BioQuali Cytoscape Plugin - User's Guide

(1.04.2011)

Table of Contents

INTRODUCTION ......................................................................................................................... 3

INSTALLATION ........................................................................................................................... 4

INPUT DATA ................................................................................................................................ 4

Network ..................................................................................................................................... 4

Expression Dataset .................................................................................................................... 5

USAGE ......................................................................................................................................... 5

Test graph consistency .............................................................................................................. 5

Test graph consistency with literature data ............................................................................... 7

Test graph consistency with Gene Expression ratios ................................................................ 8

Visualize core graph ................................................................................................................ 10

Apply BioQuali Visual Style................................................................................................... 11

RESULTS .................................................................................................................................... 12

Consistent graph ..................................................................................................................... 12

Inconsistent graph ................................................................................................................... 13

Consistent graph with data (predictions) ................................................................................ 14

Inconsistent graph with data ................................................................................................... 15

Multiple Inconsistencies in the Graph .................................................................................... 17

Network core ........................................................................................................................... 18

CORRECTION ............................................................................................................................ 19

Automatic Correction of inconsistencies ................................................................................ 19

Inconsistent subgraph ......................................................................................................... 19

Multiple inconsistencies ..................................................................................................... 19

Manual Correction of inconsistencies ..................................................................................... 20

SAVING YOUR RESULTS......................................................................................................... 21

INTRODUCTION

BioQuali is a Plugin for Cytoscape developed at the Symbiose project at IRISA-INRIA,

Rennes, France (www.irisa.fr/symbiose). The technical development and the integration of the

Plugin has been carried on by the GenOuest Bioinformatics Platform

(http://www.genoweb1.irisa.fr).

BioQuali analyses regulatory networks and expression datasets by checking a global

consistency between the regulatory model and the expression data. It diagnoses a regulatory

network searching for the regulations that are not consistent with the expression data, and it

outputs a set of genes which predicted expression is decided in order to explain the expression

data provided.

The consistency of a network is understood in the following sense: "Each positive or negative

variation of a node in the network must be explained by a positive or negative total influence

received from its predecessors". We have discretized this analysis by imposing that all nodes in

the network are limited to be up or down regulated, and that the edges in the network can

represent positive, negative, or unknown influences. However, for analyzing a global

consistency all the nodes in the network must be taken into account. The BioQuali Cytoscape

Plugin proposes the user to visualize the solution of this problem automatically and in few

minutes no matter the size of the network.

BioQuali plugin bases all its functionalities in a Python library BioQuali available on line at

http://www.irisa.fr/symbiose/projects/bioqualiCytoscapePlugin/data/bioquali_python.tgz. As a

Cytoscape plugin, it provides the visualization of this analysis in order to provide a friendlier

environment and to encourage users of different disciplines to analyze their regulatory

networks.

INSTALLATION

The BioQuali Cytoscape plugin works with the Cytoscape version 2.8.0. There are 3 ways to

install it:

1. Download the bioquali.jar from

http://www.irisa.fr/symbiose/projects/bioqualiCytoscapePlugin/data/2_8/bioquali.jar

and copy the jar file in the Cytoscape plugins directory. The BioQualiPlugin will be

loaded when Cytoscape will start. It will be available from the Cytoscape "Plugins" item

menu.

2. Open Cytoscape, go to the "Plugins" item menu and select "Manage Plugins", select in

the 'Available to Install’ directory BioQualiPlugin v.2, and then click "Install". After

this operation will be completed, the plugin will be available from the Cytoscape

"Plugins" item menu.

3. Launch the java web start program in the following address: http://genoweb.univ-

rennes1.fr/Serveur-

GPO/outils/interactionNetwork/BIOQUALI/BioqCyPlugin/JWS/BioqCyPlugin.jnlp.

However, this will execute the plugin with an old version of Cytoscape: 2.6.0

BioQuali plugin works under Windows and Linux. There are two versions of it: (1) adapted for

Cytoscape 2.6 and 2.6.1, (2) adapted for Cytoscape 2.8.0.

INPUT DATA The network and data to be analyzed must be mapped into (+,-,?,&) values. A network is

composed of regulations that can be positive influences (+), negative influences (-), complex

formation influences (&) or dual/unknown influences (?). The expression dataset is composed

by positive (+) or negative (-) gene/protein expression shifts. At least a network file should be

imported into Cytoscape before executing BioQuali. The formats of the files to import are

explained below.

