Post on 23-Jan-2016
description
Supervised and unsupervised methods for large scale genomic data integration
Curtis Huttenhower
03-25-10Harvard School of Public HealthDepartment of Biostatistics
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Greatest Biological Discoveries?
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Are We There Yet?
• How much biology is out
there?
• How much have we found?
• How fast are we finding it?
Human Proteins withAnnotated Biological Roles
Age-Adjusted Citation Rates forMajor Sequencing Projects
Species Diversity ofEnvironmental Samples
Fierer 2008
#DistinctRoles
Matt Hibbs
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#DistinctRoles
Matt Hibbs
Are We There Yet?
• How much biology is out
there?
• How much have we found?
• How fast are we finding it?
Human Proteins withAnnotated Biological Roles
Age-Adjusted Cost per Citation forMajor Sequencing Projects
Species Diversity ofEnvironmental Samples
Fierer 2008
Lots!
Not nearly all
Not fast enough
Our job is to create computational microscopes:
To ask and answer specific biomedical questions using
millions of experimental results
5
Outline
1. Big picture:Algorithms for mining
genome-scale datasets
2. Details:Recovering mechanistic detail
from high-throughput data
3. Applications:Microbial communities and functional metagenomics
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A framework for functional genomics
HighSimilarity
LowSimilarity
HighCorrelation
LowCorrelation
G1G2
+
G4G9
+
…
G3G6
-
G7G8
-
…
G2G5
?
0.9 0.7 … 0.1 0.2 … 0.8
+ - … - - … +
0.8 0.5 … 0.05 0.1 … 0.6
HighCorrelation
LowCorrelation
Fre
quen
cy
Coloc.Not coloc.
Fre
quen
cy
SimilarDissim.
Fre
quen
cy
P(G2-G5|Data) = 0.85
100Ms gene pairs →
← 1
Ks
data
sets
+ =
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Functional networkprediction and analysis
Global interaction network
Metabolism network Signaling network Gut community network
Currently includes data from30,000 human experimental results,
15,000 expression conditions +15,000 diverse others, analyzed for
200 biological functions and150 diseases
HEFalMp
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HEFalMp: Predicting human gene function
HEFalMp
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HEFalMp: Predicting humangenetic interactions
HEFalMp
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HEFalMp: Analyzing human genomic data
HEFalMp
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HEFalMp: Understanding human disease
HEFalMp
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Meta-analysis for unsupervisedfunctional data integration
Evangelou 2007
Huttenhower 2006Hibbs 2007
1
1log2
1'
'
''
z
eiey ,
ieeeiey ,,
i
ieiee yw ,*,̂
22,
*, ˆ
1
eie
ies
w
Simple regression:All datasets are equally accurate
Random effects:Variation within and
among datasets and interactions
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Meta-analysis for unsupervisedfunctional data integration
Following up with semi-supervised approach
Evangelou 2007
Huttenhower 2006Hibbs 2007
1
1log2
1'
'
''
z
+ =
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Functional mapping: mining integrated networks
Predicted relationships between genes
HighConfidence
LowConfidence
The strength of these relationships indicates how cohesive a process is.
Chemotaxis
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Functional mapping: mining integrated networks
Predicted relationships between genes
HighConfidence
LowConfidence
Chemotaxis
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Functional mapping: mining integrated networks
Flagellar assembly
The strength of these relationships indicates how
associated two processes are.
Predicted relationships between genes
HighConfidence
LowConfidence
Chemotaxis
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Functional Mapping:Scoring Functional Associations
How can we formalizethese relationships?
Any sets of genes G1 and G2 in a network can be compared
using four measures:
• Edges between their genes
• Edges within each set• The background edges
incident to each set• The baseline of all edges
in the network
),(),(
),(
2121
21, 21 GGwithin
baseline
GGbackground
GGbetweenFA GG
Stronger connections between the sets increase association.
Stronger within self-connections or nonspecific background connections decrease association.
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Functional Mapping:Bootstrap p-values
• Scoring functional associations is great……how do you interpret an association score?– For gene sets of arbitrary sizes?– In arbitrary graphs?– Each with its own bizarre distribution of edges?
