StrengthTrainer GLSY22522 Manual de usuario

VAN Package

User Guide Version 1.0

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Table of Contents

Topic Page no

1. Installation instructions 2

2. Example dataset 4

3. Example analysis

Protein-protein interactome

MicroRNA-target interactome

4. Network visualization using R or Cytoscape – an example

. Meta-analysis of multiple datasets – an example

6. Generating microRNA/protein based interactome – an

example

7. Understanding input data and parameters

8. Conversion of gene symbols to Entrez Ids

9. Understanding output data

10. Combining output data with known cancer annotation

11. Measures of association

12. References 20

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Section 1: nstallation instructions

Packages to download

For Windows



VAN_1.0.0.zip



VANData_1.0.0.zip

For Unix



VAN_1.0.0.tar.gz



VANData_1.0.0.tar.gz

For Mac



VAN_1.0.0.tgz



VANData_1.0.0.tgz

Example dataset to download

Example_DataSet.zip

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nstallation Steps

Download and install R version 2.1 .1 or higher from the website http://www.r-

project.org

At the R command prompt type

chooseCRANmirror()

## Select one of the options from the pop-up menu, e.g. Australia (Canberra)

setRepositories()

## Select the following three options from the pop-up menu –

## CRAN, BioC software, BioC annotation

install.packages("annotate")

install.packages("doParallel")

install.packages("marray")

install.packages("xlsx")

install.packages("igraph")

install.packages("org.Hs.eg.db")

install.packages("qvalue")

For Windows users, assume that the VAN and VANData packages –

VAN_1.0.0.zip and VANData_1.0.0.zip, are saved in the directory C:/My_Packages.

Now, at the R command prompt type

setwd("C:/My_Packages")

## Note that the separator has to be “/” and not “\”

## for example, setwd("C:\My_Packages") will result in an error message

install.packages("VANData_1.0.0.zip", repos=NULL)

install.packages("VAN_1.0.0.zip", repos=NULL)

For Mac users, set the working directory using the appropriate file path syntax.

Next, install the following packages

install.packages("VANData_1.0.0.

tgz", repos=NULL)

install.packages("VAN_1.0.0.

tgz", repos=NULL)

For Unix users, set the working directory using the appropriate file path syntax.

Next, install the following packages

install.packages("VANData_1.0.0.

tar.gz", repos=NULL)

install.packages("VAN_1.0.0.

tar.gz", repos=NULL)

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Section 2: Example dataset

The Example_DataSet.zip contains –

1. Gene_Expr_Two_Conditions.txt: An example gene expression dataset with samples

corresponding to two conditions – StateA and StateB

2. Gene_Expr_Four_Conditions.txt: An example gene expression dataset with samples

corresponding to four conditions – StateA, StateB, StateC, and StateD.

3. Micro_Expr_Two_Conditions.txt: An example microRNA expression dataset with

samples corresponding to two conditions – StateA and StateB.

4. PPI_Map.txt: An example protein-protein interactome file.

. Mirnome_Map.txt: An example microRNA based interactome file.

6. MiTa Lite_Example.txt: An example MiTabLite file downloaded from the website

http://wodakla .org/iRefWe /search/index [1]

7. Gene_Output_1.txt, Gene_Output_2.txt, Gene_Output_3.txt: Three example output

files; each file contains the names of hubs and, for a given hub, the number of

interactors and the p-value for the test of significance. The test corresponds to the

null hypothesis that there is no change in association between a hub and its

interactors across biological states.

8. Gene_Output_1_Cor.txt: An example output file containing the correlations for all

hub-interactor pairs in two states, namely StateA and StateB.

9. Gene_Output_2_Cor.txt: An example output file containing the correlations for all

hub-interactor pairs in four states, namely StateA, StateB, StateC, and StateD.

10. Micro_Output_1_Cor.txt: An example output file containing the correlations for all

microRNA-target gene pairs in two states, namely StateA and StateB.

11. Cancer_Gene_Census.xls: An example file downloaded from the website

http://www.sanger.ac.uk/genetics/CGP/Census. This file contains the catalogue of

genes

that have been causally linked to cancer(s)

12. ExampleVisualStyle.props: An example Vizmap Property File for use with Cytoscape [2]

to visualize the changes in hub-interactor associations across biological states. This

property file is independent of the dataset being analyzed, which implies that the

same property file can be used for visualizing any dataset.

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Section 3: Example analysis

For all the analyses, we assume that example dataset (Section 2) is saved in the folder

C:/My_Packages. We also assume that the VAN package has been loaded and the current

working directory has been set appropriately as shown below –

Load the VAN package and set the working directory

At the R command prompt type

setwd("C:/My_Packages")

## The above command is for Windows users

## Unix and Mac users should set the working directory using the appropriate file path syntax

library(VAN)

The li rary command loads VAN and some additional packages (refer Section 1:

Installation Steps) into memory. If these packages were developed using a version of R

that is different from the version installed on the user’s computer, warning messages

may be generated. However, these warning messages will not affect the execution of the

program.

1. Combining gene expression data with a protein-protein interaction (PP ) dataset –

Two conditions

identifySignificantHubs(exprFile = "Gene_Expr_Two_Conditions.txt"

, labelIndex = 1

, mapFile = "PPI_Map.txt"

, outFile = "Test_Output_PPI.txt"

, randomizeCount = 10)

For an explanation of input/output data, refer sections 7 and 9. If only a subset of the

hubs present in the input PPI_Map.txt file are of interest, then use the input

parameter hu Vect as shown below

identifySignificantHubs(exprFile = "Gene_Expr_Two_Conditions.txt"

, labelIndex = 1

, mapFile = "PPI_Map.txt"

, outFile = "SubTest_Output_PPI.txt"

, randomizeCount = 10

, hubVect = c("ABL1", "GRB2"))

Now, the output file SubTest_Output_PPI.txt only contains the results for the two user-

defined hubs, namely, ABL1 and GRB2.

