This document discusses pathway and network analysis. It defines systems biology and biological networks. Some benefits of studying pathways and networks are that it improves statistical power, allows identification of potential causal mechanisms, and facilitates integration of multiple data types. Types of analysis include gene set enrichment and de novo network construction. Visualization is important for representing relationships between molecules and finding subnetworks. Software like Cytoscape can be used to import networks, map gene expression data to node colors/borders, filter networks, and export publication-quality images. A tutorial demonstrates combining expression and network data in Cytoscape to tell biological stories.

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Pathway and Network Analysis

2. Pathway and Network Analysis
Presented by:
Manar-Aleslam Mattar

3. • 3
Lecture outline
• System biology
• Biological Networks
• Benefits of studying pathway & network analysis
• Comparison between pathway and network
• Types of Pathway and Network Analysis
• Pathway and network analysis workflow overview
• Network Visualization
• Network Visualization Steps
• Cytoscape
• Tutorial

4. System Biology
• Is the study of an organism, viewed as an integrated and
interacting network of genes, proteins and biochemical
reactions which give rise to life.

5. Biological Networks
• Networks represent relationships between different
biological molecules.
**Examples of different types of relationships in a biology:
1- protein-protein interaction network
2- Genetic interactions revealed by combinations of mutations.

6. Benefits of studying pathway & network analysis

7. Benefits of studying pathway & network analysis
1- Improve the statistical power.
2- Results concepts are easier to interpret.
3- Potential causal mechanisms can be identified.
4- More reproducible.
5- Facilitate the integration of multiple data types.

8. Comparison between pathway and network
NetworkPathway

9. Comparison between pathway and network

10. Types of Pathway and Network Analysis

11. Fixed gene set enrichment analysis

12. Fixed gene set enrichment analysis
• Pathways, biological processes, and networks are treated as gene sets
• Identifies genes in pathways that are present in a gene list more
frequently than expected by chance.
**Analysis workflow steps:
• (1) gene list is defined by filtering experimental data for genes with
significant gene-level statistics.
• (2) Enrichment analysis is performed to determine pathways over-
represented in the gene list.

13. De novo Network Construction and Clustering

14. De novo Network Construction and Clustering
• De novo construction of cancer gene networks by analyzing
networks of molecular or functional interactions.
• Begin with a list of mutated or altered genes, and one or more
databases of gene or protein interactions, such as STRING, or
GeneMANIA.
• The altered genes and a subset of their neighbours are then extracted
from the databases and reconstructed as an interaction network.

15. Types of Pathway and Network Analysis

16. Pathway and network analysis workflow overview

18. Network Visualization

19. Network Visualization
• Nodes in a network:
• Gene
• Protein
• Micro RNA
• Microorganism
• Disease
• Edges in a network (interaction):
• Physical protein interaction
• Genetic interaction
• Signaling interaction
• Metabolic interaction
• DNA binding

20. Benefits from network visualization

21. Benefits from network visualization
1- Represent relationship between biological molecules
2- Better than tables in excel in discovering the relationships
3- Finding sub network
4- Visualize multiple data types together

22. Network Visualization Steps
1- Choose the software that you will use
2- Import network data
3- Network layout generation
4- Import attribute/ annotation data table
5- Mapping the attributes or annotations to your network
6- Analyze the network and export it as a table or as image

23. 1- Software to create and analyze networks

24. 2- Import the network data
Data can be loaded from different sources in different formats
depends on your biological question :
1) Public databases ( String, Wikipathways,…etc)
2) Local or remote file
3) Software apps like (Biopax, KEGG,…etc) in Cytoscape.
***In the tutorial we will load a network from local file called
( galFiltered.sif ) which contains a protein–protein interactions

26. 3- Network layout generation
• Layouts determine the location of nods and sometimes the path
of edges
• Use the layout to convey the relationships between the nodes
Types of layouts:
• Simple (Gird, Partition)
• Hierarchical : layout data as a tree or hierarchy
• Circular (radial ) : arrange nodes around a circle , use the node
attributes to govern position
• Force directed

27. • No organization
• Nodes are overlapping
• Can’t analyse the network
• well organized
• No overlapping
• Easier to interpret

28. 4- Import attribute/ annotation data table
• This table will contain the attributes that will be used to set
the visual properties of the nodes ( color, shape, border)
• In the tutorial the data is the gene expression of Gal1, Gal4,
and Gal80 are all yeast transcription factors.
• All expression data from experiments involve some
perturbation of these transcription factor genes.

29. 4- Import attribute/ annotation data table
• The first column is the node names, and must match the
names of the nodes in your network exactly.
• Two columns per experiment (one for expression measurement
and a second for corresponding significance value)

31. 5- Mapping the annotations to network
Visual features:
• Node properties ( fill color, border color, shape, width, height, opacity
and label )
• Edge properties (type, color, thickness)
Example:
• The expression values mapped to node color:
 Nodes with low expression will be colored blue,
 Nodes with high expression will be colored red.
• Significance for expression values mapped to Node Border Width:
 Nodes with significant changes will appear with a thicker border

33. Mapping the attributes or annotations to your network
Data attributes can be mapped in three main ways:
1) Passthrough: directly passing the data value to the visual
attribute, e.g., labels.
2) Continuous: Mapping a continuous range of numerical values
to a range of visual attributes, e.g., expression values to a color
gradient.
3) Discrete : Mapping discrete data values to specified visual
attributes, e.g., five different categories of Cancer types to five
different colors.

34. Cytoscape

35. Cytoscape

36. How to find your App?
• Through App store: http://apps.cytoscape.org/apps/all

37. Examples of some Apps and its uses

38. How to create a network using Cytoscape

39. Tutorial
This tutorial presents a scenario of how expression and network
data Can be combined to tell a biological story and includes the
concepts :
1) Visualizing the network using expression data
2) Filtering the network based on the expression data
3) Assessing expression data in the context of a biological network

40. Tutorial
VIDEO

41. Summary
• Created a visual style using expression value as node color and
with border width mapped to significance
• Selected high expressing genes and their neighbors and created a
new network
• Export this network as a publication-quality image.

44. Results
• Both nodes GAL4 and GAL11 are pale blue with thin
borders which means they are low expressed and with no
significant changes in expression.
• GAL4 interacts with GAL80, which shows a significant
level of repression: it is medium blue with a thicker border.
• most nodes interacting with GAL4 show significant levels
of induction: they are rendered as red rectangles.

45. Further Reading

46. THANK YOU!
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