Empirically!# Genes 1 5 10 50
1
5
10
50
Histograms of FAs for random sets
For any graph, compute FA scores for many randomly chosen gene sets of different sizes. Null distribution is
approximately normal with mean 1.
Standard deviation is asymptotic in the sizes
of both gene sets.
Maps FA scores to p-values for any gene sets and
underlying graph.
100
102
104
100
101
102
103
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
|G1|
|G2|
Null distribution σs for one graph
|)(|||
|||)(|),(ˆ
1),(ˆ
ji
jijiFA
jiFA
GCG
BGGAGG
GG
)(1)( ),(ˆ),,(ˆ, 212121xxFAP GGGGGG
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Functional Mapping:Functional Associations Between Processes
EdgesAssociations between processes
VeryStrong
ModeratelyStrong
NodesCohesiveness of processes
BelowBaseline
Baseline(genomic
background)
VeryCohesive
BordersData coverage of processes
WellCovered
SparselyCovered
Hydrogen Transport
Electron Transport
Cellular Respiration
Protein ProcessingPeptide
Metabolism
Cell Redox Homeostasis
Aldehyde Metabolism
Energy Reserve
Metabolism
Vacuolar Protein
Catabolism
Negative Regulation of Protein Metabolism
Organelle Fusion
Protein Depolymerization
Organelle Inheritance
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Functional Mapping:Functional Associations Between Processes
EdgesAssociations between processes
VeryStrong
ModeratelyStrong
NodesCohesiveness of processes
BelowBaseline
Baseline(genomic
background)
VeryCohesive
BordersData coverage of processes
WellCovered
SparselyCovered
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Functional Maps:Focused Data Summarization
ACGGTGAACGTACAGTACAGATTACTAGGACATTAGGCCGTATCCGATACCCGATA
Data integration summarizes an impossibly huge amount of experimental data into an
impossibly huge number of predictions; what next?
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Functional Maps:Focused Data Summarization
ACGGTGAACGTACAGTACAGATTACTAGGACATTAGGCCGTATCCGATACCCGATA
How can a biologist take advantage of all this data to study
his/her favorite gene/pathway/disease without
losing information?
Functional mapping• Very large collections of genomic data• Specific predicted molecular interactions• Pathway, process, or disease
associations• Underlying experimental results and
functional activities in data
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Outline
1. Big picture:Algorithms for mining
genome-scale datasets
2. Details:Recovering mechanistic detail
from high-throughput data
3. Applications:Microbial communities and functional metagenomics
• Gene expression
• Physical PPIs
• Genetic interactions
• Colocalization
• Sequence
• Protein domains
• Regulatory binding
sites
…
?
How do functional interactionsbecome pathways?
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+ =
Functional genomic data
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With Chris Park, Olga Troyanskaya
Simultaneous inference of physical, genetic, regulatory, and functional networks
Functional interactions
Regulatory interactions
Post-transcriptional regulation
Metabolic interactions
Phosphorylation Protein complexes
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Learning a compendium of interaction networks
Train one SVM per interaction type
Resolve consistency using hierarchical Bayes net
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Learning a compendium of interaction networks
AUC
0.5 1.0
Both presence/absence and directionality of
interactions are accurately inferred
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Using network compendia to predictcomplete pathways
Additional 20 novel synthetic lethality predictions tested,
14 confirmed(>100x better than random)
Confirmed
Unconfirmed
With David Hess
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Interactive aligned network viewer –coming soon!
Graphle
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Outline
1. Big picture:Algorithms for mining
genome-scale datasets
2. Details:Recovering mechanistic detail
from high-throughput data
3. Applications:Microbial communities and functional metagenomics
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Microbial Communities andFunctional Metagenomics
• Metagenomics: data analysis from environmental samples– Microflora: environment includes us!
• Pathogen collections of “single” organisms form similar communities
• Another data integration problem– Must include datasets from multiple organisms
• What questions can we answer?– What pathways/processes are present/over/under-
enriched in a newly sequences microbe/community?– What’s shared within community X?