2. Combining gene expression and microRNA expression data with a microRNA-

target interactome – Two conditions

identifySignificantHubs(exprFile = c("Gene_Expr_Two_Conditions.txt",

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"Micro_Expr_Two_Conditions.txt")

, labelIndex = 1

, mapFile = "Mirnome_Map.txt"

, outFile = "Test_Output_Mirnome.txt"

, randomizeCount = 10)

The order of the two expression data files is important. As shown in the above

example, the first file should correspond to gene expression data and the second file

to microRNA expression data.

3. Combining gene expression with a protein-protein interaction dataset –

Multiple conditions

identifySignificantHubs(exprFile = "Gene_Expr_Four_Conditions.txt"

, labelIndex = 1

, mapFile = "PPI_Map.txt"

, outFile = "Test_Output_PPI_Four_Cond.txt"

, randomizeCount = 10

, assocType = "FSTAT")

4. Combining gene expression and microRNA expression data with a microRNA-

target interactome – Multiple conditions

If multiple conditions are to be evaluated for a combination of gene and microRNA

expression data, then the exprFile input parameter should contain two file names, as

shown in Example 2.

Note: In the above examples we set randomizeCount=10 for quick execution. We note

that this parameter should be set to 1000 during an actual analysis. For an explanation of

the output files, refer Section 9.

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Section 4: Network Visualization using R and Cytoscape – An example

We provide two options for visualizing the changes in associations between a

protein/microRNA hub and its interactors. Typically, the input file for data visualization

will correspond to the output correlation file generated by the function

identifySignificantHu s (Section 3). For example, if the user was interested in visualizing

the hub-interactor correlations corresponding to the analysis performed in Example 1 of

Section 3, then the input file will be Test_Output_PPI_Cor.txt. However, to illustrate our

data visualization function, we consider input files that are already available in the

Example dataset (Section 2).

Option 1: Visualization in R

For an analysis involving two conditions, we can visualise a single hub of interest

together with its interactors, e.g. the gene “ABL1” along with its interactors, by typing at

the R command prompt

visualizeNetwork(inputFile="Gene_Output_1_Cor.txt"

, inputHub="ABL1"

, paletteVector=c("red", "yellow", "green"))

For an explanation of the data file to be provided as input, refer Section 9. Similarly, for

an example visualization of a microRNA and its interactome, at the R command prompt

type

visualizeNetwork(inputFile="Micro_Output_1_Cor.txt"

, inputHub="hsa-miR-551a"

, paletteVector=c("red", "yellow", "green"))

For an analysis involving multiple conditions, we can visualise a single hub of interest

together with its interactors for exactly two conditions, e.g. the gene “ABL1” along with

its interactors in States B and C, by typing at the R command prompt

visualizeNetwork(inputFile="Gene_Output_2_Cor.txt"

, inputHub="ABL1"

, paletteVector=c("red", "yellow", "green")

, condVector = c("StateB", "StateC"))

Option 2: Visualization in Cytoscape

For visualization in Cytoscape, we can use (i) the output file containing the hub-

interactor pairs or (ii) a subset of this file. The advantage of using the subset is that it

allows us to focus on only the significant hubs. To obtain the subset, at the R command

prompt type

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obtainPairSubset(filePrefix="Gene_Output_1"

, useAdjustedProb=FALSE

, probThresh=0.05)

The above command generates an output file “Gene_Output_1_Cor_Signif.txt”. This file

contains the hub-interactor pairs for only those hubs which have an unadjusted p-value

less than 0.0

We provide an example layout file and a ‘color-blind safe’ edge palette (created with the

aid of http:www.//color rewer2.org/ and http://jfly.iam.u-tokyo.ac.jp/color/) for visualization

using Cytoscape. For an analysis involving two conditions (e.g., StateA and StateB), the

steps to be followed are:

1. Download and install Cytoscape from the website

http://www.cytoscape.org/download.html

2. From the Cytoscape Desktop menu bar select “File”, “Import”, “Network from Table

(Text/MS Excel)”

3. At the “Import Network and Edge Attributes from Table” window:

a. Select the input file: this will be the VAN output file

Gene_Output_1_Cor_Signif.txt

b. At the “Advanced” options, check “Show Text File Import Options”

c. At the “Text File Import Options, uncheck the “Delimiter” “Space”

d. At “Attribute Names” check “Transfer first line as attribute names”

e. At the “Interaction Definition” options, select “Column 1” as the Source

Interaction and Column 2 as the Target Interaction

f. At the “Preview” click on Columns 3 to to activate import of these data

4. From the menu bar select “Layout”, “Cytoscape Layouts”, “Circular Layout”

. From the menu bar select “File”, “Import”, “Vizmap Property File)” and select the

input file: “ExampleVisualStyle.props”

6. At the Cytoscape Desktop Control Panel, click on the VizMapper

tab and select

“example visual style” as the Current Visual Style

7. At the Visual Mapping Browser, select “StateA” in the drop down box at the “Edge

Color” field. Next, select “StateB” to visualize how the network co-expression

organisation varies as a function of the second condition (StateB) compared with the

first (StateA).

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