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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

Fox Chase Cancer Center


Gene expression
Gene Expression

Fox Chase Cancer Center


Identifying pathways

M

F

H

E

A

C

B

D

Identifying Pathways

A

B

C

D

E

Fox Chase Cancer Center


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

Fox Chase Cancer Center


Biological model

Block Protein-Protein Interaction responding to stimuli of interest

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!

Fox Chase Cancer Center


Issues to solve
Issues to Solve responding to stimuli of interest

  • 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

Fox Chase Cancer Center


Outline1
Outline responding to stimuli of interest

  • Signaling and Gene Expression

  • Bayesian Decomposition

  • Examples of Analyses

Fox Chase Cancer Center


Data responding to stimuli of interest

  • * * * * * * * * * * * * * * * * * * * *

  • * * * * * * * * * * * * * * * * * * * *

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  • * * * * * * * * * * * * * * * * * * * *

  • * * * * * * * * * * * * * * * * * * * *

  • * * * * * * * * * * * * * * * * * * * *

  • * * * * * * * * * * * * * * * * * * * *

  • * * * * * * * * * * * * * * * * * * * *

(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 responding to stimuli of interest

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

Fox Chase Cancer Center


Markov chain monte carlo
Markov Chain Monte Carlo responding to stimuli of interest

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 responding to stimuli of interest

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

Fox Chase Cancer Center


Outline2
Outline responding to stimuli of interest

  • Signaling and Gene Expression

  • Bayesian Decomposition

  • Examples of Analyses

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Acknowledgements
Acknowledgements responding to stimuli of interest

  • 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 responding to stimuli of interest

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

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Data responding to stimuli of interest

  • 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 responding to stimuli of interest

Cherepinsky et al, PNAS, 100, 9668, 2003

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Roc analysis

Sensitivity = responding to stimuli of interest

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 responding to stimuli of interest

ROC Curve

Cherepinsky et al, PNAS, 100, 9668, 2003

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Bayesian decomposition
Bayesian Decomposition responding to stimuli of interest

Sensitivity

1 - Specificity

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Deletion mutant data set
Deletion Mutant Data Set responding to stimuli of interest

(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 responding to stimuli of interest

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

Fox Chase Cancer Center


Analysis
Analysis responding to stimuli of interest

  • 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 responding to stimuli of interest

13

15

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The other pattern 15
The Other Pattern: 15 responding to stimuli of interest

13

15

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Transcription factors

to Transcription Factors responding to stimuli of interest

to mRNA Changes

Transcription Factors

Signaling Pathways

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Genes from pattern 13
Genes from Pattern 13 responding to stimuli of interest

*Fig1

*Prm6

*Fus1

*Ste2

*Aga1

*Fus3

Pes4

*Prm1

ORF

*Bar1

* known to be involved in mating response

known to be regulated by Ste12p

Fox Chase Cancer Center


Validation1

Amount of Behavior Explained by Mating Pathway for Mutants responding to stimuli of interest

Validation

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

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Pattern 13 mutants
Pattern 13 Mutants responding to stimuli of interest

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Pattern 15 mutants
Pattern 15 Mutants responding to stimuli of interest

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Conclusions
Conclusions responding to stimuli of interest

  • 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 responding to stimuli of interest

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

Fox Chase Cancer Center


Patterns as basis vectors

Fuzzy Clustering responding to stimuli of interest

PCA

Patterns as Basis Vectors

BD

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Making proteins phenotype
Making responding to stimuli of interestProteins(Phenotype)

Fox Chase Cancer Center


Rosetta data
ROSETTA DATA responding to stimuli of interest

  • 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

Fox Chase Cancer Center


Rosetta data1
ROSETTA DATA responding to stimuli of interest

Fox Chase Cancer Center


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