What’s different? What’s unique?– How do human microflora interact with diabetes,
obesity, oral health, antibiotics, aging, …– Current functional methods annotate
~50% of synthetic data, <5% of environmental data
PKH1
PKH3
PKH2LPD1
CAR1
W04B5.5
pdk-1
R04B3.2
LLC1.3
T21F4.1
PDPK1
ARG1DLD
ARG2
AGA
With Jacques Izard, Wendy Garrett
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Data Integration for Microbial Communities
PKH1
PKH3
PKH2LPD1
CAR1
W04B5.5
pdk-1
R04B3.2
LLC1.3
T21F4.1
PDPK1
ARG1DLD
ARG2
AGA
~300 available expression datasets
~30 species
PKH1
PKH3
PKH2LPD1
CAR1
W04B5.5
pdk-1
R04B3.2
LLC1.3
T21F4.1
PDPK1
ARG1DLD
ARG2
AGA
Weskamp et al 2004
Flannick et al 2006
Kanehisa et al 2008
Tatusov et al 1997
• Data integration works just as well in microbes as it does in yeast and humans• We know an awful lot about some microorganisms and almost nothing about others• Sequence-based and network-based tools for function transfer both work in isolation• We can use data integration to leverage both and mine out additional biology
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Functional network prediction from diverse microbial data
486 bacterial expression
experiments
876 raw datasets
310 postprocessed
datasets
304 normalized coexpression networks
in 27 species
Integrated functional interaction networks
in 15 species
307 bacterial interaction
experiments
154796 raw interactions
114786 postprocessed
interactions
E. Coli Integration
← Precision ↑, Recall ↓
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Functional maps for cross-speciesknowledge transfer
← Precision ↑, Recall ↓
Following up with unsupervised and partially anchored network alignment
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• Sleipnir C++ library for computational functional genomics
• Data types for biological entities• Microarray data, interaction data, genes and gene sets,
functional catalogs, etc. etc.• Network communication, parallelization
• Efficient machine learning algorithms• Generative (Bayesian) and discriminative (SVM)
• And it’s fully documented!
Efficient Computation For Biological Discovery
Massive datasets and genomes require efficient algorithms and implementations.
It’s also speedy: microbial data integration
computationtakes <3hrs.
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Outline
1. Big picture:Algorithms for mining
genome-scale datasets
2. Details:Recovering mechanistic detail
from high-throughput data
3. Applications:Microbial communities and functional metagenomics
• Bayesian and unsupervised
methods for data integration• HEFalMp system for human data
analysis and integration• Functional mapping to statistically
summarize large data collections
• Simultaneous inference of an
interaction network compendium
• Accurate prediction of interaction
types and directionality• Validated pathways
and specificindividual interactions
in yeast
• Integration for microbial
communities and metagenomics
• Sleipnir software for efficient
large scale data mining
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Thanks!
NIGMShttp://function.princeton.edu/hefalmp
http://huttenhower.sph.harvard.edu/sleipnir
Olga TroyanskayaChris ParkDavid HessMatt HibbsChad MyersAna PopAaron Wong
Hilary CollerErin Haley
Jacques Izard
Wendy Garrett
Sarah FortuneTracy Rosebrock
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Validating Human Predictions
Autophagy
Luciferase(Negative control)
ATG5(Positive control) LAMP2 RAB11A
NotStarved
Starved(Autophagic)
Predicted novel autophagy proteins
5½ of 7 predictions currently confirmed
With Erin Haley, Hilary Coller
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Functional maps for cross-speciesknowledge transfer
G17
G16G15
G10
G6
G9
G8
G5
G11
G7
G12
G13
G14
G2
G1
G4
G3
O8
O4O5
O7
O9
O6
O2
O3
O1
O1: G1, G2, G3O2: G4O3: G6…
ECG1, ECG2BSG1ECG3, BSG2…
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Functional maps for functional metagenomics
GOS 4441599.3Hypersaline Lagoon, Ecuador
KEGG Pathways
Org
anis
ms
Pathog ens
Env.
