Tutorial session 2Network annotation

Exploring PPI networks using Cytoscape

EMBO Practical Course Session 8

Nadezhda Doncheva and Piet Molenaar

Overview

Focus: Network annotation and visualization Loading and manipulating attributes

Identifier mapping

Mapping data onto the network

Use visuals to convey data

Concepts Vizmapper

Data Human Neuroblastoma mutated genes list

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Attributes Nodes and edges can

have attributes associated with them Gene expression data Mass spectrometry data Protein structure

information Gene Ontology terms,

etc.

Cytoscape supports multiple data types: Numbers, Text, Logical, Lists...

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Loading attributes Use pre-formatted attribute files Import attribute from table

Excel file

Comma or tab delimited text

Import attribute from web services NCBI Entrez Gene

Ensembl Biomart

Use ‘import attribute or expression matrix’ Create attributes manually in the attribute

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Loading attributes from table (Demo)

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Use Case 2.1: Neuroblastoma Childhood neuro endocrine tumor Young children Variable clinical outcome

Low stages Good prognosis

Numeric changes of chromosomal copy numbers

High stages Poor prognosis

Structural chromosomal defects (LOH1p / 11q etc)

Few gene defects identified MYCN amplification (20%) ALK activation (7%) CCND1 / PHOX2B / NF1

Use Case 2.1: Neuroblastoma Poor prognosis Subgroup (~1/3) characterized by MYCN

amplification Rest unknown

Use case: Assignment 2.1 Whole genome sequence of 86 tumor vs blood 1043 genes with mutations

1.Load the list of genes (neuroblastoma_mutated_symbols.txt) as a network

2.Use the tab separated dataset (neuroblastoma_mutated_annotations.txt) to map additional information

1. Make sure the attributes have informative names

http://cytoscape.org/manual/Cytoscape2_8Manual.html#Import Attribute Table Files

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Assignment 2.1 results1. Load the list; use

the same importer

1. No interactions yet

2. Load the annotations

1. Check text import settings

2. Check mapping settings

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Attribute management

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Select attributes for display

Specific Attribute Tabs: for Nodes, Edges, and NetworkNode or Edge ID

Different type of attributes: Strings, Numbers, …

Tips & Tricks: Root Graph and sessions ”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

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Identifier mapping Identifiers (IDs) are ideally unique, stable name or

numbers But: too many IDs and different database records for

Gene, DNA, RNA, Protein The ID Mapping challenge:

Avoid errors by mapping IDs correctly Gene names are ambiguous Excel introduces errors Problems reaching 100 % coverage

Recommendations (for proteins and genes): Map everything to Entrez Gene IDs using a spreadsheet Manually curate missing mappings to achieve 100 %

coverage Be careful of Excel auto conversions

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Identifier mapping (Demo)

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Use case: Assignment 2.21. Use the Biomart plugin to map UniProt

identifiers on the genes

http://cytoscape.org/manual/Cytoscape2_8Manual.html#Node Name Mapping

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Assignment 2.2 results Use the Ensemble 68

set Input data type is

HGNC symbols Import more than

just the UniProt IDs

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Data mapping

Mapping of data values associated with graph elements onto graph visuals

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Data mapping 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)

Visual style

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VizMapper

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Default Visual Style editor

List of Data attributes

List of Visual Styles

List of Visual attributes

Mapping definition

Data mapping (Demo)

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Tips & Tricks: 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

Create multiple networks and map different values

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Use case: Assignment 2.31. Map the size of the nodes to the number of

occurrences

2. Map color to the tumor ids

1. Hint: use a rainbow pattern

http://cytoscape.org/manual/Cytoscape2_8Manual.html#Visual Styles

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Assignment 2.3 results Use gradient

Readily shows higher number of mutations

Use rainbow Similar names, similar

colors

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Assignment 2.3 results

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Exploring expression data VistaClara plugin

Exploratory data analysis of multi-experiment microarray studies

A graphical and interactive alternative to the standard attribute browser

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VistaClara (Demo)

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Filtering & editing data Use filters

QuickFind nodes and edges

Index the network based on a node or edge attribute

Dynamic filtering for numerical attributes

Build complex filters using AND, OR, NOT relations

Define topological filters (considers properties of near-by nodes)

Create subnetworks

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Filtering & editing data (Demo)

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Use case: Assignment 2.41. What is the gene with most mutations?

2. Filter the network for genes with more than one mutation (why?) and save the new network.

3. Use the Bisogenet plugin...

1. ...to find interactions among these

2. ...to find interactions among these and their first neighbours (or explore different settings of Bisogenet according to your taste)

4. Store your session for later use

http://cytoscape.org/manual/Cytoscape2_8Manual.html#Finding and Filtering Nodes and Edges

http://bio.cigb.edu.cu/biso/BisoGenet_User_Manual.pdf

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Assignment 2.4 results1. MYCN

1. Frequently amplified; no additional information

2. More likely not to be bystander

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Assignment 2.4 results1. MYCN

1. Frequently amplified; no additional information

2. More likely not to be bystander

3. Bisogenet

1. Between: Only large genes

2. Neighbours: Promising hairball

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To be continued…

Build, visualize and analyze your own network with Cytoscape Network generation Network annotation and visualization Network analysis

Identify active subnetworks

Analyze Gene Ontology enrichment

Perform topological analysis

Find network clusters

Find network motifs

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