Difference between revisions of "Master Regulator Analysis"

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=Overview=
 
=Overview=
 
Regulatory activity in the context of specific cellular phenotypes can be investigated 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 [[#Basso2005 | [Basso et al., 2005]]] 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).  
 
Regulatory activity in the context of specific cellular phenotypes can be investigated 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 [[#Basso2005 | [Basso et al., 2005]]] 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).  
  
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Master Regulator analysis [[#Lefebvre2010 | [Lefebvre et al., 2010]]] is an algorithm used to identify transcription factors whose targets (e.g., as represented in an ARACNe-generated interactome) are enriched for a particular gene signature.  The enrichment is evaluated using a statistical test such as Fisher’s exact test.  The objective is to place the signature genes within a regulatory context and identify the master regulators responsible for coordinating their activity, thus highlighting the regulatory apparatus driving phenotypic differentiation.  
+
Master Regulator analysis [[#Lefebvre2010 | [Lefebvre et al., 2010]]] is an algorithm used to identify transcription factors whose targets (e.g., as represented in an ARACNe-generated interactome) are enriched for a particular gene signature (e.g. a list of differentially expressed genes).  The enrichment is evaluated using a statistical test such as Fisher’s exact test or GSEA.  The objective is to place the signature genes within a regulatory context and identify the master regulators responsible for coordinating their activity, thus highlighting the regulatory apparatus driving phenotypic differentiation.  
  
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Specifically, given an interaction network ''I'', a (presumed) master regulator gene ''A'', and a set of signature genes, MRA computes the intersection between two sets of genes:
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Specifically, given an interaction network ''I'', a (presumed) master regulator gene ''A'', and a set of signature genes, MRA computes the enrichment of the signature genes in the regulon of ''A'', where the regulon of ''A'' is defined as its neighbors in the interaction network ''I''.
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# The neighbors of ''A'' in the interaction network ''I'' (this gene set is called the '''''regulon''''' of ''A'').
 
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# The set of signature genes.  The signature may be supplied independently or calculated from e.g. a differential expression experiment.
 
  
 
Interaction networks are represented as "adjacency matrices".  An adjacency matrix lists the connections that each node takes part in, and includes a measure of the strength of that interaction (e.g. the mutual information in the case of matrices generated by ARACNe).
 
Interaction networks are represented as "adjacency matrices".  An adjacency matrix lists the connections that each node takes part in, and includes a measure of the strength of that interaction (e.g. the mutual information in the case of matrices generated by ARACNe).
  
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Fisher’s exact test is used to quantify how likely it is to encounter an intersection of (at least) the observed size by chance alone. A small p-value is taken to imply that gene ''A'' may play a significant role in mediating the regulatory program that leads to the differential phenotypes.
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Their are two master regulator analysis components implementing different methods to evaluate the enrichment of the signature in the regulon.  Either method will quantify how likely it is to encounter an enrichment of (at least) the observed size by chance alone. A small p-value is taken to imply that gene ''A'' may play a significant role in mediating the regulatory program that leads to the differential phenotypes.
  
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Generating a display of the effect of a master regulator on its regulon requires performing a t-test of differential expression on a dataset representing the two phenotypes distinguished by the signature, ideally the original dataset from which the signature was determined.
+
* '''[[MRA-FET|FET Method (local service)]]''' - this method use Fisher's Exact Test.  This method is implemented locally in geWorkbench.
 +
* '''[[MARINa|MARINa Method *(grid service)]]''' - this method uses GSEA and differs in substantial ways from the FET-based method.  This method is only implemented as a grid service and currently has restricted availability due to its computational cost.  A t-test between two phenotype classes is built in to the implementation to produce the gene signature.
  
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=Setting up an MRA run=
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The MARINa method can use sample shuffling to correct for non-independance between the expression of various genes.  Sample shuffling is not implemented for the MRA-FET method and hence in that method, the p-values are not directly comparable between genes.
  
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==Prerequisites==
+
Please note that MARINa does not employ any of the special gene lists available for use with the GSEA algorithm, such as [http://www.broadinstitute.org/gsea/msigdb/index.jsp. MSigDB].  It uses only a calculated list of differentially expressed genes and the regulon of the TF being tested.
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* The Master Regulator Analysis (MRA) component must be loaded in the [[Tutorial_-_Component_Configuration_Manager | Component Configuration Manager]].
 
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** The MRA component will be listed along with the other analysis routines within the geWorkbench Analysis Panel.
 
