Introduction to Cytoscape:
Slides: https://cytoscape.org/cytoscape-tutorials/presentations/intro-cytoscape-2021-ismb.html#/
John "Scooter" Morris
ISMB/ECCB 2021 Tutorial: Reproducible omics data analysis workflows
July 22-23 2021
Goals and Motivations
- Understand the major applications of network biology
- Find relevant networks and pathways
- Import your data into Cytoscape
- Perform basic topological and other network analyses
- Perform network layouts and data visualization
- Know where to find relevant Cytoscape apps and tutorials
- Get started on using Cytoscape from R or Python
Introductions
- Executive director, Resource for Biocomputing, Visualization, and Informatics
- Roving Engineer, National Resource for Network Biology
- Cytoscape team since 2006
- Author of over two dozen Cytoscape apps
Caveats
- There are many more slides than we have time for
- Depending on your interests, we will skip entire sections
- The slides will continue to be available for your reference
Introduction to Network Biology
Why Networks?
Networks are everywhere...
especially in biology!
especially in biology!
- Molecular networks
- Cell-cell communication
- Nervous systems
- Social networks
Networks are powerful tools...
especially in biology!
especially in biology!
- Reduce complexity
- More efficient than tables
- Great for data integration
- Intuitive visualization
Why Networks - Transcriptomics
In the early days:
Why Networks - Transcriptomics
Then came microarrays:
Why Networks - Transcriptomics
And now, the network view:
Why Networks - Single Cell Transcriptomics
Cell-cell interaction networks
Computational approaches for interpreting scRNA‐seq data Rostom, et. al, 2017
Why Networks - Single Cell Transcriptomics
Source: UMAP of E-MTAB-9221 from scNetViz
Source: Silvin A. et al. Cell 2020
Why Networks - Single Cell Transcriptomics
Source: scNetViz networks of E-MTAB-9221
Why Networks - Proteomics
Ingram, et al. (2011) Proteomic Analysis of Human Skin Treated with Larval Schistosome Peptidases Reveals Distinct Invasion Strategies among Species of Blood Flukes. PLoS Negl. Trop. Dis.
Networks as Tools
Transcription factors targeting apoptosis genes that bind MKRN1 in protein-mRNA network in embryonic stem cells.
One network figure conveys results from multiple technologies. Cassar, EMBO Reports 2015, Fig.8
Networks as Tools
- Topological properties
- Hubs and subnetworks
- Classify, cluster and diffuse
- Data integration
- Data overlays
- Layouts and animation
- Exploratory analysis
- Context and interpretation
Cassar, EMBO Reports 2015, Fig.8
Applications in Research
Biological Network Taxonomy
- Metabolic
- Signaling
- Regulatory
- Disease
- Key distinctions:
- Pruned and curated
- Mechanistic and contextual details
Biological Network Taxonomy
- Protein-Protein
- Protein-Ligand
- Domain-Domain
- Others
- Residue or atomic
- Cell-cell
- Epidemiology
- Social networks
Biological Network Taxonomy
- Protein/Sequence Similarity Networks (PSNs or SSNs)
- Chemical similarity
- Ligand similarity (SEA)
- Others
- Tag clouds
- Topic maps
Biological Network Taxonomy
- Metabolic
- Signaling
- Regulatory
- Disease
- Interaction
- Physical, Genetic, Social
- Similarity
- Structural, Sequence, Topical
Sources of PPI Data
- Experimental Techniques
- High Throughput
- Low Throughput
- Computational Techniques
- Public Repositories
Experimental Techniques
- Yeast 2 hybrid
Experimental Techniques
- Yeast 2 hybrid
- Pros:
- Relatively low-tech
- Scalable
- Cons:
- High false positive and false negative rate
- Pros:
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
- Pros:
- Quantitative results in vivo
- Cons:
- Tag can obscure binding
- Protease can cleave protein
- Not good for transient interactions
- Pros:
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
- BioID - Proximity-dependent Biotinylation
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
- BioID - Proximity-dependent Biotinylation
- Pros:
- Can be used for membrane-bound complexes and insoluble proteins
- Can be used to capture a "snapshot" of dynamic processes
- Cons:
- Can capture random collisions (false positives)
- Size of BirA* (321 AA can interfere with binding (false negatives))
- NOTE: BioID2 is smaller (233 AA)
- 10 nm labeling radius. Need to consider location of fusion
- Pros:
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
- BioID - Proximity-dependent Biotinylation
- BioID2 - same procedure as BioID, but uses a smaller promiscuous biotin ligase
- split-BioID - like BioID, but BirA* is split into CBirA* and NBirA* parts, which are fused in the presence of Rapamycin, providing a conditional method
- APEX - a similar technique that uses an engineered ascorbate peroxidase, but is most often used to label as many cells as possible in an organelle rather than protein-protein interactions.