Mapping genes into pathways
Mapping pathways into
organisms
+ Integrated functional interaction networks
in 27 species
Mapping organisms into phyla
=
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Functional maps for functional metagenomics
NodesProcess cohesiveness in obesity
VeryDownregulated
Baseline(no change)
VeryUpregulated
EdgesProcess association in obesity
MoreCoregulated
LessCoregulated
Baseline(no change)
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Current Work: Molecular Mechanismsin a Colorectal Cancer Cohort
With Shuji Ogino, Charlie Fuchs
~3,100gastrointestinal
subjects
~3,800tissue samples
~1,450colon cancer
samples~1,150
CpG island methylation
~1,200LINE-1
methylation
~700TMA immuno-histochemistry
~2,100cancer
mutation tests
Health Professionals Follow-Up
StudyNurse’s HealthStudy
LINE-1 Methylation• Repetitive element making up ~20% of
mammalian genomes• Very easy to assay methylation level (%)• Good proxy for whole-genome methylation
level
DASL Gene Expression• Gene expression analysis from
paraffin blocks• Thanks to Todd Golub, Yujin
Hoshida
~775gene
expression
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Molecular Subtypes of Colorectal Cancer:Stem Cell Programs and Proliferation
Chr. 19 rearrangement,membrane receptors/channels
HSC signature
Neural/ESC signature
Angiogenesis, proliferation
BRCA interactors,chrom. stability factors
Cell cycle regulation
C1 C2 C3 C4Nonnegative matrix factorizationTumors →
← G
enes
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Molecular Subtypes of Colorectal Cancer:Stem Cell Programs and Proliferation
Subramanian et al, 2005
195
146678
166945
325
799
NeuralStem Cell Signature
HematopoeiticStem Cell Signature
EmbryonicStem Cell Signature
Chr. 19q
18
8
7
BAX
CD133 + Bcl-X(L)
CD44 + CD166
Hypotheses?• Two main pathways to
proliferation:• HSC program + BAX• ESC/NSC program
• Two main pathways to deregulation:
• Angiogenesis + chrom. instability• Cell cycle disruption (MSI?)
Note that these regulatory programsdo not appear to correspond
with demographics or commonpathologic markers…
Testing now for correlation with outcome.
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Epigenetics of Colorectal Cancer:LINE-1 methylation levels
30 35 40 45 50 55 60 65 70 75 8030
40
50
60
70
80
LINE-1 Methylation in Mul-tiple Tumors from the Same
Subject
Methylation %, Tumor #1M
eth
ylat
ion
%,
Tu
mo
r #2
ρ = 0.718, p < 0.01
Ogino et al, 2008
Lower LINE-1 methylation associates with poor colon cancer prognosis.
LINE-1 methylation varies remarkably between individuals…
…but it is highly correlated within individuals.
What does it all mean??What is the biological
mechanism linking LINE-1 methylation to colon cancer?
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Epigenetics of Colorectal Cancer:LINE-1 methylation levels
30 35 40 45 50 55 60 65 70 75 8030
40
50
60
70
80
LINE-1 Methylation in Mul-tiple Tumors from the Same
Subject
Methylation %, Tumor #1M
eth
ylat
ion
%,
Tu
mo
r #2
ρ = 0.718, p < 0.01
Ogino et al, 2008
Lower LINE-1 methylation associates with poor colon cancer prognosis.
LINE-1 methylation varies remarkably between individuals…
…but it is highly correlated within individuals.
This suggests a genetic effect.
This suggests a copy number variation.
This suggests linkage to a cancer-related pathway.
Is anything different about these outliers?
What is the biological mechanism linking LINE-1
methylation to colon cancer?
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Epigenetics of Colorectal Cancer:LINE-1 methylation levels
What is the biological mechanism linking LINE-1
methylation to colon cancer?
Preliminary Data• 10 genes differentially expressed even using simple methods• 1/3 are from the same family with known GI tumor prognostic value• 1/3 are X-chromosome testis/cancer-specific antigens• 1/2 fall in same cytogenic band, which is also a known CNV hotspot• HEFalMp links to a cascade of antigens/membrane receptors/TFs
Cell adhesion p-value ≈ 0, moderate correlation in many cancer arrays• GSEA pulls out a wide range of proliferation up (E2F),
immune response down; need to regress out prognosis correlates
Check back in acouple of months!