Network Visualization and Analysis with Cytoscape

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Introductory presentation for Network Visualization and Analysis with Cytoscape

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  • UPDATE
  • Medical professionals that work with patients? Bench biologists? Bioinformaticians? Software developers? CS and mathematicians? Physicists?
  • Cytoscape is a network visualization and analysis tool. What does that mean?Well, what are networks? They are points connected by lines; or nodes connected by edges. And what purpose do they serve? They are intuitive representations of the relationship between things (this connects to this, which connects to this). They reduce complexity by providing structure (like groupings and heirarchy). And networks and graphs like this provide efficiency over tables (reading this as a table of pair-wise interactions would be unenlightening).So, a network tool like Cytoscape gives us a way to generate and study networks. There are two main goals: Analysis and Visualization. You can analyze the inherent properites of a network (such as…). In more functional terms this might include identifying hubs or “interesting” subnetworks or following a mechanism of action. The other main goal is Visualization. You can not only visualize the network itself, but you can overlay data onto it and feed it back into the analysis. For example…
  • Cytoscape has been used to study…
  • And, specifically related to biomedical research, Cytoscape has been used to study a variety of diseases. In each of these cases, subnetworks are pulled out of the integration of networks and various biomedical datasets to identify…
  • So, the challenge here is to make sense of biological networks. Networks in biology come in all sorts of shapes and sizes, each inscrutable in it’s own way…
  • Signaling pathway: Androgen Receptor Signaling PathwayMetabolic pathway: One Carbon pathway
  • ----- Meeting Notes (8/9/13 16:35) -----Think about YOUR data. What type is it? What network representation is most effective?
  • Add some examples
  • Add counter examples
  • Need layout examples
  • Notes: it is a Cytoscape goal that both the graph model and the data are free from Biological semantics
  • Notes: it is a Cytoscape goal that both the graph model and the data are free from Biological semanticsMention water distribution network publication
  • UPDATE
  • More tutorials for 2.8 and 3.0
  • Network Visualization and Analysis with Cytoscape

    1. 1. 1 Intro to Cytoscape Network Visualization and Analysis with Cytoscape Alex Pico apico@gladstone.ucsf.edu Hey, early birds! USB:Cytoscape Tutorial/2.8 Installers/…
    2. 2. 2 The Plan • USB flash drive: – Slides .pptx – The “Book” – 6 Tutorials – Cytoscape installers (plus updated plugin)
    3. 3. 3 The Plan • Schedule – Introductions (1:30) – Overview – Cytoscape concepts and UI – Tutorial #1 – 10 min break – WikiPathways and Pathway Analysis (2:30) – More Tutorials… – Coffee break – Jing Wang - NetGestalt (3:30) – Q&A (4:20)
    4. 4. 4 Introductions • Me – Executive Director, NRNB – Systems Biology Group, Gladstone Institutes – 6 Years on Cytoscape core development team • GenMAPP-CS Workspaces, criteriaMapper, GO-Elite, BubbbleRouter, Mosaic, NOA, PathExplorer – Co-founder and developer of WikiPathways – Background: structural biology, pathway analysis • You?
    5. 5. 5 Why networks? • Networks provide an integrating context for data • Commonly understood diagrammatic representation for concepts and relationships • Provides structure that helps reduce underlying complexity of the data • More efficient than searching databases element-by-element • Network tools give us functionality for studying complex processes • Analysis of global characteristics of the data, e.g. degree, clustering coefficient, shortest paths, centrality, density • Identify key elements (hubs) and „interesting‟ subnets • Help elucidate mechanisms of interaction • Visualization of data superimposed upon the network • Help understand how a process is modulated or attenuated by a stimulus
    6. 6. 6 Applications of Network Biology • Gene Function Prediction – shows connections to sets of genes/proteins involved in same biological process • Detection of protein complexes/other modular structures – discover modularity & higher order organization (motifs, feedback loops) • Network evolution – biological process(s) conservation across species • Prediction of new interactions and functional associations – Statistically significant domain- domain correlations in protein interaction network to predict protein-protein or genetic interaction jActiveModules, UCSD PathBlast, UCSD mCode, University of Toronto DomainGraph, Max Planck Institute
    7. 7. 7 Applications of Networks in Disease • Identification of disease subnetworks – identification of disease network subnetworks that are transcriptionally active in disease. • Subnetwork-based diagnosis – source of biomarkers for disease classification, identify interconnected genes whose aggregate expression levels are predictive of disease state • Subnetwork-based gene association – map common pathway mechanisms affected by collection of genotypes (SNP, CNV) Agilent Literature Search Mondrian, MSKCC PinnacleZ, UCSD
    8. 8. 8 The Challenge • Making sense out of biological networks….