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
- BioID - Proximity-dependent Biotinylation
- Protein-fragment complementation assays
- Phage display
- Protein microarrays
- Chemical crosslinking
Jensen & Bork, Science, 2008
Experimental Techniques
- Genetic interactions
- Measures functional linkage between genes
- Orthogonal to physical interaction techniques
- Two approaches
- Synthetic Genetic Array (SGA)
- Epistatic MiniArray Profile (E-MAP)
Experimental Techniques
Synthetic Genetic Array (SGA)
Experimental Techniques
Synthetic Genetic Array (SGA)
Experimental Techniques
- Genetic Interactions: Epistatic MiniArray Profile (E-MAP)
- High throughput
- Quantitative
- Can provide corroboration to PPI data
Experimental Techniques
- Xray crystallography
- NMR
- SAXS
- ELISA Binding Assays
- Co-IP
Computational Techniques
- Text mining
- Search literature for protein-protein co-occurrences
- Orthology
- Predicts interaction based orthologous pairs in another species
- Domain pairs
- Predicts interaction based on domains interacting in other proteins
Public Repositories
- Pathway data
- WikiPathways
- Reactome
- KEGG
- Pathway Commons (collection)
- Published Pathway Figures
Public Repositories
Sources of PPI Data
- Experimental Techniques
- High Throughput
- Low Throughput
- Computational Techniques
- Public Repositories
Know what you're getting.
Finding Network Data
There is no such thing!
There are hundreds of different interaction databases
Starting with a gene list......
Networks
- STRING (stringApp)
- NDEx (built-in)
- PSICQUIC (built-in)
- IntAct (IntAct App)
STRING
stringApp
- STRING Protein query:
- Genomic Context Predictions
- High-throughput Lab Experiments
- (Conserved) Co-Expression
- Automated Textmining
- Previous Knowledge in Databases
- STRING compound query:
- Queries the STITCH database
- Combines STRING protein with known compound interactions
- STRING disease query:
- Queries the DISEASES database
- Interactions of disease-associated proteins
- STRING Pubmed query:
- Extracts protein-protein interactions from pubmed
- STRING Cross-species query:
- Loads all interactions between two species. Currently limited to host-virus and host-parasite.
STRING Settings
STRING Results
STRING Options
- Change confidence level of interactions
- Expand network
- Functional Enrichment
- Filter by tissue or compartment
- Filter by evidence type
STRING Enrichment
NDEx
- Repository for biological network knowledge
- Organisations and individual scientists can deposit
- Use CyNDEx App to import/export networks
- Featured collections include:
- Pathway Interaction Database (NCI-PID)
- Cancer Cell Maps Initiative (CCMI)
- The NDEx Butler
- NetPath
NDEx Search Results
NDEx Network
PSICQUIC
PSICQUIC is included in the Cytoscape network search tool:
PSICQUIC search results
- Select results from multiple sources
- Interaction type is included
IntAct
IntAct App
- IntAct Fuzzy Search:
- Search the terms you gave among interactors data:
- id : eg. Q5S007, EBI-5323863, CHEBI:15996
- name : eg. LRRK2, GTP
- aliases : eg. Dardarin, Park8, guanosine triphosphate
- description : eg. Leucine-rich repeat serine/threonine-protein kinase 2
- You can use it to search broad thematics like tumor, kinase, cell-cycle…
- You can also use it to query specific interactors like GTP, but you will also obtain most GTP related proteins like GTPase, because their description contains GTP.
- Search the terms you gave among interactors data:
- Exact Query
- Query the terms you gave among reduced interactors data :
- id : eg. Q5S007, EBI-5323863, CHEBI:15996
- name : eg. LRRK2, GTP
- aliases : eg. Dardarin, Park8, guanosine triphosphate
- You can use it when you have a list of known proteins or gene.
