Identifying Changes in Signaling from HighThroughput Data

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Identifying Changes in Signaling from
High-Throughput Data
Michael Ochs Fox Chase Cancer Center
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The New Paradigm
Group 1
Group 2
Targeted Therapies
Personalized Medicine
Your Chromosomes Here
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Outline

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

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Cellular Signaling
Extracellular Signal
Signal Transduction
Metabolic Changes
Transcription
Downward, Nature, 411, 759, 2001
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Gene Expression
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Identifying Pathways
A B C D E
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Goal of Analysis
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Biological Model
But the Gene Lists are Incomplete as are
the Network Diagrams!
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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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Outline

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

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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
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
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
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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Outline

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

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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
Tobin and Morel, Asking About Cells, Harcourt
Brace, 1997
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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
Cherepinsky et al, PNAS, 100, 9668, 2003
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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
ROC Curve
Cherepinsky et al, PNAS, 100, 9668, 2003
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Bayesian Decomposition
Sensitivity
1 - Specificity
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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
• gt25 Data Missing in Ratios or Uncertainties
• lt 2 Experiments with 3 Fold Change
• Filter Experiments
• lt 2 Genes Changing by 3 Fold
• 228 Experiments/764 Genes

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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

• 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
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15
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The Other Pattern 15
13
15
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Transcription Factors
Signaling Pathways
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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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Validation
(Posas, et al, Curr Opin Microbiology, 1, 175,
1998)
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Pattern 13 Mutants
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Pattern 15 Mutants
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Conclusions

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

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Acknowledgements
Fox Chase

• Tom Moloshok
• Jeffrey Grant
• Yue Zhang
• Elizabeth Goralczyk
• Liat Shimoni
• Luke Somers (UPenn)
• Olga Tchuvatkina
• Michael Slifker
• Sinoula Apostolou
• Brendan Reilly

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

• Collaborators
• A. Godwin (FCCC)
• A. Favorov (GosNIIGenetika)
• J.-M. Claverie (CNRS)
• G. Parmigiani (JHU)
• O. Favorova (RMSU)

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Patterns as Basis Vectors
BD
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MakingProteins(Phenotype)
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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 DATA