1. Canadian Bioinformatics Workshops

2. Module #: Title of Module2

3. Module 8 – Variants to Networks Part 2 – From Genes to Pathways
Jüri Reimand
Bioinformatics for Cancer Genomics
May 30- June 3, 2016
Informatics and Biocomputing
Ontario Institute for Cancer Research

4. Learning Objectives of Module
What identifiers can be used for genes?
What gene annotations are available for genes?
What is a gene-set enrichment test and how does it work?
Why do I need multiple test correction for gene-set enrichment and how does it work?
How to visualize gene-set enrichment results using Enrichment Map
General principles of network visualization in Cytoscape

5. Introduction

6. Interpreting Gene Lists
My cool new screen worked and produced 1000 hits! …Now what?
Genome-Scale Analysis (Omics)
Genomics, Proteomics
Tell me what’s interesting about these genes
Are they enriched in known pathways, complexes, functions
Analysis tools
Ranking or
clustering
Eureka! New
heart disease
gene!
Prior knowledge about cellular processes

7. Pathway and network analysis
Save time compared to traditional approach
my favorite gene
What are the advantages of pathway and network analysis?
The bioinformatic approach of interpreting a gene list saves a lot of time compared to the traditional approach that consists in reading about each gene in the list.
In addition, the traditional approach is often ending by focusing only on our few favorite genes, thus the bioinformatics approach represents a less bias way to interpret the results.

8. Activity Profiles / Somatic Mutations Activity Maps
Prior Knowledge about genes
GENE SETS
NETWORKS
PATHWAYS
Spindle
Ca++ Channels
Gene.A
Gene.B
Gene.C
Gene.G
Gene.H
Gene.I
Apoptosis
MAPK
Gene.D
Gene.E
Gene.F
Gene.L
Gene.M
Gene.N
Scoring models
Search algorithms
Informatics
Activity Maps
GENE SETS
NETWORKS
PATHWAYS
Spindle
Ca++ Channels
Gene.A
Gene.B
Gene.C
Gene.G
Gene.H
Gene.I
Apoptosis
MAPK
Gene.D
Gene.E
Gene.F
Gene.L
Gene.M
Gene.N

9. Gene Identification

10. Gene Identification Getting gene IDs right is important
Identify the right entity
Stable and traceable
Issues to keep in mind:
What is the output of the experiment?
Are the annotations used to analyze the experimental data in a compatible ID system?
Is the statistical test appropriate? (most used tests assume a random uniform distribution over genes)

11. ID Challenges Avoid errors: map IDs correctly
Beware of 1-to-many mappings
Gene name ambiguity – not a good ID
e.g. FLJ92943, LFS1, TRP53, p53
Better to use the standard gene symbol: TP53
Excel error-introduction
OCT4 is changed to October-4 (paste as text)
Problems reaching 100% coverage
E.g. due to version issues
Use multiple sources to increase coverage
Zeeberg BR et al. Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics BMC Bioinformatics Jun 23;5:80

12. ID Mapping Services g:Convert Ensembl Biomart
Input gene/protein/transcript IDs (mixed)
Type of output ID
g:Convert
Ensembl Biomart

13. Gene Annotations and Gene-sets

14. From Cell Biology to Gene-sets
Where can I get these gene-sets?
How were the gene-sets compiled?
How are they structured?
Nuclear Pore
Ribosome
Gene.AAA
Gene.ABA
Gene.ABC
Gene.RP1
Gene.RP2
Gene.RP3
Gene.RP4
Cell Cycle
P53 signaling
Gene.CC1
Gene.CC2
Gene.CC3
Gene.CC4
Gene.CC5
Gene.CC1
Gene.CK1
Gene.PPP

15. Pathways and other gene function attributes
Available in databases
Pathways
Gene Ontology biological process, pathway databases e.g. Reactome
Other annotations
Gene Ontology molecular function, cell location
Chromosome position
Disease association
DNA properties
TF binding sites, gene structure (intron/exon), SNPs
Transcript properties
Splicing, 3’ UTR, microRNA binding sites
Protein properties
Domains, secondary and tertiary structure, PTM sites
Interactions with other genes

16. Pathways and other gene function attributes
Available in databases
Pathways
Gene Ontology biological process, pathway databases e.g. Reactome
Other annotations
Gene Ontology molecular function, cell location
Chromosome position
Disease association
DNA properties
TF binding sites, gene structure (intron/exon), SNPs
Transcript properties
Splicing, 3’ UTR, microRNA binding sites
Protein properties
Domains, secondary and tertiary structure, PTM sites
Interactions with other genes