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* '''Interaction Network''' - An interaction network calculated with ARACNe from a dataset which includes the particular cellular phenotypes being investigated. In calculating the network, all genes that will be tested as possible master regulators should be used as hubs in the ARACNe calculation.
 
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* '''Signature genes''' - A list of signature genes which distinguish between two phenotypes.  This list may come from a t-test, clustering, or some combination of methods.  The user must define this set using methods relevant to the particular dataset and study goals.
 
−
* '''Candidate master regulator list''' -  A set of genes that will be tested as candidate master regulators.  This set may be comprised of e.g. transcription factor and signalling pathway genes.
 
−
* '''Gene Expression dataset''' - A gene expression dataset in which the phenotypic signature was identified or can be demonstrated.  A t-test of differential expression will be run to generate the graphic "bar code" display of the effect of the master regulator on its regulon.  
 
  
−
==Parameters and Settings==
+
With the release of geWorkbench 2.5.0, MRA-FET and MRA-MARINa are located in two separate sets of components, which can be loaded in the [[Component_Configuration_Manager| CCM]].
  
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[[Image:MRA_Parameters_panel.png]]
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=Setting up an MRA run=
  
−
===Load Network===
+
==Prerequisites==
−
There are 2 ways to designate the interaction network, represented by an adjacency matrix, that will be used for computing the regulons of the candidate master regulator genes:
+
* Either or both Master Regulator Analysis (MRA) components, MRA-FET and MRA-MARINa, must be loaded in the [[Component_Configuration_Manager | Component Configuration Manager]].
−
* '''From File''': by choosing a file that describes a network.
 
−
* '''From Project''': by selecting an adjacency matrix node from the Project Folders component. Several analytical components in geWorkbench (e.g., [[Tutorial_-_ARACNE | ARACNE]]) produce adjacency matrix results nodes that can be utilized for this purpose.
 
  
−
All edges in the network are assumed to be significant, and any strength value included is not used.
+
[[Image:MRA-MARINa-CCM.png]]
  
−
===Master Regulators===
 
−
A set of candidate master regulator markers.  This set must be loaded into the Markers component before running MRA.  This can be read in either directly as markers, or as gene symbols.
 
  
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===Signature Markers===
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===Gene Expression dataset===
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A set of markers comprising the signature that distinguishes the chosen phenotype from others.  This set must be loaded into the Markers component before running MRA. It can be read in either directly as markers, or as gene symbols (but be aware that a gene can be represented by more than one marker, and not all may correspond to the signature).
+
A gene expression dataset in which the phenotypic signature was identified or can be demonstrated.  A t-test of differential expression will be run to generate the graphic "bar code" display of the effect of the master regulator on its regulon or to generate the signature gene list (MARINa method).
  
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===Fisher's Exact Test threshold===
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===Interaction Network===
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Enter a p-value for the significance at which to accept the overlap of the regulon of a candidate TF and the signature set of genes.
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An interaction network in the form of an adjacency matrix (See [[File_Formats|File Formats]].  Networks can be loaded from a file, or calculated with ARACNe from a dataset which includes the particular cellular phenotypes being investigated.  If calculating the network with ARACNe, all genes to be tested as possible master regulators should be used as hubs.
  
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===T-test for differential expression===
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If the incorrect network format is chosen, the user is warned and the analysis setup is terminated.
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A "bar-code" graphic is generated using a t-test on a differential expression dataset.  However, all t-values are accepted (critical alpha = 1) and used to order the bars representing the regulon markers.
 
  
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All  that is required is to set up sets of arrays representing the two phenotypes of interest (those distinguished by the signature). At least two sets of arrays must be activated, and at least one marked as "case", representing the target phenotype of the gene signature.  "Control" is the default classification.  See also the [[Differential_Expression | Differential Expression tutorial]]).
+
If the network is loaded into MARINa as gene symbols or Entrez IDs, it will be transformed (expanded) to include all probesets annotated to each such gene if an annotation file has been loaded for the expression dataset.
  
 +
===Signature genes (FET method)===
 +
A list of signature gene markers which distinguish between two phenotypes.  This list may come from a t-test, clustering, or some combination of methods.  The user must define this set using methods relevant to the particular dataset and study goals.
  
−
[[Image:Array_set_class_assignment_MRA.png]]
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===Candidate master regulator list (FET method)===
 +
A set of gene markers that will be tested as candidate master regulators.  This set may be comprised of e.g. transcription factor and signalling pathway genes.
  