Network

Represented by a .SIF (Simple interaction format) File

nodeA <induces> nodeB

nodeC <represses> nodeD

nodeE <unknown> nodeF

nodeF <formsComplex> F_C

nodeC <formsComplex> F_C

F_C <induces> node Z

The Edge Attributes of the network in this example will be:

ID Interaction Signs

nodeA(<induces>) nodeB induces +

nodeC (<represses>) nodeD represses -

nodeE (<unknown>) nodeF unknown ?

nodeF (<formsComplex>) F_C formsComplex &

nodeC (<formsComplex>) F_C formsComplex &

F_C (<induces>) node Z induces +

The regular expression for the nodes of the network is the following: “[-a-zA-Z0-9_|:\(\)/\+\']+\@*[-a-zA-Z0-9_|\(\)/]*”

Edit an existing network. Note that it is important to fill the Interaction Edge Attribute

for all the edges of your network before checking its consistency with BioQuali

Expression Dataset

A file of observations provided as a Node Attribute file (.NA extension). The name of

the attribute (first line of this file) must end by "var". This file summarizes the

qualitative variations (+, -) reported in literature or obtained elsewhere of certain nodes

in the network. For example:

HeatShockvar

nodeA = +

nodeX = -

nodeZ = -

In this example nodeA is being up-regulated under the Heat Shock experiment.

Expression Data Matrix (.pvals extension). For example:

GENE COMMON hs30 hs50 hs30 hs50

b0591 entS -0.034 0.111 1.56240e-02 7.91340e-06

b3973 birA -0.090 0.007 9.64330e-01 3.44760e-01

USAGE

Test graph consistency

1. Import the network in SIF format (File -> Import -> Network from multiple file types)

or use an already created network where the Interaction Edge Attribute is filled with an

appropriate label for all the edges in your network.

2. Click the "Run" button in the BioQuali window next to the "Test graph consistency"

option.

3. Click the "Add signs to the graph" button to assign +,-,?,& values to your network. This

button will appear anytime you run the consistency of a graph without assigned +,-,?,&

values to all of its edges.

4. Classify your Interaction labels into +, -, ? or &, and then press OK. After this step, an

Edge Attribute named "name_of_the_network" ending with the word "signs" will be

created. The "&" symbol may be seen as a boolean function AND; it states that if both,

A and B, hold a "&" influence with a product C, then for all possible +,- values of A and

B, C will only be + if both, A and B, are +. In any other case C will be -.

5. Click the "Run" button in the BioQuali window next to the "Test graph consistency"

option.

6. Two types of results are possible: (1) Consistent graph, or (2) Inconsistent graph. See

details in the Results Section.

Test graph consistency with literature data

1. Import the network in SIF format (File -> Import -> Network from multiple file types)

or use an already created network where the Interaction Edge Attribute is filled with an

appropriate label for all the edges in your network.

2. Import the Expression Dataset as a Node Attributes File (File -> Import -> Attribute

from Table (text/MS Excel)), and select the Node Attribute file.

3. In the BioQuali window, choose the "Litterature data" option, and then click the "Run"

button next to the "Test graph consistency with" option. If the network does not have +,

-, ? or & values assigned to its edges, then you will have to follow steps 3 and 4 from

the "Test graph consistency" previous section, and then press again the "Run" button.

4. Choose the experiment (its name should finish by var) that you want to analyze and

then press OK

5. After this step you will get one of the following results: (1) Consistent graph with data

and thus predictions, (2) Inconsistent graph with data, or (3) Multiple inconsistencies in

the graph. See details in the Results Section.

Test graph consistency with Gene Expression ratios

1. Import the network in SIF format (File -> Import -> Network from multiple file types)

or use an already created network where the Interaction Edge Attribute is filled with an

appropriate label for all the edges in your network.

2. Import the Gene Expression Data (matrix) file (.pvals) : File -> Import ->

Attribute/Expression Matrix.

3. In the BioQuali window, choose the "Gene Expression Ratios" option, and then click

the "Run" button next to the "Test graph consistency with" option. If the network does

not have +, -, or ? values assigned to its edges, then you will have to follow steps 3 and

4 from the "Test graph consistency" section, and then press again the "Run" button.

4. Choose the threshold to filter your experimental data, and chose your experimental

condition. Then press OK. BioQuali will select the expression values of higher absolute

value than your inserted "Threshold" and will transform the positive ones into + and the

negative ones into -. This +/- set of observations will be converted into a Node

Attribute named "Your_experiment" + "expvar", and will be used to check the

consistency of the graph.

5. After this step you will get one of the following results: (1) Consistent graph with data

(predictions) and thus predictions, (2) Inconsistent graph with data, or (3) Multiple

inconsistencies in the graph. See details in the Results Section.

Visualize core graph

1. Import the network in SIF format (File -> Import -> Network from multiple file types)

or use an already created network where the Interaction Edge Attribute is filled with an

appropriate label for all the edges in your network.

2. In the BioQuali window, press the "Run" button next to the "Visualize core Graph"

option. If the network does not have +, -, ? or & values assigned to its edges, then you

will have to follow steps 3 and 4 from the "Test graph consistency" section, and then

press again the "Run" button.

3. If there is no expression data imported, then BioQuali will calculate the core of the

network without data. If there is expression data imported, you have to choose with

respect to which expression data you want to obtain the core of the network.