17. What is the Gene Ontology (GO)?
Set of biological phrases (terms) applied to genes:
protein kinase
apoptosis
membrane
Dictionary: term definitions
Ontology: A formal system for describing knowledge

18. GO Structure Terms are related within a hierarchy
is-a
part-of
Describes multiple levels of detail of gene function
Terms can have more than one parent or child

19. What does GO cover? GO terms divided into three aspects:
cellular component
molecular function
biological process
glucose-6-phosphate isomerase activity
Cell division

20. Part 1/2: Terms Where do GO terms come from?
GO terms are added by editors at EBI and gene annotation database groups
Terms added by request
Experts help with major development
Jun 2012
Apr 2015
increase
Biological process
23,074
28,158
22%
Molecular function
9,392
10,835
15%
Cellular component
2,994
3,903
30%
total
37,104
42,896
16%

21. Part 2/2: Annotations
Genes are linked, or associated, with GO terms by trained curators at genome databases
Known as ‘gene associations’ or GO annotations
Multiple annotations per gene
Some GO annotations created automatically (without human review)

22. Hierarchical annotation
Genes annotated to specific term in GO automatically added to all parents of that term
AURKB

23. Annotation Sources Manual annotation Electronic annotation
Curated by scientists
High quality
Small number (time-consuming to create)
Reviewed computational analysis
Electronic annotation
Annotation derived without human validation
Computational predictions (accuracy varies)
Lower ‘quality’ than manual codes
Key point: be aware of annotation origin

24. Evidence Types http://www.geneontology.org/GO.evidence.shtml
Experimental Evidence Codes
EXP: Inferred from Experiment
IDA: Inferred from Direct Assay
IPI: Inferred from Physical Interaction
IMP: Inferred from Mutant Phenotype
IGI: Inferred from Genetic Interaction
IEP: Inferred from Expression Pattern
Author Statement Evidence Codes
TAS: Traceable Author Statement
NAS: Non-traceable Author Statement
Curator Statement Evidence Codes
IC: Inferred by Curator
ND: No biological Data available
Computational Analysis Evidence Codes
ISS: Inferred from Sequence or Structural Similarity
ISO: Inferred from Sequence Orthology
ISA: Inferred from Sequence Alignment
ISM: Inferred from Sequence Model
IGC: Inferred from Genomic Context
RCA: inferred from Reviewed Computational Analysis
IEA: Inferred from electronic annotation

25. Evidence codes in g:Profiler
> Input
gene list
> Enriched pathways
Colored
evidence codes
of gene annotations

26. GO and gene annotations are evolving rapidly
Wadi et al. in review; bioRxiv

27. Many GO tools are out of date
Wadi et al. in review; bioRxiv

28. Out-of-date tools (2010) miss up to 80% of enriched gene sets
Wadi et al. in review; bioRxiv

29. Pathways

30. What are Pathways?
Depict mechanistic details of metabolic, signaling and other biological processes
Advantages:
Curated, accurate
Cause and effect captured.
Human-interpretable visualizations
Disadvantages:
More sparse coverage of genome than functional sets
More complex models are required to score pathways
Static model of dynamic systems

31. Signaling

33. Gene-set Enrichment Tests

34. Activity Profiles / Somatic Mutations Activity Maps
Prior Knowledge about genes
GENE SETS
NETWORKS
PATHWAYS
Spindle
Ca++ Channels
Gene.A
Gene.B
Gene.C
Gene.G
Gene.H
Gene.I
Apoptosis
MAPK
Gene.D
Gene.E
Gene.F
Gene.L
Gene.M
Gene.N
Scoring models
Search algorithms
Informatics
Activity Maps
GENE SETS
NETWORKS
PATHWAYS
Spindle
Ca++ Channels
Gene.A
Gene.B
Gene.C
Gene.G
Gene.H
Gene.I
Apoptosis
MAPK
Gene.D
Gene.E
Gene.F
Gene.L
Gene.M
Gene.N

35. Typical Enrichment Test
Experiment
Experimentally “positive” genes
(e.g UP-regulated)
Enrichment Table
Set p-value
Spindle
Apoptosis
ENRICHMENT
TEST
Experimentally “detectable” genes
(aka background set)
Gene-set
Databases