−
=Viewing MRA analysis results=
+
===Note on Marker Sets===
−
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 t-test:
+
geWorkbench provides a mechanism to restrict some analyses to using certain sets of markers by "activating" these sets in the Markers component.  However, as the MRA analysis component uses named marker sets directly, it does not respect the activation state of marker sets in the Markers component, and such activated sets will have no effect on the analysis. 
  
−
[[Image:MRA_results_node.png]]
+
However, activating microarray sets would restrict the markers used in generating the "bar graph" by the MRA viewer.
  
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The results of the analysis can be visualized in the MRA Viewer component by selecting the result node.
+
For this reason, no marker sets should be "activated" (their check-box checked) during MRA analysis.
  
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==MRA Results Viewer==
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==Parameters and Settings==
−
The MRA viewer is structured in 3 distinct areas.
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===Main===
 +
The settings on this tab apply to both the FET and MARINa methods.
  
  
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[[Image:MRA_viewer_GBM_FOSL2.png]]
+
====Load Network====
 +
There are 2 ways to designate the interaction network, represented by an adjacency matrix, that will be used for computing the regulons of the candidate master regulator genes:
 +
* '''From File''': by choosing a file that describes a network. 
 +
* '''From Workspace''': by selecting an adjacency matrix node from the [[Workspace|Workspace]] component.
  
 +
=====Load Network from File=====
 +
* The file loading controls will become active when this option is chosen.
 +
* Press the "Load" button to bring up the file browser.
 +
* After selecting a file, a second dialog will ask for details about the format and symbols used.
  
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===Summary Listing===
+
[[Image:MRA_Load_Network_Dialog.png]]
  
 +
* '''File Format''': 
 +
** ADJ
 +
** SIF
 +
** MARINa 5-column format (internal use only)
  
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[[Image:MRA_Summary_listing.png]]
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* '''Nodes Represented by''':  
 +
** probeset id
 +
** gene symbol
 +
** entrez id
 +
** other
  
 +
If the network is loaded into MARINa as gene symbols or Entrez ID, it will be transformed (expanded) to include all probesets annotated to each such gene if an annotation file has been loaded for the expression dataset.
  
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At upper left in the MRA viewer.  For each candidate master regulator found to have a significant effect using Fisher's Exact test, the following four columns are displayed:
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After the file has been loaded, its name will be displayed in the adjacent text field.
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* '''Master Regulator''' - This is either the master regulator gene name or the marker/probeset name identifying the corresponding array feature (depending on the selection of the radio buttons “Symbol” and “Probe set”).
 
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* '''FET p-value'''  -  the p-value from Fisher’s exact test. The test utilizes a 2x2 contingency table where rows classify markers as belonging to the signature set or not, while columns indicate if a marker belongs to the regulon of the master regulator or not. Counts are computed using all markers found in the input experiment data. (Fischer's exact test includes p-values for more-extreme tables). 
 
−
* '''Genes in Regulon''' - the number of markers (genes) found to be first neighbors of the master regulator in the loaded network - its regulon.
 
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* '''Genes in Intersection Set''' - The number of markers found in the intersection of the signature and the regulon of the candidate MR.
 
  
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The contents of the table can be ordered by any column, by clicking on the column name.
+
=====Load Network from Workspace=====
 +
Several analytical components in geWorkbench (e.g., [[ARACNe | ARACNe]], [[Cellular_Networks_KnowledgeBase | CNKB]]) produce adjacency matrix results nodes that can be utilized for this purpose.  Networks can also be loaded into the [[Workspace|Workspace]] directly from a file.
  
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Clicking on the radio button for any of the master regulators will display the list of intersection genes in a table to the right (Detailed Listing), and will draw the regulon bar graph below.
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* The pulldown menu for choosing an available adjacency matrix will become active.  Only adjacency matrices that are children of the current microarray dataset will be offered.
  
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===Detailed Listing===
+
All edges in the network are assumed to be significant, and any strength value included is not used.
  
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The detailed list shows the genes/markers contained in the intersection set of the  MR regulon and the signature.
+
====Enrichment Threshold====
−
 
+
Enter a p-value for the significance at which to accept the overlap of the regulon of a candidate TF and the signature set of genes.
−
 
+
For the FET (local service), this is calculated using the FET.  For the MARINa (grid service) method, this is calculated using GSEA.
−
[[Image:MRA_Detailed_listing.png]]
 
−
 
 
−
The genes are displayed in a table with the following columns:
 
−
* '''Genes in intersection set''': the names of the genes in the intersection set. Either the gene name or the marker/probe set name is used (based on the choice of "Symbol" or "Probe Set" radio buttons).
 