- Will only ask to choose between several interactors if there is ambiguity on which proteins you meant to select.
- Query the terms you gave among reduced interactors data :
IntAct
Summary View
IntAct
Evidence View
IntAct
Mutation View
IntAct
Filters
- Can filter network by:
- MI Score
- Interaction detection method
- Participant detection method
- Host organism
- Expansion (e.g. spoke vs. matrix)
- Type (interaction type)
- Mutations (hides unmutated edges)
Pathways
- WikiPathways (WikiPathways App)
- Reactome (ReactomeFI App)
- Pathway Commons (CyPath2 App)
- KEGG (KEGGscape App)
WikiPathways
- Handmade pathway models
- Collaborative platform open to all researchers
- Thousands of pathways for dozens of species
WikiPathways Search
WikiPathways Results
WikiPathways Results
Pathway Database Apps
Analytical Approaches
The levels of organization of complex networks:
- Node degree provides information about single nodes
- Three or more nodes represent a motif
- Larger groups of nodes are called modules or communities
- Hierarchy describes how the various structural elements are combined
Analytical Approaches
Degree is most commonly used, but there are other measures of the relative importance of a single node in a network.
Analytical Approaches
Network topology statistics such as node degree, degree distribution, centralitiy, clustering coefficient, shortest paths, and robustness of the network to the random removal of single nodes are important network characteristics.
Modularity refers to the identification of sub-networks of interconnected nodes that might represent molecules physically or functionally linked that work coordinately to achieve a specific function.
Motif analysis is the identification of small network patterns that are over-represented when compared with a randomized version of the same network. Regulatory elements are often composed of such motifs.
Network alignment and comparison tools can identify similarities between networks and have been used to study evolutionary relationships between protein networks of organisms.
Analytical Approaches
- Concepts
- Graph/Network correspondence
- Node/Vertex correspondence
- Edge directedness
- Usually a network property
- Multigraph
- Allow multiple edges between nodes
- Hypergraph
- Allow edges to connect more than 2 nodes
Analytical Approaches
- Network measures
- Shortest path length
- Shortest traversal distance between two nodes
- Can be weighted if edges have weights or hops if not
- Node degree
- Indegree: # of edges for which this node is a target
- Outdegree: # of edges for which this node is a source
- Degree (undirected): # of edges incident to this node
- Hub
- node with an exceptionally high degree
- Shortest path length
Analytical Approaches
- Network measures
- Diameter: the largest distance between two nodes
- Radius: the minimum of the maximum path between any two nodes
- Characteristic path length: average shortest path length
- Density: extent to which a network is fully connected
- Heterogeneity: tendency of the network to contain hub nodes
- Connected components: number of disconnected groups of nodes
Analytical Approaches
- Network measures
- Clustering coefficient
- Measures how close the neighbors of a node are to being a clique (fully connected)
- Clustering coefficient
Analytical Approaches
- Centrality - measures of node importance
- Degree centrality (find hubs)
- Betweenness centrality (bottleneck)
- Closeness centrality
- Degree centrality (find hubs)
Analytical Approaches
- Scale-free networks
- Degree distribution follows a power law:
P(k) ~ k-y, where y is a constant - Distinct "hubs" (essential proteins?)
- Resilient to perturbation
- Biological (and social) networks tend to be scale-free
- Degree distribution follows a power law:
Analytical Approaches
- Small-world networks
- any two arbitrary nodes are connected by a small number of intermediate edges
- the network has an average shortest path length much smaller than the number of nodes in the network (Watts, Nature, 1998).
