(c) M Gerstein '06, gerstein.info/talks 1 CS/CBB 545 - Data Mining Predicting Networks through...

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CS/CBB 545 - Data MiningPredicting Networks through

Bayesian Integration #2 - Application

Mark Gerstein, Yale Universitygersteinlab.org/courses/545

(class 2007,03.01 14:30-15:45)

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Predicting Networks via Bayesian Integration:

Real Thingbut with a few features

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Papers on Predicting Protein Interactions A Enright et al. (1999) "Protein interaction maps for complete genomes based on gene fusion events." Nature.

402(6757):86-90. E Marcotte et al. (1999) "A Combined Algorithm for Genome-Wide Prediction of Protein Function." Nature 402,

83-86 (1999). E Marcotte et al. (1999) "Detecting Protein Function & Protein-Protein Interactions from Genome Sequences."

Science 285, 751-753 M Pellegrini et al. (1999) "Assigning protein functions by comparative genome analysis: protein phylogenetic

profiles." Proc.Natl. Acad. Sci. 96, 4285-4288. R Jansen et al. (2003). "A Bayesian networks approach for predicting protein-protein interactions from genomic

data." Science 302: 449-53. I Lee et al. (2004) "A Probabilistic Functional Network of Yeast Genes". Science 206: 1555-1558 H Yu et al. (2004) "Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs."

Genome Res 14: 1107-18. L Lu et al. (2005) "Assessing the limits of genomic data integration for predicting protein networks."

Genome Res 15: 945-53. A Ramani et al. (2005) "Consolidating the set of known human protein-protein interactions in preparation for large-

scale mapping of the human interactome." Genome Biology 6:r40.

Xia et al. (2006) "Integrated prediction of the helical membrane protein interactome in yeast." J Mol Biol. 357:339-49

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Bayesian Formalism

R Jansen et al. (2003). "A Bayesian networks approach for predicting protein-protein interactions from genomic data." Science 302: 449-53.

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Overview of information integrated: PIE & PIP

Noisy high-throughput protein-protein interaction data

Features that are not explicit interactions but which are suggestive

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co-essentiality & gene-expression correlations

• if A and B interact both are more likely be essential or non-essential less likely that A is essential and B is non-essen (and the converse)

• Gene Expression Correlation Discussed earlier

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Subcellular localization as a gold-standard negative: If A and B interact they have to be in

the same cellular compartment

Nucleus

Membrane

Extra-cellular[secreted]

ER

Cytoplasm

Mitochondria

Golgi

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Predicting Networks via Bayesian Integration: Relating Function to

Interactions

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GO DAG

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Proteins classified by GOwikipedia

Natural Science

Chemistry Biology

Biochemistry

Glycolysis

Also:DMOZ, Y!

Define a continuous measure of similarity on a DAG

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Simple Measures

Shortest Distance

Lowest Common Ancestor

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Resnik's Measure

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New MeasureN/NAB

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Differences between Methods

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Relationship of Functional Similarity to Protein Interactions

(Exclude Interactions in Evidence Codes)

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Relating Functions to Interactions and vice versa

• Functions associated with nodes, Interactions with edges

• Interaction Prediction Two nodes with consistent labeling predicts interactions.

• Turn it around: function prediction Two connected nodes should be consistently labeled. Use this to

predict function from interactions. Constraint satisfaction problem.

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Practical Issues in Assessing Results

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Combination of All Features

Genetic Interactions

GO

MIPS

mRNA co-expr.

Individual Features and their

Integration for Yeast

Membrane Protein

Interaction Prediction

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Integration of Features in PIP Gives Much Higher Likelihood Ratios than Any Individual

Feature

Likelihood ratio L better

than

de novo prediction

(PIP)

Merged Expression Correlations

(CC + Rosetta) Essentiality

Similar MIPS

function

600 9.9k

60 85k 56k6 420k 330k 380k0 19M 16M 7.6M 6.2M

Total Number of Protein Pairs Meeting Criteria

Jansen et al. Science 302:449

"WeightedVote"

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t-SNAREs

v-SNAREs

H+-transportingATPase (vacuolar)

lipid biosynthesis

cytochrome coxidase

cytochrome bc1complex

oligosaccharyltransferase

transport

COPII

carbohydrate transport

transport

proteintargeting

amino acidglycosylation

Map of Known and Predicted Membrane Protein Interactome in Yeast

New

Known

Xia et al. (2006) "Integrated prediction of the helical membrane protein interactome in yeast." J Mol Biol. 357:339-49

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How do we assess topology prediction?

• Validate against a gold-standard of known interactions• Choice of gold-standard is of paramount importance

A negative set is equally important and often overlooked. Reliably measured negative sets are not available, sets based on exclusion based on genomic annotations (i.e. GO cellular component) are often used.

• Measure false positive (FP), true positive (TP), false negative (FN) and true negative (TN) rate

• Often plot results as a ROC-curve (sensitivity vs. specificity) Other metrics useful (e.g. PPV) Balance of positives and negatives

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Comparison of Predictions against a Positive and Negative Gold Standard

Threshold "predictions" at different levels and compare to + and - gold standards

ROC plot(cross validated)

"Error Rate"

"Cov

erag

e"

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Effect on Predictions of Large Number of Negatives

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Importance of Balanced Positive and Negative Examples

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Predicting Networks via Bayesian Integration: Adding in Even More

Features

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Adding in More Features

Absolute mRNA Expression Level Absolute Protein Abundance Marginal Essentiality Synthetic Lethality Co-regulation (via ChIP-chip)