−
* '''T-test value''': The actual value of the t-test statistic for the gene. A positive value indicates that the expression of the gene is higher in cases than in controls. A negative value has the opposite meaning.
 
−
 
 
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===Graph View===
 
−
For a given master regulator ''A'' and the intersection between its regulon and the set of differentially expressed genes, the graph view helps assess if the intersection genes are preferentially over-expressed in the cases versus the controls. The biological motivation comes from observing [[#Lim2009 | [Lim et al., 2009]]] that regulators with multiple targets tend to affect the expression level of (most of) their targets in one particular direction: they either promote their expression or inhibit it; but they rarely do both equally.
 
−
 
 
−
 
 
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* The red-blue gradient at the bottom of the graph represents the range between the lowest (blue)  and the highest (red) t-test statistic recorded among all differentially expressed genes. The white area in the middle represents zero.
 
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* The vertical bars correspond to the genes displayed in the table under the “Detailed listing” portion of the interface, i.e., the intersection between the differentially expressed genes and the regulon of the master regulator ''A'' currently selected within the “Summary listing” table.  
 
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* The relative location of a bar on the gradient represents the t-test statistic recorded for the corresponding gene.
 
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* Further, the color of each bar provides information about the correlation between the expression levels of the target gene and the putative master regulator A (correlations are computed as Pearson’s correlation, using data from all microarrays in the experiment): red means that the two genes are positively correlated (r > 0) while blue means that correlation is negative (r < 0).
 
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FOSL2:
 
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[[Image:MRA_graph_GBM_FOSL2.png]]
 
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The bar graph shown above, for FOSL2, indicates that more of its regulon genes have positive differential expression in the mesenchymal phenotype, and that expression of FOSL2 is positively correlated with the differential expression of its regulon in the mesenchymal phenotype.  
 
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ZNF238:
 
  
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[[Image:MRA_graph_GBM_ZNF238.png]]
+
===MRA-FET (Local service)===
 +
Please see the separate [[MRA-FET|MRA-FET]] chapter for details on running the FET version of master regulator analysis.
  
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The bar graph shown above, for ZNF238, indicates that  expression of ZNF238 is negatively correlated with the differential expression of its regulon in the mesenchymal phenotype.
+
===MARINA (grid service)===
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Please see the separate [[MARINa|MARINa]] chapter for details on running MARINa.
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===Control buttons===
 
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Additional functionality is made available through the following buttons:
 
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* '''Add to Set''': creates a set containing the markers that correspond to the genes currently displayed in the table. The new marker set appears in the Markers component and is named after the master regulator.
 
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* '''Export selected''': same as “Add to Set” but instead of being added to a marker set, the markers are stored into a file.
 
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* '''Export all''': stores into a file information for all master regulators (instead of only the one currently selected in the “Summary” view). Specifically, for each master regulator, the file lists the markers for all genes that are both differential expressed and also belong to the master regulator’s regulon.
 
  
 
=Dataset History=
 
=Dataset History=
−
Each results node stores the parameter settings used to setup the corresponding MRA run. The specific parameter values can be inspected within the Dataset History component, after clicking on the MRA results node in the Project Folders pane.
+
Each results node stores the parameter settings used to setup the corresponding MRA run. The specific parameter values can be inspected within the Dataset History component, after clicking on the MRA results node in the [[Workspace]].
−
 
 
−
=Example of running MRA=
 
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This example uses a dataset comprised of 176 microarrays described in Phillips (2006).  The analysis follows that described in Carro et al. (2010) for master regulators of Glioblastoma.
 
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==Prerequisites==
 
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# Obtain the annotation file for the HG-U95Av2 array type from the Affymetrix NetAffx website (http://www.affymetrix.com/support/technical/byproduct.affx?product=hgu95). The name will be similar to "HG_U95Av2.na29.annot.csv", where na29 is the version number. Loading the annotation file associates gene names and other information with the Affymetrix probeset IDs (see the geWorkbench FAQ for details on obtaining these files).
 
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# Store in your disk the following 2 files:
 
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## [[Media:Interaction_network.txt | Interaction_network.txt]]: describes an interaction network with 1955 nodes and 3810 edges. The file has 1955 lines (one for each node, the node name is the first entry in every line) and each line lists the edges emanating from that node. Edges are describes as tab-delimited pairs where the first member of the pair is the name of the target node and the second member is a number specifying a weight for the edge (MRA does not use the weight information but other geWorkbench components do). Node names correspond to marker ids.
 