    9. 9. 9 The Challenge • Biological networks (nodes and edges) – Seldom tell us anything by themselves – Analysis involves: • Understanding the characteristics of the network – Modularity – Comparison with other networks (specifically random networks) – Visualization involves: • Placing nodes in a meaningful way (layouts) • Mapping biologically relevant data to the network – Node size – Node color – Edge weights
    10. 10. 10 The Challenge
    11. 11. 11 Biological Network Taxonomy • Pathways – Signaling – Metabolic – Regulatory – Phylogeny (could also be thought of as similarity)
    12. 12. 12 Biological Network Taxonomy • Interaction Networks – Protein-Protein interactions – Protein-Ligand interactions – Genetic interactions – Domain-Domain interactions – Others • Residue or atomic interactions • Cell/cell interactions • Population biology • Epidemiology • Social networks
    13. 13. 13 Biological Network Taxonomy • Similarity – Protein-Protein similarity – Chemical similarity – Ligand similarity (SEA) – Others • Tag clouds • Topic maps
    14. 14. 14 Visualization of Biological Networks • Depiction • Data Mapping • Layouts • Animation
    15. 15. 15 Depiction • There are various ways to depict biological networks: – Node-Link (graph) representation • This is what we most often think of – Partitioned Node-Link representation • Split graph into discrete partitions based on some feature – Matrix representation • Can be useful for very dense networks • Can also map information into cells of matrix – e.g. degree, color scale (heat map)
    16. 16. 16 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 – Passthrough (labels) – Continuous (numeric values) – Discrete (categories)
    17. 17. 17 Data mapping • Avoid cluttering your visualization with too much data – Map the data you are specifically interested in to call out meaningful differences – Mapping too much data to visual attributes may just confuse the viewer – Can always create multiple networks and map different values
    18. 18. 18 Layouts • Layouts determine the location of nodes and (sometimes) the paths of edges • Types: – Simple • Grid • Partitions – 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
    19. 19. 19 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
    20. 20. 20 Layouts • Use layouts to convey the relationships between the nodes • Layout algorithms may need to be “tuned” to fit your network – LayoutsSettings… menu • Lots of parameters to change layout algorithm behavior • Can also consider laying out portions of your network
    21. 21. 21 Animation • Animation is useful to show changes in a network: – Over a time series – Over different conditions – Between species
    22. 22. 22 Introduction to Cytoscape • Overview • Core Concepts – Networks vs. Attributes – VizMapper – Apps • Working with Data – Loading network from the Web – Importing networks from csv files or Excel – Importing attributes from csv files or Excel – The attribute browser • Cytoscape tips & tricks
    23. 23. 23 WHAT IS CYTOSCAPE? www.cytoscape.org  Visualization  Integration  Analysis Cytoscape
    24. 24. 24  Open source  Cross platform  Consortium University of Toronto Cytoscape
    25. 25. 25 Core Concepts • Networks and Annotations Annotations e.g., attributes or data Networks e.g, biological pathways
    26. 26. 26 Core Concepts • Visual Mapping with VizMapper AnnotationsNetworks VizMapper
    27. 27. 27 Core Concepts • Cytoscape Apps! http://apps.cytoscape.org
    28. 28. 28 Cytoscape • Common use cases – Visualizing: • PPI • Pathways – Integration: • Expression profiles • Other state data – Analysis: • Network properties • Data mapped onto network
    29. 29. 29 Loading Networks • Use import network from table: – Excel file – Comma or tab delimited text
    30. 30. 30 Loading Networks • Use import network from table: – Excel file – Comma or tab delimited text • Use import network from web services – Allows query and load from a variety of services: • Pathway commons • WikiPathways (if GPML plugin is loaded) • NCBI Entrez Eutilities • BioCyc
