Pathway Analysis and Proteomics

Pathway Analysis and Proteomics
Fenghai Duan f.duan@ UNMC/PSM/Biostat June 21st, 2006
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Outline
• Pathway analysis
– Relevance network – Bayesian network
• Construct your genetic networks
– First, try RBIN and CBRN – Then, try BN on the sub-graph of RBIN and CRBN given enough data are available
Butte, Atul J. et al. (2000) Proc. Natl. Acad. Sci. USA 97, 12182-12186 13
Copyright ©2000 by the National Academy of Sciences
Pros and Cons of CBRN
• Pros
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Pathway Analysis
• Relevance network (RN)
– Biology-based RN: Relevant Biological Interaction Networks (RBIN) (Stragtagene’s pathway analysis tools) – Correlation-based RN: Butte, 2000
• Bayesian network
– Probabilistic graphical approach – Studying the causality instead of correlation
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RBIN
• Studying lists of genes (or proteins) by representing their known biological functional relationship with large graphs:
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Observation and Intervention
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Learning BN
• Finding the best network structure • Finding the best set of parameters for the conditional distributions, given the graphical structure
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Elements of BN
• A BN is a directed acyclic graph (DAG) that consists of
– a set of nodes (genes) – a set of edges representing the dependencies between genes
• Networks are constructed based on the conditional probabilities between genes • The goals is to determine the dependent relationships between genes • With the help of interventions, inferring the causal relationships between genes
• Greedy List Expansion
– Adding each neighbor node to the list and re-calculating the list’s probability – Stopping the node adding till the list’s probability measure begins to increase 9
– Considering the number of interactions between a protein and a list (of proteins) – Considering the size of the sub-graph induced by the list – Considering the number of interactions bridging two lists – Comparing the probability distribution of above with the model of random graphs 7
MI ( A, B ) = H ( A) + H (B ) − H ( A, B )
• Pearson’s Correlation (Butte et al., PNAS, 2000)
~2 = r r r2 abs(r )
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• Constructing CBRN
An E about Pathway Analysis
• Compose the set of genes for constructing genetic networks
– Identify differentially expressed (DE) genes, but some network genes might not be DE – Filter out “noise” genes according to certain thresholds – Throw in the genes based on known biological facts
– Fast and light computation – Useful to local relation
• Cons
– No dependent relationship – No causal inference – Lots of false positives
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Bayesian Networks
• Slide 17-30 were adapted from the lecture of Florian Markowetz, “Bayesian network models for gene regulation”, 2004 (http://compdiag.molgen.mpg.de)
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• A table of parameters that describe a conditional distribution for each variable given its parent --- Conditional Probability Table
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Acknowledgement to Florian Markowetz
Techniques of CBRN
• Mutual Information (Butte & Kohane, Pac. Symp. Biocomput, 2000)
– Entropy
H ( A) = −∑ p( xi ) log 2 ( p( xi ))
i =1 n
– Removing low-entropy variables (e.g., genes) – Mutual information
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Why Pathway Analysis?
• So far, Microarray analysis provides us a snapshot of thousands of gene expression profiles in a cell, e.g., DE genes, clusters of similar-expressed genes • But, it is more important to investigate the regulatory relationship among genes, e.g., does Gene A regulate Gene B?
– i.e., a collection of standardized facts extracted from biological literature
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Procedure of RBIN
• List Connectedness:
– Extracting all interactions involving the members of the list – Calculating the probability that these interactions occur in a randomly selected collection of objects
An Example of RBIN
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Correlation-based RN (CBRN)
• Butte et al., 2000 • Using Pearson’s correlation or mutual information to construct the correlation matrix • Setting the threshold to assign zero to the small values • Absence of edges indicates that genes are not correlated • Finally, a pairwise and non-directional network was constructed 11
• Proteomics
– Types of Proteomics – Steps in Expressional Proteomics – Technologies for Proteomics – Genomics vs. Proteomics