Threading ScoresCo-evolution Scores

Rosetta Stone Gene Clusters Phylogenetic ProfileGene Neighborhood

Interologs

More features for de novo

prediction (PIP)

Correlation of functional genomics data for pair of proteins

Matching sequence/structure feature for a pair of proteins

Similar conservation pattern or chrromosomal distribution for a

pair of proteins

Other feature

[Eisenberg, Bork, Boone, Skolnick, Vidal]

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Evidence Integration

+ Graph Clustering

I Lee et al. (2004) "A Probabilistic Functional Network of Yeast Genes". Science 206: 1555-1558

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Integrated Network Showing Context Inferred

Linkages


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ROC-like Plot Showing Power of Integration


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Feature List

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Phylogenetic Profiles

M Pellegrini et al. (1999) "Assigning protein functions by comparative genome analysis: protein phylogenetic profiles." Proc.Natl. Acad. Sci. 96, 4285-4288.

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Gene Fusion Method A Enright et al. (1999) "Protein interaction maps for complete genomes based on gene fusion events." Nature. 402(6757):86-90.

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Co-citation feature

• P(chance L or more abstracts co-cite two proteins | n,m,N)

[from Ramani et al., 2005]

A Ramani et al. (2005) "Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome." Genome Biology 6:r40.

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Intuition on Hyper-

geometric

• Consider picking m articles citing protein #1 from N total articles, n of which cite #2

• (numb. ways of picking k citing #1 from those citing #2) * (numb. ways of picking m-k citing #2 from overall population N that do not cite #1) / (numb. of ways of picking m articles citing #1 from overall population)

[from http://mathworld.wolfram.com/HypergeometricDistribution.html]

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Abs. Prot. Abund. & mRNA abund

• If A and B interact, abundance (A) - abundance (B) is less than just two random pairs (might be described proportionally)

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Interologs & Regulogs

H Yu et al. (2004) "Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs." Genome Res 14: 1107-18.

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LRcutoff ~ 1000

More similar Pairs of Sequences

Hig

her

Wei

ghte

d V

ote,

Mor

e lik

ely

to in

tera

ct

75

[Yu et al, Genome Res 14: 1107-18]

High likelihood ratios from interolog

mapping (for very similar

sequences)

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Synthetic Lethality

A

B

a

B

XA

bViable Lethal

a

bWild-type Viable

X

• Synthetic Lethality Identifies Functional Relationships

• Large-Scale Synthetic Lethality Analysis Should Generate a Global Map of Functional Relationships between Genes and Pathways

• Gene Conservation = Genetic Network Conservation

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A

B

C

EssentialProduct

X

Y

Z

A

B

C

EssentialProduct

X

Y

Z

A

B

C

EssentialProduct

X

Y

Z

A

B

C

EssentialProduct

X

Y

Z

X

Dead

X

DeadDead

X

X X

X

GeneticInteractionNetwork

Similar Patterns of Genetic Interactions Identify Pathways or Complexes

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Prevalence of interactions for co-regulated proteins

Occ

urr

en

ce o

f m

oti

f re

lati

ve t

o r

andom

expect

ati

on (

log)

TF-target Regulation

Target interaction

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Predicting protein interactions by completing defective cliques

• High-throughput experiments are prone to missing interactions

P Q

K

• If proteins P and Q interact with a clique K of proteins which all interact with each other, then P and Q are more likely to interact with each other

• P, Q, and K form a defective clique

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Predicting protein interactions by completing defective cliques (cont’d)

• A defective clique can be viewed as the union of two overlapping complete cliques

P Q

K• To find all defective

cliques, we look for partial overlap of pairs of maximal cliques

• In test cases shows good prediction likelihood ratio

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Examples

Yu et al, 2006, Bioinformatics

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Predicting Networks via Bayesian Integration: Practical Analysis of Feature Correlation

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Does adding in more features help?

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Integration of Three Best Additional Features with Four Original Features

0.75

0.8

0.85

0.9

0.95

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

FPR = FP/N

TP

R =

TP

/P

4 Orig. Features

Total 7 Features"Co

vera

ge"

"Error Rate"

L Lu et al. (2005) "Assessing the limits of genomic data integration for predicting protein networks." Genome Res 15: 945-53.

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Feature Correlation


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What is Mutual Information?

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Entropy H(a)

Information (or uncertainly) about event a is log2 1/f(p,a)

Entropy of an ensemble is probability weighted information content

H(a) = - a=1 to 20 f(a) log2 1/f(a),

where f(a) = frequency of character a

Say string only has one character (AAAAA): H(a) = 1 log2 1 + 0 log2 0 + 0 log2 0 + … = 0 + 0 + 0 + … = 0

Say string is random with all 20 chars equiprobable (ACD..ACD..ACD..):Hrand(a) = -.05 log2 .05 - .05 log2 .05 - … =.22 + .22 + … = 4.3 bits

H for a bionomial variable is 2*log2(1/.5) = 1 bit

Shannon derived a measure of information content called the self-information or "surprisal" of a message: log 1/p

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Worked Mutual Information Example

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Feature Correlation


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Could lack of improvement have to do with feature correlation? NO! See effect of using

Boosting to overcome correlation

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

FPR = FP/N

TP

R =

TP

/P

Boosted Naïve Bayes

Naïve Bayes

"Co

vera

ge"

"Error Rate"


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Control: Observe how boosting improves integration when features are definitely

correlated

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FPR = FP/N

TP

R =

TP

/P

Using Simple Naïve Bayes

Using Boosting

"Co

vera

ge"

"Error Rate"

(c) M Gerstein '06, gerstein.info/talks 1 CS/CBB 545 - Data Mining Predicting Networks through...

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