EnrichNet: network-based gene set enrichment analysis

Presenter: Lu Liu

The problem: Functional Interpretation

• Identify and assess functional associations between an experimentally derived gene/protein set and well-known gene/protein sets

Agenda

• Related research

• The method

• The Evaluation

• The results

• The conclusion

Related Research

• Over-representation analysis (ORA)

• Gene set enrichment analysis (GSEA)

• Modular enrichment analysis (MEA)

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

Agenda

• Related research

• The method

• The Evaluation

• The results

• The conclusion

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

Input

Output

The method

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Input GeneSet

Pathway 1

Pathway N

……

.

RWR

.6 .6 .6 .6 .5

.4 .3 .2 .1

……

.Pathway 1

Pathway N

Algorithm for distance score

Relate scores to a background model

• Discretized into equal-sized bins• Quatify each pathway’s deviation from

average

Agenda

• Related research

• The method

• The Evaluation

• The results

• The conclusion

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

Agenda

• Related research

• The method

• The Evaluation

• The results

• The conclusion

The results-EnrichNet vs ORA

The results-Xd-score vs Q-value

The results-comparative validation

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).

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).

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

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