EnrichNet : network-based gene set enrichment analysis
description
Transcript of EnrichNet : network-based gene set enrichment analysis
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EnrichNet: network-based gene set enrichment analysis
Presenter: Lu Liu
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The problem: Functional Interpretation
• Identify and assess functional associations between an experimentally derived gene/protein set and well-known gene/protein sets
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Agenda
• Related research
• The method
• The Evaluation
• The results
• The conclusion
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Related Research
• Over-representation analysis (ORA)
• Gene set enrichment analysis (GSEA)
• Modular enrichment analysis (MEA)
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Limitations
• ORA tend to have low discriminative power
• Functional information from interaction network disregarded
• Missing annotation gene/protein ignored
• Tissue-specific gene/protein set association often infeasible
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Agenda
• Related research
• The method
• The Evaluation
• The results
• The conclusion
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General workflow
• Input gene/protein list(>=10), a database of interest (KEGG etc.)
• Processinggene mapping, score the distance with RWR, compare scores with background model
• Output A pathways/processes ranking table, visualization
of sub-networks
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Input
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Output
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The method
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Input GeneSet
Pathway 1
Pathway N
……
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RWR
.6 .6 .6 .6 .5
.4 .3 .2 .1
……
.Pathway 1
Pathway N
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Algorithm for distance score
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Relate scores to a background model
• Discretized into equal-sized bins• Quatify each pathway’s deviation from
average
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Agenda
• Related research
• The method
• The Evaluation
• The results
• The conclusion
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Evaluation method
• Compare with ORA5 datasets and 2 reference gene sets from literature1. select 100 most DEGs2. get association scores of EnrichNet and ORA3. compute a running-sum statistic for all gene sets
• The consensus of GSEA-derived(SAM-GS, GAGE) pathway ranking as external benchmark pathway ranking
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Agenda
• Related research
• The method
• The Evaluation
• The results
• The conclusion
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The results-EnrichNet vs ORA
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The results-Xd-score vs Q-value
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The results-comparative validation
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Protein–protein interaction sub-networks (largest connected components) for target and reference set pairs with small overlap, predicted to be functionally associated by EnrichNet: (a) gastric cancer mutated genes (blue) and genes/proteins from the BioCarta pathway ‘Role
of Erk5 in Neuronal Survival’ (magenta, the shared genes are shown in green); (b) bladder cancer mutated genes (blue) and genes/proteins from Gene Ontology term ‘Tyrosine
phosphorylation of Stat3’ (GO:0042503, magenta; the only shared gene NF2 is shown in green).
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Protein–protein interaction sub-network (largest connected component) for the PD gene set (blue) and genes/proteins from GO term ‘Regulation of interleukin-6 biosynthetic process’
(magenta, GO:0045408; the only shared gene IL1B is shown in green).
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The results-tissue specificity
• EnrichSet don’t require additional gene expression measurement data
• Brain tissue: Xd-scores over-representated• Non-Brain tissue: center of Xd-score
distribution significant lower
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The conclusion
• EnrichNet sometimes has more discriminative power when target sets and pathway set has large overlaps
• EnrichNet can identifies novel function associations through direct and indirect molecular interactions when target sets and pathway set has little overlaps