36. Typical Enrichment Test
Random samples
of array genes
Typical Enrichment Test
The output of an enrichment test is a P-value
The P-value assesses the probability that,
by random sampling
the “detectable” genes,
the overlap is at least as large as observed.
Fisher’s Exact Test does not require to actually perform the random sampling, it is based on a theoretical null-hypothesis distribution (Hypergeometric Distribution)
Most used statistical model:
Fisher’s Exact Test

37. Fisher’s Exact Test b a d c 2 x 2 Contingency Table
Exp_positive=yes
Exp_positive=no
Gene-Set=yes a b
Gene-Set=no c d b a d c
Probability of one table to occur by random sampling:
Hypergeometric distribution formula:
Test p-value: sum of random sampling probabilities for tables as extreme or more extreme than the real table

38. Importance of the Background
Inappropriate modeling of the background will lead to incorrectly biased results
E.g.: kinase phosphorylation assay: only kinases can be detected
Depending on the experiment, the background may be easy or difficult to define b a d c

39. Multiple test corrections

40. How to win the P-value lottery, part 1
Random draws
Expect a random draw with observed enrichment once every 1 / P-value draws
… 7,834 draws later …
Background population:
500 black genes,
4500 red genes
Whenever you do multiple tests, need to correct the p-values
Remind audience of p-value definition
Motivating example: Do 20 tests at a p-value of 0.05, expect one false positive

41. How to win the P-value lottery, part 2 Keep the gene list the same, evaluate different annotations
Observed draw
Different annotation
RRP6
MRD1
RRP7
RRP43
RRP42
RRP6
MRD1
RRP7
RRP43
RRP42
Use different annotations. “evaluate different annotations”
Black vs red nodes
Square vs round nodes

42. Simple P-value correction: Bonferroni
If M = number of annotations tested:
Corrected P-value = M x original P-value
Corrected P-value is greater than or equal to the probability that at least one (or more) of the observed enrichments is due to random draws.
The jargon for this correction is
“controlling for the Family-Wise Error Rate (FWER)”

43. Bonferroni correction caveats
Bonferroni correction is very stringent and can “wash away” real enrichments leading to false negatives,
Often one is willing to accept a less stringent condition, the “false discovery rate” (FDR), which leads to a gentler correction when there are real enrichments.

44. False discovery rate (FDR)
FDR is the expected proportion of the observed enrichments due to random chance (e.g. 5%).
Compare to Bonferroni correction which is a bound on the probability that any one of the observed enrichments could be due to random chance.
Typically FDR corrections are calculated using the Benjamini-Hochberg procedure.
FDR threshold is often called the “q-value”

45. Benjamini-Hochberg example I
(Nominal)
P-value
Rank
Category 1 2 3 4 5 … 52 53
Transcriptional regulation
Transcription factor
Initiation of transcription
Nuclear localization
Chromatin modification …
Cytoplasmic localization
Translation
0.001
0.002
0.003
0.0031
0.005 …
0.97
0.99
Sort P-values of all tests in decreasing order

46. Benjamini-Hochberg example II
(Nominal)
P-value
Rank
Category
Adjusted P-value 1 2 3 4 5 … 52 53
Transcriptional regulation
Transcription factor
Initiation of transcription
Nuclear localization
Chromatin modification …
Cytoplasmic localization
Translation
0.001
0.002
0.003
0.0031
0.005 …
0.97
0.99
x 53/1 = 0.053
x 53/2 = 0.053
x 53/3 = 0.053
x 53/4 = 0.040
x 53/5 = 0.053 …
x 53/52 = 1.004
x 53/53 = 0.99
Adjusted P-value is “nominal” P-value
times number of tests
divided by the rank of the P-value in sorted list
Adjusted P-value = P-value X [# of tests] / Rank

47. Benjamini-Hochberg example III
FDR /
Q-value
(Nominal)
P-value
Rank
Category
Adjusted P-value 1 2 3 4 5 … 52 53
Transcriptional regulation
Transcription factor
Initiation of transcription
Nuclear localization
Chromatin modification …
Cytoplasmic localization
Translation
0.001
0.002
0.003
0.0031
0.005 …
0.97
0.99
x 53/1 = 0.053
x 53/2 = 0.053
x 53/3 = 0.053
x 53/4 = 0.040
x 53/5 = 0.053 …
x 53/52 = 1.004
x 53/53 = 0.99
0.040
0.053 …
0.99
Q-value (or FDR) corresponding to a nominal P-value
is the smallest adjusted P-value
assigned to P-values with the same or higher ranks.

