Network Enrichment Analysis: Method, Software, And Web-Site For

Functional Interpretation Of 'Omics' Data In Global Networks Andrey Alexeyenko

User-friendly implementation on-line

https://www.evinet.org/

E-mail: andrej.alekseenko@scilifelab.se Telefon: +46 8 52481513

http://research.scilifelab.se/andrej_alexeyenko/ http://ki.se http://bils.se

Three inputs to NEA

Functional

gene set

e.g. pathway

?

Network enrichment analysis

Experimentally

defined

(altered)

genes

0 50 100 150 200 250 300 350 400 450

No. of positives, random groups

0

200

400

600

800

1000

1200

1400

No

. o

f p

osi

tives,

rea

l gro

ups

GEA_SIGN

GEA_REST

NEA_SIGN

NEA_REST

Gene set enrichment analysis

Alexeyenko A., Lee W., … Pawitan Y. Network enrichment analysis:

extension of gene-set enrichment analysis to gene networks.

BMC Bioinformatics, 2012

1) Your gene/protein set: mutations, rare variants,

candidate disease genes, differentially

expressed genes.

2) A collection of previously known functional sets:

pathways, Gene Ontology terms etc.

3) A global network of interactions and relations

between genes and proteins (known and/or

predicted)

Comparison to state-of-the-art method

P53 signaling

Cell cycle

Apoptosis

0 2 4 6 8

0.0

0.2

0.4

0.6

0.8

1.0

Years since surgery

Re

lap

se

-fre

e s

urv

iva

l

Predictors:

GO:0001666 response to hypoxia

GO:0005154 EGFR binding

GO:0005164 TNF binding

GO:0070848 response to growth factor stimulus

Example applications

a) Distinguishing

between driver

and passenger

cancer mutations

Red: drivers

Black: passengers

b) Identifying driver copy number changes in cancer

confidence

copy number distribution in the cohort

c) Network enrichment scores as predictors of clinical features When used as biomarkers, the scores are often superior

to gene expression and mutation profiles

Network enrichment analysis: the concept

The overall goal of our tools for network enrichment analysis

(NEA) is functional exploration of experimental data in either

hypothesis-driven or hypothesis-free manner. Similarly to any

biological observation, statistical significance of network

patterns should always be estimated. This is done by

comparing the network connectivity score either between or

within gene sets to that expected by chance.

The method extends the gene set enrichment analysis

(GSEA) into the network domain. The output and

interpretation of NEA are similar to those of GSEA, but the

sensitivity of NEA is higher ~5-10-fold. In addition, NEA is

applicable to both gene sets and single genes, including

those lacking any GO and pathway annotations.

NEA can also accommodate any custom/proprietary network

Alexeyenko A. and Sonnhammer E. Global networks of functional

coupling in eukaryotes by

comprehensive data integration

Genome Research, 2009