EnrichNet is a novel approach for assessing functional associations between target gene/protein sets and reference gene sets by utilizing network-based gene set enrichment analysis. It employs random walk with restart algorithms to account for molecular network structure and provide interactive visualizations for biological interpretation. The method demonstrates higher agreement with benchmark datasets compared to traditional enrichment analysis techniques.

1. Speaker: Enrico Glaab, Luxembourg Centre for Systems Biomedicine
EnrichNet: network-based gene set enrichment analysis
Authors: Enrico Glaab, Anaïs Baudot, Natalio Krasnogor, Reinhard Schneider, Alfonso Valencia

2. 1
Motivation
How to identify and score functional associations between a gene/protein set of
interest (target set) and a collection of known, annotated gene/protein sets
(reference sets), representing cellular pathways, processes or complexes?
Problem:
Functional annotation/pathway
databases (reference sets)
Experimentally-derived
gene/protein set (target set)

3. 2
Previous approaches
Previous gene/protein set enrichment analyses techniques:
Three types of enrichment analysis approaches (see Huang et al., Nucleic Acid Res, 2009):
• Over-representation analysis (ORA)
• Gene Set Enrichment Analysis (GSEA)
• Integrative and modular enrichment analysis (MEA)
generally applicable, but scores often not discriminative, rankings difficult to interpret biologically
quantitative measurements required, molecular network neighbourhood not taken into account
mostly use clustering of annotations or data from ontology graphs rather than molecular networks
GOAL: Maximally exploit functional information from a molecular interaction
network for association scoring and visualization

4. 3
EnrichNet: Design principles (1)
Network association measure for mapped datasets:
account for distances in a molecular network and multiplicity and density of interactions between
the datasets of interest (use random walk distances instead of shortest paths distances)
Example sub-networks:
reference node
target set node
other nodes
Case 1:
dense inter-
connections
Case 2:
sparse inter-
connections

5. 4
EnrichNet: Design principles (2)
Handling of overlapping nodes and long distance outliers:
overlapping nodes and node pairs with small distances expected to be over-represented in
functionally associated datasets: assign heigher weight to short distance node pairs
account for outlier nodes: assign lower weight to long distance node pairs
Example sub-network:
outlier
(low weight)
outlier
(low weight)
pathway node
target set node
other nodes
overlap (high weight)

6. 5
EnrichNet: Procedure
Input:
• 10 or more human gene or protein identifiers of interest (= target set)
• Selection of a reference database (gene sets from GO, KEGG, BioCarta, Reactome,
WikiPathways, PID, etc.)
Processing (details on next slides):
• Target and reference datasets are mapped onto a human genome-scale molecular network
(default: STRING confidence-weighted PPI network, optional: user-defined network)
• Random walk with restart (RWR) algorithm applied to compute node-specific association scores
between mapped target set and reference sets
• Integration of scores for each reference set and comparison against background model
Output:
• Ranking table of reference pathways with association scores (optional: 60 tissue-specific scores)
• For each reference dataset: Interactive sub-network visualization of the association with target set

7. 6
EnrichNet: Random walk with restart (RWR)
RWR relevance scoring (Tong et al., 2006):
Simulate random walks via iterative matrix
multiplications:
pt+1 = (1-r) A pt + p0
• A:= network adjacency matrix
• r:= restart probability (here: r = 0.9)
• pi
t:= probability walker is at node i at time t
Result: a vector of node relevance scores for
each reference pathway (converted to
distance scores and compared against a
background model, see next slide)
Example network:
target set target/pathway overlap
pathway 1 pathway 2

8. 7
EnrichNet: Background model
Pathway-based background model:
• Gene/protein sets for background model should have similar connectivity properties as
pathway-representing reference nodes (not the case for random matched-size node sets)
use score distribution across the entire reference database as background
(n = number of equally spaced distance bins, default: n = 10;
Tissue-specific scores: pre-filter nodes by tissue-label)
• Apply Xd-distance (Olmea et al., 1999) to compare foreground against background distances
distance-dependent weighting (account for long-distance and high degree outliers)

9. 8
EnrichNet: Comparative analysis
Comparative analysis on benchmark microarray data:
• compare EnrichNet against classical over-representation analysis using benchmark datasets from
the Broad Institute of MIT and Harvard (5 gene expression datasets and 2 reference databases)
EnrichNet provides a consistently higher agreement with benchmark gene set rankings

10. 9
EnrichNet: Results
Biological application on disease-related gene sets
EnrichNet is suited in particular for the following settings:
1) Target gene/protein set of interest has no associated high-throughput experimental data:
Examples: Mutated genes in genetic diseases (OMIM, COSMIC, CGC)
Gene sets obtained from the literature (risk factors, animal model genes)
2) Target and reference set share few members but are densely connected in the network:
Examples: Occurs often for differentially expressed genes (DEGs) in complex
phenotypes (examples for Parkinson‘s disease on next slides)
Occurs often when integrating results from different studies or omics
(e.g. comparing transcriptomics and proteomics data)

11. 10
DEGs for Parkinson‘s disease (PD) vs. KEGG PD pathway
• DEGs in PD vs.
control samples
• KEGG Parkinson‘s
disease pathway
• Overlap
OPA1 mediates
mitochondrial fusion
NR4A2 mutations have been
associated with familial PD

12. 11
DEGs for PD vs. exocytosis regulation pathway
• DEGs in PD vs.
control samples
• Regulation of exocytosis
process (Gene Ontology)
• Overlap

13. 12
Summary
• EnrichNet provides a new means to score and interpret gene/protein set
associations by exploiting functional information captured in the graph structure
of molecular networks
• New functional associations are identified and sub-network visualizations
enable a biological interpretation on the level of single molecular interactions

14. 13
Availability
Software, tutorials and examples freely available at:
www.enrichnet.org
We acknowledge support by:

15. 14
References
References
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Bioinformatics, 28(18):i451-i457, 2012
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data, Bioinformatics, 28(3):446-447, 2012
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