User:Floratos/MRA

< User:Floratos
Revision as of 00:25, 17 October 2009 by Floratos (talk | contribs) (Overview)

Aris's Home | Enhancements | Release v1.1 | Bison Issues | MRA | Release Testing | Home Page


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.
  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.

<Add Image here>

Parameters and Settings

P-Value Estimation

P-value corrections

False Discovery Control

Analysis Actions

Working with and viewing the analysis results

Significant markers set

The ANOVA result node in the Project Folders component

Color Mosaic Viewer

Tabular Viewer

Spreadsheet columns

Controls

Further customizing the spreadsheet

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