4. After this step you will obtain the Network core. See details in the Results Section.

Apply BioQuali Visual Style

By executing this option your network will be colored. Positive interactions (+) will be in

green, negative interactions (-) in red, and other types of interactions (?) in black. Depending

on their expression value in the chosen Expression dataset, the borders of the nodes in the graph

will be also colored green (+), or red (-).

RESULTS

Consistent graph

If your graph is consistent, you will receive this message in the BioQuali results panel.

Your graph will be colored with the BioQuali Visual Style, which is green edges for positive (+)

influences, red edges for negative (-) influences, black edges for dual/unknown (?) influences,

and blue edges for complex formation (&) influences.

Inconsistent graph

In this case the inconsistent part of the network will be colored orange in the original graph and

a new graph will be built from this inconsistent region called inconsistent subgraph. The

Results Panel in the right will list the inconsistent regulations. You can select them in the

Results Panel to highlight the edges and nodes in the inconsistent subgraph or in the original

graph. An inconsistent subgraph of a network without expression data associated represents a

region in the network where no +, - variation of its nodes can be explained by any +,- variation

of its predecessors, given the actual influences of the network. There are 2 possible ways of

correcting it: (1) Using the Automatic Correction proposed by the Plugin, or (2) By a Manual

Correction. See details in the Correction Section.

Consistent graph with data (predictions)

When a graph is consistent with the experimental data provided, BioQuali will generate a set of

predictions. These predictions correspond to the nodes fixed as + or - that explain the

expression dataset provided. The predictions are colored in cyan in the graph, and a list of

them is presented in the Results Panel. It is possible to select in the graph the predictions by

clicking on their names in the Result Panel. In the example that follows the cyan nodes are the

predictions, the yellow are the selected nodes, and the nodes with red/green borders are the +/-

observed nodes.

Inconsistent graph with data

The region in the network inconsistent with the expression data provided will be colored orange

in the original graph and a new graph will be built from this inconsistent region called

inconsistent subgraph. The Results Panel in the right will list the inconsistent regulations. You

can highlight the inconsistent edges/nodes in the network by selecting them in the Result Panel.

A subgraph inconsistent with data means that no +, - variation of its nodes can explain the data

in the expression dataset (nodes with red/green border). There are 2 possible ways of correcting

it: (1) Using the Automatic Correction proposed by the Plugin, or (2) A Manual Correction.

See details in the Correction Section.

Multiple Inconsistencies in the Graph

When a graph is inconsistent with the experimental data provided, BioQuali may find a set of

local inconsistencies in the graph. Each local inconsistency corresponds to a node of the graph

and it is listed in the Results Panel. When we select one of them, the local inconsistent node

with its predecessors are highlighted in the graph. All the local inconsistencies in the graph

appear at once. It is possible to check the box in the Neutralize column of the Results Panel to

neutralize the local inconsistency. The details of this step are described in the Automatic

Correction of Multiple Inconsistencies Section

Network core

The core of the network is the graph formed by the nodes which have successors or that are

observed in some experiment dataset provided. It appears as a graph with green nodes and

edges.

CORRECTION

Automatic Correction of inconsistencies

Inconsistent subgraph

After obtaining an inconsistent subgraph, a "neutralize inconsistent edges" button will appear in

the BioQuali window. If you click this button you will assign a ? sign to all the edges in the

inconsistent subgraph. After this step you can retest the consistency of the graph again.

Multiple inconsistencies

To correct the multiple local inconsistencies in the graph we need to:

1. Check the box in the Neutralize column in the Results Panel of the inconsistencies you

want to correct (see Example below).

2. Click the "Apply" button.

3. Rerun the consistency check in the original graph.

The inconsistent edges of the inconsistent nodes and their predecessors will be assigned ?

(neutralized)

Manual Correction of inconsistencies

An inconsistency can be corrected in 3 ways:

1. Correcting the Expression Dataset:

Gene Expression dataset: By correcting the numerical value of the observation in the

Node Attribute ending by "exp".

Literature dataset: By changing the +,- value of the observed nodes in the Node

Attribute named "your_experiment"+"var"

2. Correcting the regulatory sign of an edge, by changing the +,- value of the edge in the

inconsistent graph

3. Adding new regulations into the network

If the inconsistent region is a graph, it may be corrected by neutralizing only one edge of the

graph. By neutralizing we mean: assigning a ? value to the signs Edge Attribute related of the

chosen edge. To be sure that this was the only error, you will need to recalculate the

consistency of the graph and data.

SAVING YOUR RESULTS

You can save your results by saving the Cytoscape session or by exporting the Edge or Node

Attributes (File -> Export -> Node/Edge Attributes). We detail in the following table the list of

node/edge attributes generated.

Attributes’ names When it was created?

Edge Attributes my_networksign Created by clicking on the

"Add signs to the graph"

button, after trying to test the

consistency of a network

named "my_network"

Node Attributes predictions Created when a graph is

consistent with expression

data

experiment1var Created after importing Node

Attributes with +, - values

experiment2exp Created after loading a Gene

Expression .pvals file

experiment2expvar Created after filtering by a

chosen threshold the Gene

Expression values