1. Pathway and network analysis Functional interpretation of gene lists
Bing Zhang
Department of Biomedical Informatics
Vanderbilt University

2. Omics studies generate lists of interesting genes
log2(ratio)
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-log10(p value)
Microarray
RNA-Seq
Differential expression
Proteomics
Lists of genes with potential biological interest
Clustering

3. Organizing genes based on pathways

4. Advantages of pathway analysis
Better interpretation
From interesting genes to interesting biological themes
Improved robustness
Robust against noise in the data
Improved sensitivity
Detecting minor but concordant changes in a pathway

5. Pathway databases Databases Limitation
BioCarta (
KEGG (
MetaCyc (
Pathway commons (
Reactome (
STKE (
Signaling Gateway (
Wikipathways (
Limitation
Limited coverage
Inconsistency among different databases
Relationship between pathways is not defined

6. Gene Ontology
Structured, precisely defined, controlled vocabulary for describing the roles of genes and gene products
Three organizing principles: molecular function, biological process, and cellular component
Dopamine receptor D2, the product of human gene DRD2
molecular function: dopamine receptor activity
biological process: synaptic transmission
cellular component: plasma membrane
Terms in GO are linked by several types of relationships
Is_a (e.g. plasma membrane is_a membrane)
Part_of (e.g. membrane is part_of cell)
Has part
Regulates
Occurs in

7. Gene Ontology

8. Annotating genes using GO terms
Two types of GO annotations
Electronic annotation
Manual annotation
All annotations must:
be attributed to a source
indicate what evidence was found to support the GO term-gene/protein association
Types of evidence codes
Experimental codes - IDA, IMP, IGI, IPI, IEP
Computational codes - ISS, IEA, RCA, IGC
Author statement - TAS, NAS
Other codes - IC, ND
IDA: inferred from direct assay
IMP: inferred from mutant phenotype
IGI: inferred from genetic interaction
IPI: inferred from physical interaction
IEP: inferred from expression pattern
ISS: inferred from sequence or structure
IEA: inferred from electronic annotation
RCA: inferred from reviewed computational analysis
IGC: inferred from genomic context
TAS: traceable author statement
NAS: non-traceable author statement
IC: inferred by curator
ND: no biological data available
ND is used when the curator has determined that there is no existing literature to support an annotation.
NOT the same as having no annotation at all No annotation means that no one has looked yet

9. Annotating genes using GO terms……
DLGAP1
discs, large (Drosophila) homolog-associated protein 1
DLGAP2
discs, large (Drosophila) homolog-associated protein 2
DNM1
dynamin 1
DOC2A
double C2-like domains, alpha
DRD1
dopamine receptor D1
DRD1IP
dopamine receptor D1 interacting protein
DRD2
dopamine receptor D2
DRD3
dopamine receptor D3
DRD4
dopamine receptor D4
DRD5
dopamine receptor D5
Parent
Cell-cell signaling
Synaptic transmission
226 human genes
Child

10. Access GO Downloads (http://www.geneontology.org) Web-based access
Ontologies
Annotations
Web-based access
AmiGO:
QuickGO:

11. Coverage of GO annotations
Homo sapiens
Mus musculus
#term
#gene
GO/BP
6502
15228
6227
15709
GO/MF
3144
16389
2961
17287
GO/CC
947
16765
882
16801

12. Over-representation analysis: concept98
Hoxa5
Hoxa11
Ltbp3
Sox4
Foxc1
Edn1
Ror2
Gnag
Smad3
Wdr5
Trp63
Sox9
Pax1
Acd
Rai1
Pitx1 ……
Sash1
Cd24a
Agt
Psrc1
Ctla2b
Angptl4
Depdc7
Sorbs1
Macrod1
Enpp2
Tmem176a ……
1842
581
Observe
compare 65
1842
581
Differentially expressed genes (581 genes)
Expect
Is the observed overlap significantly larger than the expected value?
Development (1842 genes)

13. Over-representation analysis: method
Significant genes
Non-significant genes
Total
genes in the group k
j-k j
Other genes
n-k
m-n-j+k
m-j n
m-n m
Hypergeometric test: given a total of m genes where j genes are in the functional group, if we pick n genes randomly, what is the probability of having k or more genes from the group?
Observed k n j m
Zhang et.al. Nucleic Acids Res. 33:W741, 2005

14. Over-representation analysis: limitations
Arbitrary thresholding
Ignoring the order of genes in the significant gene list

15. Gene Set Enrichment Analysis: concept
Do genes in a gene set tend to locate at the top or bottom of the ranked gene list?

16. Gene Set Enrichment Analysis: method
-1/(n-k)
+1/k
k: Number of genes in the gene set S
n: Number of all genes in the ranked gene list
Subramanian et.al. PNAS 102:15545, 2005

17. Pathway-based analysis
Organizing genes by
Pathways
Gene Ontology
Enrichment analysis methods
Over-representation analysis
Gene Set enrichment analysis
Major limitation
Existing knowledge on pathways or gene functions is far from complete

18. Biological networks Networks Nodes Edges Physical interaction networks
Protein-protein interaction network
Proteins
Physical interaction, undirected
Signaling network
Modification,
directed
Gene regulatory network
TFs/miRNAs
Target genes
Physical interaction,
Metabolic network
Metabolites
Metabolic reaction,
Functional association networks
Co-expression network
Genes/proteins
Co-expression,
undirected
Genetic network
Genes
Genetic interaction,

19. Properties of complex networks
Human protein-protein interaction network
9,198 proteins and 36,707 interactions
Scale-free
(hubs)
Hierarchical modular
Small world
(six degree separation)

20. Network visualization
Network visualization tools
Cytoscape (
Gehlenborg et al. Nature Methods, 7:S56, 2010

21. Network distance vs functional similarity
Proteins that lie closer to one another in a protein interaction network are more likely to have similar function and involve in similar biological process.
Network-based gene function prediction
Network-based disease gene prediction
Sharan et al. Mol Syst Biol, 3:88, 2007

22. Organizing genes based on network modules
Protein-protein interaction modules
Transcriptional regulatory modules
Transcription factor targets
miRNA targets
Network module-based analysis
Over-representation analysis
GSEA TF

23. WebGestalt: http://www.webgestalt.org
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Jul. 1, 2013 – Jun. 30, 2014
49,136 visits from 18,213 visitors
~200
ID types
Statistical analysis
~60K
gene sets
Zhang et.al. Nucleic Acids Res. 33:W741, 2005
Wang et al. Nucleic Acids Res. 41:W77, 2013

24. WebGestalt output: Enriched GO terms
Response to unfolded proteins
12 genes
adjp=1.32e-08

25. WebGestalt output: an enriched pathways
Input genes
TGF Beta Signaling Pathway

26. WebGestalt output: enriched network modules

27. GSEA: http://www.broadinstitute.org/gsea

28. GSEA: output

29. Summary Organizing genes by “gene sets” Enrichment analysis methods
Pathways
Gene Ontology
Network modules
Enrichment analysis methods
Over-representation analysis: WebGestalt
Gene Set enrichment analysis: GSEA
Tools
WebGestalt (Over-representation analysis)
GSEA (Gene set enrichment analysis)
Manuals for WebGestalt and GSEA in the reading folder