Revision as of 09:39, 20 October 2009

Overview

Regulatory activity in the context of specific cellular phenotypes can be modeled using interaction networks. These are graphs where nodes represent genes and an edge between two nodes A and B means that genes A and B are participants in the same regulatory activity. E.g., A can be a transcription factor for B; or, A can be an miRNA that silences B. Analysis of such regulatory networks [refs] has convincingly demonstrated their scale free nature which is dominated by a relatively small number of nodes with a large degree of connectivity. The genes corresponding to those nodes are known as "master regulators" and collectively orchestrate the regulatory program of the underlying cellular phenotype(s).

The master regulator analysis (MRA) component in geWorkbench combines regulatory information from interaction networks with differential expression analysis. The objective is to place differentially expressed genes within a regulatory context and identify the master regulators responsible for coordinating their regulation, thus highlighting the regulatory apparatus driving phenotypic differentiation. Specifically, given an interaction network I, a master regulator gene A, and two sets of microarrays representing two distinct phenotypes, MRA computes the intersection between two sets of genes:

1. The neighbors of A in the interaction network I (this gene set is called the regulon of A).
2. The set of differentially expressed genes in the array data from the two phenotypes of interest.

Fisher’s exact test is then used to quantify how likely it is to encounter an intersection of the observed size by chance alone. A small p-value is taken to imply that gene A may play a significant role in controlling the regulatory program that leads to the differential phenotypes.

Setting up an MRA run

Prerequisites

• First confirm that the MRA component is available in geWorkbench. If not, it can be loaded using the Component Configuration Manager.
• The MRA will be listed along with the other analysis routines within the geWorkbench Analysis pane.

• Data from an expression experiment (comprising measurements from multiple arrays/samples) has been loaded and the corresponding node has been selected withing the Project Folders:

• Two array sets (each containing a distinct subset of arrays from the expression experiment) have been selected in the "Arrays/Phenotypes" component, representing the two phenotypes under investigation. One of the array sets (identified by the red-color pin) has been classified as containing the "Case" arrays while the other one contains the "Controls":

NOTE:The "Case"/"Control" assignment is needed because, in the general case, it is possible to select more than one array sets for this analysis. In that case it is necessary to explicitly designate which belong to each of the 2 phenotypes.

Parameters and Settings

Load Network

There are 2 ways to designate the interaction network that will be used for computing the neighbors of the candidate master regulator genes:

• From File: by choosing a file that describes a network (see example and format description below).
• From Project: by selecting a node from the project folders which represents an interaction network. Several analytical components in geWorkbench (e.g., ARACNE) produce results nodes that can be utilized for this purpose.

Transcription Factors

There are 2 ways to designate the candidate master regulator genes to use (in most cases such genes will be transcription factors, hence the explicit use of the term in this field):

• From File: by choosing a file that contains a list of (comma separated) marker names.
• From Sets: by selecting one among the marker sets within the “Markers” component.

T-test p-value (alpha)

Differential expression between the 2 phenotypes of interest is assessed using a t-test. The p-value provided by the user indicates the significance threshold below which a gene’s average expression is presumed to be significantly different in the 2 sets of arrays (cases and controls). Additional parameter settings affecting the execution of the t-test are defined within the “T-test” subtab. The parameters specified there are exactly the same as those used for the differential expression t-test analysis (see description in the corresponding tutorial.

Working with and viewing the analysis results

Following the successful completion of the MRA computation, a result node appears in the Project Folder area of the geWorkbench interface, under the microarray experiment node used for the computation:

[[Image:]]

The results of the analysis can be visualized in the MRA Viewer component by selecting the result node.

Results Viewer

The MRA viewer is structured in 3 distinct areas. ===Transcription Factor

Dataset History

Example of running MRA

Prerequisites

Loading and preparing the example data

Choosing array groups

Setting up the parameters and starting MRA

Results

References