Identifying changes in signaling from high throughput data
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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

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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

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

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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

Fox Chase Cancer Center


Outline1

Outline

  • Signaling and Gene Expression

  • Bayesian Decomposition

  • Examples of Analyses

Fox Chase Cancer Center


Identifying changes in signaling from high throughput data

Data

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

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

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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

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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Identifying changes in signaling from high throughput data

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

TPtrue positive

TNtrue negative

FPfalse positive

FNfalse 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 Mutants

Validation

(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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