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## [[Media:Master_regulators.csv | Master_regulators.csv]]: a list of master regulators. The marker ids in this file correspond to genes whose [http://www.geneontology.org/ Gene Ontology] annotation (under the Molecular Function category) lists them as transcription factors.
 
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==Loading and preparing the example data==
 
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===Microarray dataset===
 
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# Load a microarray dataset.  (See [[Tutorial_-_Local_Data_Files | Local Data Files]]).
 
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# When prompted, load the annotation file.
 
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===Marker sets===
 
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# Load marker sets for the list of candidate master regulators and for the signature genes.
 
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[[Image:MRA_GBM_Marker_sets.png]]
 
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===Array sets===
 
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Array sets are shown defined for the three phenotypic classes of arrays in the dataset: Mesenchymal (MES), Proneural (PN), and Proliferative (Prolif).
 
−
 
 
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* MES and PN are "activated" for use in the t-test by checking the boxes next their names.
 
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* The MES set is classifed as "Case".  Right click on the thumbtack adjacent to the set name.
 
−
 
 
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[[Image:Array_set_class_assignment_MRA.png]]
 
−
 
 
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==Setting up the parameters and starting MRA==
 
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In the Analysis Panel, select the "MRA Analysis" entry and set the parameters as follows:
 
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* '''Load Network''' - from the drop down choose the option "From Set", click on the "Load" button, and select the desired set.
 
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* '''Master regulators''' - select the desired set from those loaded in the Markers component.
 
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* '''Signature markers''' - select the desired set from those loaded in the Markers component.
 
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[[Image:MRA_GBM_param_setup.png]]
 
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* Click on the '''Analyze''' button.
 
−
 
 
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==Results==
 
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Upon completion of the analysis, an MRA results node is placed in the Project Folders tree. The analysis results can be browsed using the MRA viewer and are as shown above in the MRA Results Viewer section.
 
  
 
=References=
 
=References=
 
<span id="Basso2005"></span>
 
<span id="Basso2005"></span>
 
* Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A (2005). Reverse engineering of regulatory networks in human B cells. Nat Genet 37(4):382-390 ([http://www.nature.com/ng/journal/v37/n4/abs/ng1532.html link to paper]).
 
* Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A (2005). Reverse engineering of regulatory networks in human B cells. Nat Genet 37(4):382-390 ([http://www.nature.com/ng/journal/v37/n4/abs/ng1532.html link to paper]).
 +
* Carro MS, Lim WK, Alvarez MJ, Bollo RJ, Zhao X, Snyder EY, Sulman EP, Anne SL, Doetsch F, Colman H, Lasorella A, Aldape K, Califano A, Iavarone A  (2010)  The transcriptional network for mesenchymal transformation of brain tumors.  Nature 463(7279):318-25. PMID: [http://www.ncbi.nlm.nih.gov/pubmed/20032975 20032975].
 
<span id="Lefebvre2010"></span>
 
<span id="Lefebvre2010"></span>
−
* Carro MS, Lim WK, Alvarez MJ, Bollo RJ, Zhao X, Snyder EY, Sulman EP, Anne SL, Doetsch F, Colman H, Lasorella A, Aldape K, Califano A, Iavarone A  (2010)  The transcriptional network for mesenchymal transformation of brain tumors.  Nature 463(7279):318-25.
+
* Lefebvre C, Rajbhandari P, Alvarez MJ, Bandaru P, Lim WK, Sato M, Wang K, Sumazin P, Kustagi M, Bisikirska BC, Basso K, Beltrao P, Krogan N, Gautier J, Dalla-Favera R, Califano A (2010)  A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers.  Mol Syst Biol.  6:377. PMID: [http://www.ncbi.nlm.nih.gov/pubmed/20531406 20531406].
−
* Lefebvre C, Rajbhandari P, Alvarez MJ, Bandaru P, Lim WK, Sato M, Wang K, Sumazin P, Kustagi M, Bisikirska BC, Basso K, Beltrao P, Krogan N, Gautier J, Dalla-Favera R, Califano A (2010)  A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers.  Mol Syst Biol.  6:377. PMID: 20531406 ([http://www.ncbi.nlm.nih.gov/pubmed/20531406 link to paper]).
 
 
<span id="Lim2009"></span>
 
<span id="Lim2009"></span>
 
* Lim WK, Lyashenko E, Califano A: Master regulators used as breast cancer metastasis classifier. