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Identifying Changes in Signaling from High-Throughput Data. Michael Ochs Fox Chase Cancer Center. Group 1 Patients. Group 2 Patients. Overall Survival (years). 0. 2. 4. 6. 8. 10. The “New” Paradigm. Group 1. Group 2. Targeted Therapies. Personalized Medicine.

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identifying changes in signaling from high throughput data

Identifying Changes in Signaling from High-Throughput Data

Michael Ochs

Fox Chase Cancer Center

Fox Chase Cancer Center

the new paradigm
Group 1

Patients

Group 2

Patients

Overall Survival (years)

0

2

4

6

8

10

The “New” Paradigm

Group 1

Group 2

Targeted Therapies

Personalized Medicine

Your Chromosomes Here

Fox Chase Cancer Center

outline
Outline
  • Signaling and Gene Expression
  • Bayesian Decomposition
  • Examples of Analyses

Fox Chase Cancer Center

cellular signaling
Cellular Signaling

Extracellular Signal

Signal Transduction

Metabolic Changes

Transcription

Downward, Nature, 411, 759, 2001

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gene expression
Gene Expression

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identifying pathways
M

F

H

E

A

C

B

D

Identifying Pathways

A

B

C

D

E

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goal of analysis
Take measurements of thousands of genes, some of which are responding to stimuli of interest

3

1

2

And find the correct set of basis vectors that link to pathways

*

*

*

*

*

*

then identify the pathways

Goal of Analysis

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biological model
Block Protein-Protein Interaction

Leads to Loss of Some Transcripts, Reduction of Others Depending on Active Signaling Pathways

Biological Model

But the Gene Lists are

Incomplete as are the

Network Diagrams!

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issues to solve
Issues to Solve
  • Overlapping Signals
    • Genes are involved in multiple processes
    • Various processes are active simultaneously in any observed data
  • Identification of Process Behind Signal
    • If find a signal, what is the cause
    • Do identification without a complete model

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outline1
Outline
  • Signaling and Gene Expression
  • Bayesian Decomposition
  • Examples of Analyses

Fox Chase Cancer Center

slide11
Data
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *
  • * * * * * * * * * * * * * * * * * * * *

(Spellman et al, Mol Biol Cell, 9, 3273, 1999)

(Cho et al, Mol Cell, 2, 65, 1998)

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bd identification of signals
Distribution of

Patterns

condition M

condition 1

of simpler behaviors

Patterns of

Behavior

complex behavior

pattern 1

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

is explained

as combinations

pattern k

vs

Mock

BD: Identification of Signals

condition 1

condition M

gene 1

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

pattern k

pattern 1

gene 1

* * * *

* * * *

* * * *

* * * *

* * * *

* * * *

* * * *

* * * *

** **

* * * *

* * * *

* * * *

X

=

gene N

Data

gene N

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markov chain monte carlo
Markov Chain Monte Carlo

We cannot always solve the problem directly, we can only estimate relative probabilities of possible solutions

Markov Chain Monte Carlo is

used to explore the possible solutions

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bayesian statistics
Bayesian Statistics

p(data | model) p(model)

p(model | data) =

p(data)

condition 1

condition M

pattern 1

pattern k

gene 1

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

gene 1

* * * *

* * * *

* * * *

* * * *

* * * *

* * * *

* * * *

* * * *

** **

* * * *

* * * *

* * * *

pattern 1

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

=

X

pattern k

condition M

condition 1

gene N

gene N

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outline2
Outline
  • Signaling and Gene Expression
  • Bayesian Decomposition
  • Examples of Analyses

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acknowledgements
Acknowledgements
  • Tom Moloshok (Cell Cycle, Mouse)
  • Ghislain Bidaut (Yeast Deletion Mutants)
  • Andrew Kossenkov (TFs, YDMs)
  • Bill Speier, DJ Datta, Daniel Chung, Ryan Goldstein, Matt Lewandowski

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cell cycle
Cell Cycle

Tobin and Morel, Asking About Cells, Harcourt Brace, 1997

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slide18
Data
  • Data: Expression data of 788 yeast cell-cycle regulated genes [Cho, 1998] across 17 different time points was taken for analysis.
  • Coregulation: 11 groups (from 5 to 17 genes in each group – 67 genes in total, 18 from 67 genes belong to more than one group) were composed, based on literature review (not cell cycle literature).
  • Analysis: with and without coregulation information

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validation
Validation

Cherepinsky et al, PNAS, 100, 9668, 2003

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roc analysis
Sensitivity =

Specificity=

ROC Analysis

ROC

Receiver Operator Characteristic

Fraction of called positives that are correct

Sensitivity

Fraction of called negatives that are correct

TP true positive

TN true negative

FP false positive

FN false negative

1 - Specificity

Area under the curve is the measurement of algorithm efficacy

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hierarchical clustering
Hierarchical Clustering

ROC Curve

Cherepinsky et al, PNAS, 100, 9668, 2003

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bayesian decomposition
Bayesian Decomposition

Sensitivity

1 - Specificity

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deletion mutant data set
Deletion Mutant Data Set

(Hughes et al, Cell, 102, 109, 2000)

  • 300 Deletion Mutants in S. cerevisiae
    • Biological/Technical Replicates with Gene Specific Error Model
    • Filter Genes
      • >25% Data Missing in Ratios or Uncertainties
      • < 2 Experiments with 3 Fold Change
    • Filter Experiments
      • < 2 Genes Changing by 3 Fold

228 Experiments/764 Genes

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bd matrix decomposition
Mutant M

Mutant 1

pattern 1

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

pattern k

BD: Matrix Decomposition

Distribution of

Patterns

(what genes are in patterns)

Mutant 1

Mutant M

gene 1

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

* * * * * * * * * *

pattern k

pattern 1

gene 1

* * * *

* * * *

* * * *

* * * *

* * * *

* * * *

* * * *

* * * *

** * *

* * * *

* * * *

* * * *

X

=

Patterns of

Behavior

(does mutant contain

pattern)

gene N

Data

gene N

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analysis
Analysis
  • Bayesian Decomposition
    • Identify patterns and linked genes
    • Use genes to determine function
  • Interpretation of Functions
    • Gene Ontology
    • Transcription factor data
  • Validation

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use of ontology pattern 13
Use of Ontology: Pattern 13

13

15

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the other pattern 15
The Other Pattern: 15

13

15

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transcription factors
to Transcription Factors

to mRNA Changes

Transcription Factors

Signaling Pathways

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genes from pattern 13
Genes from Pattern 13

*Fig1

*Prm6

*Fus1

*Ste2

*Aga1

*Fus3

Pes4

*Prm1

ORF

*Bar1

* known to be involved in mating response

known to be regulated by Ste12p

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validation1
Amount of Behavior Explained by Mating Pathway for MutantsValidation

(Posas, et al, Curr Opin Microbiology, 1, 175, 1998)

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pattern 13 mutants
Pattern 13 Mutants

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pattern 15 mutants
Pattern 15 Mutants

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conclusions
Conclusions
  • Transcriptional Response Provides Signatures of Pathway Activity
  • Ontologies Can Guide Interpretation
  • Bayesian Decomposition Can Dissect Strongly Overlapping Signatures

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acknowledgements1
Tom Moloshok

Jeffrey Grant

Yue Zhang

Elizabeth Goralczyk

Liat Shimoni

Luke Somers (UPenn)

Olga Tchuvatkina

Michael Slifker

Sinoula Apostolou

Brendan Reilly

Collaborators

A. Godwin (FCCC)

A. Favorov (GosNIIGenetika)

J.-M. Claverie (CNRS)

G. Parmigiani (JHU)

O. Favorova (RMSU)

Acknowledgements

Fox Chase

Ghislain Bidaut (UPenn CBIL)

Andrew Kossenkov

Vladimir Minayev (MPEI)

Garo Toby (Dana Farber)

Yan Zhou

Aidan Petersen

Bill Speier (Johns Hopkins)

Daniel Chung (Columbia)

DJ Datta (UCSF)

Elizabeth Faulkner (UPenn)

Frank Manion

Bob Beck

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patterns as basis vectors
Fuzzy Clustering

PCA

Patterns as Basis Vectors

BD

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making proteins phenotype
MakingProteins(Phenotype)

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rosetta data
ROSETTA DATA
  • From 5 to 20 patterns were posited in the analysis.
  • Results were checked on information about Metabolic Pathways taken from Saccharomyces Genome Database - 11 groups of 4-6 genes, known to be involved in the same metabolic pathways.
  • ROC analysis was performed

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rosetta data1
ROSETTA DATA

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