, interferon-inducible protein p78 (mouse)

HERC5: hect domain and rld 5 NME4: non-metastatic cells 4, protein expressed in

HLA-DQB1: major histocompatibility complex, class ii, dq beta 1

LOC653157: similar to iduronate 2-sulfatase precursor (alpha-l-iduronate sulfate sulfatase) (idursulfase)

LOC643313: similar to hypothetical protein loc284701 RSAD2: radical s-adenosyl methionine domain containing 2

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Conclusions

• A Bayesian network framework to infer dynamic gene networks from time-series gene expression microarrays:– Does not require densely sampled microarray data.– Able to handle noise and handle biological variability.– Temporal dependency is captured by first-order Markov

process.– The optimal network model is achieved by parallelized search

algorithm. • Application to HIV viral load tracking shows how our

method can be used in clinical studies:– Our network model tracks viral load trajectories with higher

accuracy than viral load auto-regressive model.– Our model provides candidate gene targets for drug/vaccine

development.

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Acknowledgements

Supported by Center for HIV/AIDS Vaccine Immunology (CHAVI) # U19 AI067854-06:

•National Institute of Allergy and Infectious Diseases (NIAID)•National Institutes of Health (NIH)•Division of AIDS (DAIDS)•U.S. Department of Health and Human Services (HHS)

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Stationary Network Inference

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• All networks between pairs of times are identical.

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Pathway: Immune Response (16/30 genes, p<10-6)AMY1A: amylase, alpha 1a; salivary OTOF: otoferlin

TNFAIP6 : tumor necrosis factor, alpha-induced protein 6

KIR2DL3: killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 3

NBPF14: neuroblastoma breakpoint family, member 14 OSBP2: oxysterol binding protein 2

IRF7: interferon regulatory factor 7 CFD: complement factor d (adipsin)

HLA-DQA1: major histocompatibility complex, class ii, dq alpha 1

HLA-DRB1: major histocompatibility complex, class ii, dr beta 1

RPS23: ribosomal protein s23 GPR56: g protein-coupled receptor 56

IFI44L: interferon-induced protein 44-like CCL23: chemokine (c-c motif) ligand 23

KLRC2: killer cell lectin-like receptor subfamily c, member 2

ITIF3: interferon-induced protein with tetratricopeptide repeats 3

SOS1: son of sevenless homolog 1 (drosophila) G1P2: interferon, alpha-inducible protein (clone ifi-15k)

LOC652775: similar to ig kappa chain v-v region l7 precursor

CCL3L1: chemokine (c-c motif) ligand 3-like 1

MBP: myelin basic protein S100P: s100 calcium binding protein p

IFITM3: interferon induced transmembrane protein 3 (1-8u)

MX1: myxovirus (influenza virus) resistance 1, interferon-inducible protein p78 (mouse)

HERC5: hect domain and rld 5 NME4: non-metastatic cells 4, protein expressed in

HLA-DQB1: major histocompatibility complex, class ii, dq beta 1

LOC653157: similar to iduronate 2-sulfatase precursor (alpha-l-iduronate sulfate sulfatase) (idursulfase)

LOC643313: similar to hypothetical protein loc284701 RSAD2: radical s-adenosyl methionine domain containing 2

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major histocompatibility complex, class ii, dr beta 1 otoferlin

tumor necrosis factor, alpha-induced protein 6 killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 3

neuroblastoma breakpoint family, member 14 oxysterol binding protein 2

interferon regulatory factor 7 complement factor d (adipsin)

major histocompatibility complex, class ii, dq alpha 1 amylase, alpha 1a; salivary

ribosomal protein s23 g protein-coupled receptor 56

killer cell lectin-like receptor subfamily c, member 2 chemokine (c-c motif) ligand 23

interferon-induced protein 44-like interferon-induced protein with tetratricopeptide repeats 3

son of sevenless homolog 1 (drosophila) interferon, alpha-inducible protein (clone ifi-15k)

similar to ig kappa chain v-v region l7 precursor chemokine (c-c motif) ligand 3-like 1

myelin basic protein s100 calcium binding protein p

interferon induced transmembrane protein 3 (1-8u) myxovirus (influenza virus) resistance 1, interferon-inducible protein p78 (mouse)

hect domain and rld 5 non-metastatic cells 4, protein expressed in

major histocompatibility complex, class ii, dq beta 1 similar to iduronate 2-sulfatase precursor (alpha-l-iduronate sulfate sulfatase) (idursulfase)

similar to hypothetical protein loc284701 radical s-adenosyl methionine domain containing 2

Pathway: Antiviral Defense (8/30 genes, p<10-3)

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Pathway: Inflammatory Response (5/30 genes, p<0.05)

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Interferon Family Dominates

3 pathways; 2 pathways; 1 pathway

inferring nonstationary gene networks from temporal gene expression data

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