    31. 31. 31 Loading Attributes • Loading attributes – Use import attribute from table
    32. 32. 32 Loading Attributes • Loading attributes – Use import attribute from table • The Data Panel
    33. 33. 33 Tutoral #1 Introduction to Cytoscape – Part 1 http://tutorials.cytoscape.org
    34. 34. 34 Examples/Demos • clusterMaker – Clustering and cluster visualizations • Agilent LitSearch Tool – Extracting networks from abstracts • WikiPathways – Search and load pathway diagrams
    35. 35. 37 Cytoscape 2.8 vs. Cytoscape 3 • Cytoscape 2.8: – One network. All other networks are projections on that network. • Essentially a rooted tree • No way to duplicate nodes without sharing attributes • Cytoscape 3: – Allows multiple roots. • Can have multiple trees • Each group of networks that shares a single root is called a collection
    36. 36. 42 Loading Networks (3.0) • Conceptually the same as 2.8 – Use import network from table: • Excel file • Comma or tab delimited text • …but – Must specify if you want a new network collection (tree) – If not, you need to specify the join column
    37. 37. 43 Loading Tables (3.0) • Same as 2.8, except: – Use Import table from file – You need to specify the network collection
    38. 38. 44 Data Panel
    39. 39. 45 Visual Style Manager
    40. 40. 46 Cytoscape 2.8 vs 3.0 • Compare and Contrast – 3.0 is more stable – 3.0 has improved model and UI – 2.8 has more apps Depends on what you need and when you need it • Timing – Current release: 3.0.2 – 3.1 coming in October – 22 apps and counting!
    41. 41. 47 More Tutorals http://tutorials.cytoscape.org
    42. 42. 48 Tips & Tricks • “Root graph” – “There is one graph to rule them all….” – The networks in Cytoscape are all “views” on a single graph. – Changing the attribute for a node in one network will also change that attribute for a node with the same ID in all other loaded networks – There is no way to “copy” a node and keep the same ID – Make a copy of the session
    43. 43. 49 Tips & Tricks • Network views – When you open a large network, you will not get a view by default – To improve interactive performance, Cytoscape has the concept of “Levels of Detail” • Some visual attributes will only be apparent when you zoom in • The level of detail for various attributes can be changed in the preferences • To see what things will look like at full detail: – ViewShow Graphics Details
    44. 44. 50 Tips & Tricks • Sessions – Sessions save pretty much everything: • Networks • Properties • Visual styles • Screen sizes – Saving a session on a large screen may require some resizing when opened on your laptop
    45. 45. 51 Tips & Tricks • Logging – By default, Cytoscape writes it’s logs to the Error Dialog: HelpError Dialog – Can change a preference to write it to the console • EditPreferencesProperties… • Set logger.console to true • Don’t forget to save your preferences • Restart Cytoscape – (can also turn on debugging: cytoscape.debug, but I don’t recommend it)
    46. 46. 52 Tips & Tricks • Memory – Cytoscape uses lots of it – Doesn’t like to let go of it – An occasional restart when working with large networks is a good thing – Destroy views when you don’t need them – Java doesn’t give us a good way to get the memory right at start time • Cytoscape 2.7 does a much better job at “guessing” good default memory sizes than previous versions
    47. 47. 53 Tips & Tricks • .cytoscape directory – Your defaults and any plugins downloaded from the plugin manager will go here – Sometimes, if things get really messed up, deleting (or renaming) this directory can give you a “clean slate” • Plugin manager – “Outdated” doesn’t necessarily mean “won’t work” – Plugin authors don’t always update their plugins immediately after new releases – Click on “Show outdated plugins” to see the entire list of plugins.

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