cis-regulation trans-regulation 5 objective: pathway reconstruction
TRANSCRIPT
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Cis-regulation
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Trans-regulation
5
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Objective: pathway reconstruction
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Identify candidate causal genes within the eQTL confidence interval around a marker by (partial) gene
expression correlation analysis
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Target gene
Genome with potential candidate genes
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Target gene
Marker
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Target gene
Bootstrap confidence interval
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Target gene
Significant correlation with target gene
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Target gene
Significant correlation with target gene
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Correlation Partial correlation
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direct
interaction
common
regulator
indirect
interaction
co-regulation
Distinguish between direct and indirect interactions
A and B have a low partial correlation
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Target gene
Significant correlation with target gene
Method of Bing and Hoeschele
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Target gene
Keep only the strongest correlation, if significant
Method of Bing and Hoeschele
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Target gene
Compute 1st-order partial correlations
Method of Bing and Hoeschele
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Target gene
Keep only the strongest partial correlation, if significant
Method of Bing and Hoeschele
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Target gene
Compute 2nd –order partial correlations
Method of Bing and Hoeschele
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Target gene
Discard 2nd-order partial correlation if not significant
Method of Bing and Hoeschele
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Target gene
Resulting network
Method of Bing and Hoeschele
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Network reconstruction, part 1
• For each gene included in the gene list of an eQTL confidence interval compute correlation coefficient with the gene expression profile of the gene affected by the eQTL.
• Test for significant departure from zero via a t-test with Bonferroni correction (threshold p-value: 0.05/n, n: number of genes in the eQTL confidence interval)
• If significant: Identify the gene with the most significant correlation coefficient Gene 1.
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Network reconstruction, part 2
• Compute first-order partial correlation coefficients between the other genes and the gene affected by the eQTL, conditional on Gene 1.
• Test for significant departure from zero via a t-test with Bonferroni correction (threshold p-value: 0.05/(n-1), n: number of genes in the eQTL confidence interval).
• If significant: Identify the gene with the most significant partial correlation coefficient Gene 2.
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Network reconstruction, part 3
• Compute second-order partial correlation coefficients between the other genes and the gene affected by the eQTL, conditional on Genes 1 & 2.
• Test for significant departure from zero via a t-test with Bonferroni correction (threshold p-value: 0.05/(n-2), n: number of genes in the eQTL confidence interval).
• If significant: Identify the gene with the most significant partial correlation coefficient Gene 3.
• And so on …
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Shortcomings
• Iterative, heuristic piecemeal approach
• No conditioning on the whole system, but on a set of pre-selected genes
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Friedman et al. (2000), J. Comp. Biol. 7, 601-620
Marriage between
graph theory
and
probability theory
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Hyperparameter β trades off data versus prior knowledge
KEGG pathwayMicroarray data
βBayesian analysis: integration of prior knowledge
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Hyperparameter β trades off data versus prior knowledge
KEGG pathwayMicroarray data
β small
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Hyperparameter β trades off data versus prior knowledge
KEGG pathwayMicroarray data
β large
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Input:Learn:MCMC
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Protein signalling network from the literature
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Predicted network
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