- Interaction networks have been shown to be small-world networks (Barabási, Nature Reviews in Genetics, 2004)
Analytical Approaches
- Other techniques
- Guilt-by association
- Combine weak signals to get a stronger one
- Over-representation analysis
- Also called enrichment analysis
- Statistical technique to determine if a set of terms are over-represented (enriched) in a network (or subnetwork)
- Clustering
- Also called unsupervised classification
- Variety of techniques for group nodes based on attributes or connectivity
- Guilt-by association
Analytical Approaches
- Other techniques
- Motif finding
- Finding subnetworks of interest
Cytoscape Apps for Graph Analysis
Network Visualization
Depiction
- Various ways to depict biological networks:
- Node-Link (graph) representation
- Partitioned Node-Link representation
- Matrix representation
- Can be useful for very dense networks
- Can also map information into cells of matrix
- e.g. degree, color scale (heat map)
Data Mapping
- Mapping of data values associated with graph elements onto graph visuals
- Visual attributes
- Node fill color, border color, border width, size, shape, opacity, label
- Edge type, color, width, ending type, ending size, ending color
- Mapping types
- Continuous (numeric values)
- Discrete (categories)
- Passthrough (labels)
Data Mapping
- Avoid cluttering your visualization with too much data
- Highlight meaningful differences
- Avoid confusing the viewer
- Consider creating multiple network images
Layouts
- Layouts determine the location of nodes and (sometimes) the paths of edges
- Types:
- Simple
- Grid
- Hierarchical
- layout data as a tree or hierarchy
- Works best when there are no loops
- Circular (Radial)
- arrange nodes around a circle
- could use node attributes to govern position
- e.g. degree sorted
- Simple
- Types:
Layouts
- Types:
- Force-Directed
- simulate edges as springs
- may be weighted or unweighted
- Combining layouts
- Use a general layout (force directed) for the entire graph, but use hierarchical or radial to focus on a particular portion
- Multi-layer layouts
- Partition graph, layout each partition then layout partitions
- Many, many others
- Force-Directed
Layouts
- Manual tweaks with
Node Layout Tools - Scale
- Align
- Rotate
Layouts
- Use layouts to convey the relationships between the nodes.
- There is not one correct layout. Try different things.
- Layout algorithms may need to be “tuned” to fit your network.
Animation
- Animation is useful to show changes in a network:
- Over a time series
- Over different conditions
- Between species
Cytoscape
- Open source
- Cross platform
- Consortium
Core Concepts
Networks and Tables
Networks
e.g., PPIs or pathways
Tables
e.g., data or annotations
Visual Styles
Core Concepts
Cytoscape Apps!
apps.cytoscape.org
Loading Network Data
- Cytoscape can import network data from:
- Files (or URLs)
- Excel, TSV, CSV
- XGMML: eXtensible Graph Markup and Modelling Language
- SBML: Systems Biology Markup Language
- BioPAX
- PSI-MI
- SIF: Simple Interaction Format
- GML: Graph Markup Language
- ... and others depending on loaded Apps
- Files (or URLs)
Loading Network Data
- Cytoscape can import network data from:
- Public repositories:
- PSICQUIC
- STRING (via the stringApp)
- IntAct (via the IntActApp)
- Reactome (via the ReactomeFI app)
- WikiPathways (via the WikiPathways app)
- Pathway Commons (via the CyPath2 app)
- NDEx
- Automation:
- Command line scripts
- CyREST via R, Python, etc
- Public repositories:
Loading Table Data
- Cytoscape can load tables from:
- Files (or URLs)
- Excel, TSV, CSV
- Public repositories
- BioMart
- Automation:
- Command line scripts
- CyREST via R, Python, etc
- Files (or URLs)
Saving and Exporting
- Sessions save everything as
.cys files:
Networks, Tables, Styles, Screen sizes, etc. - Export networks in different formats:
SIF, GML, XGMML, BioPAX, JSON - Export tables as CSV files
- Publication quality graphics in several formats:
PDF, SVG, PNG, and JPEG
Tour of Cytoscape
- Launch the latest version of Cytoscape
Tour of Cytoscape
- Open CyBrowser
- Enter URL: tutorials.cytoscape.org
Tour of Cytoscape
- Click on Tour of Cytoscape
- Resize window to your preference
Tour of Cytoscape
- Loading networks
- Loading tables
- Selection and filtering
- Changing visual attributes
- Exporting images
- Saving sessions
Hands-on Exercises
- Bulk RNA-Seq workflow: RNA-Seq Data Network Analysis
- Biological scenario: Basic Data Visualization
Hands-on Exercises
What have we learned?
Q&A
Thank You!
Thank You!
surveymonkey.com/r/cytoscape
Introduction to Cytoscape:
Slides: https://cytoscape.org/cytoscape-tutorials/presentations/intro-cytoscape-2021-ismb.html#/
John "Scooter" Morris
ISMB/ECCB 2021 Tutorial: Reproducible omics data analysis workflows
July 22-23 2021
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