48. Benjamini-Hochberg example III
P-value threshold for FDR < 0.05
FDR /
Q-value
(Nominal)
P-value
Rank
Category
Adjusted P-value 1 2 3 4 5 … 52 53
Transcriptional regulation
Transcription factor
Initiation of transcription
Nuclear localization
Chromatin modification …
Cytoplasmic localization
Translation
0.001
0.002
0.003
0.0031
0.005 …
0.97
0.99
x 53/1 = 0.053
x 53/2 = 0.053
x 53/3 = 0.053
x 53/4 = 0.040
x 53/5 = 0.053 …
x 53/52 = 1.004
x 53/53 = 0.99
0.040
0.053 …
0.99
Red: non-significant
Green: significant at FDR < 0.05
Q-value (or FDR) corresponding to a nominal P-value
is the smallest adjusted P-value
assigned to P-values with the same or higher ranks.

49. Reducing multiple test correction stringency
The correction to the P-value threshold α depends on the # of tests that you do, so, no matter what, the more tests you do, the more sensitive the test needs to be
Can control the stringency by reducing the number of tests: e.g. restrict testing to the appropriate GO annotations; or filter gene sets by size.

50. Enrichment Results Visualization: Enrichment Map

51. Gene-set Enrichment Redundancy Problem
Many redundant gene-sets
Gene Ontology has a very large number of gene-sets, often with slight differences
Different pathway databases have different yet overlapping definitions of pathways
Globally, it is useful to grasp the overlap relations between enriched gene-sets
--> we need a visualization framework
going beyond the enrichment table

52. GO.id GO.name p.value covercover.rat Deg.mdn Deg.iqr
GO: taxis E
GO: chemotaxis E
GO: adaptive immune response based on somatic recombination E
GO: adaptive immune response E
GO: leukocyte mediated immunity
GO: B cell mediated immunity
GO: myeloid cell differentiation
GO: immune effector process
GO: regulation of phagocytosis
GO: positive regulation of phagocytosis
GO: lymphocyte mediated immunity
GO: growth factor binding
GO: protein polymerization
GO: endoplasmic reticulum membrane
GO: immunoglobulin mediated immune response
GO: heart development
GO: response to bacterium
GO: regulation of endocytosis
GO: acute inflammatory response
GO: positive regulation of endocytosis
GO: myeloid leukocyte activation
GO: amino acid biosynthetic process
GO: regulation of inflammatory response
GO: activation of immune response
GO: positive regulation of immune system process
GO: positive regulation of immune response
GO: antigen processing and presentation
GO: regulation of immune system process
GO: regulation of immune response
GO: negative regulation of enzyme activity
GO: phagocytosis
GO: myeloid leukocyte differentiation
GO: humoral immune response
GO: lymphocyte activation
GO: leukocyte chemotaxis
GO: negative regulation of protein kinase activity
GO: negative regulation of transferase activity
GO: transforming growth factor beta receptor signaling pathw
GO: insulin-like growth factor binding
GO: T cell activation
GO: humoral immune response mediated by circulating immunogl
GO: cytosolic ribosome (sensu Eukaryota)

53. Summarising pathway analysis with Enrichment Map
Nodes – gene sets reflecting pathways, processes
Edges – sets share many common genes
Network clustering groups processes/pathways as themes

54. Ependymoma subtyping
A form of pediatric and adult cancer in brain and CNS
Pathology is primary means of classification and grading, limited clinical utility
New methylation biomarkers developed for classification
Reveals 9 subtypes of ependymoma
Characterised by distinct molecular alterations, gene expression, clinical properties
What are common and subtype-specific pathways?
Molecular classification of ependymal tumors across all CNS compartments, histopathological grades, and age groups - KW Pajtler et al Cancer Cell 2015

55. Pathways activated in EPN subtypes
Pajtler, et al.
Cancer Cell 2015

56. Cytoscape Network Visualization

57. Network Representations
How to visually interpret biological data using networks
Merico D, Gfeller D, Bader GD
Nature Biotechnology 2009 Oct 27

58. Cytoscape
Cytoscape is a freely available, open-source, Java-based application
Very popular in the community, provides key functionalities, extended by plugins (now called “apps” to be cool)

59. Key Ideas in Network Visualization
Layout
Automatic layout algorithms are necessary to arrange a network in a way that suggests the existence of patterns to the human eye
Node and Edge visual attributes
Can be used to map a number of information items relating to gene / proteins and their interactions / similarity

60. Layout: Before and After

61. Visual Attributes

62. We are on a Coffee Break & Networking Session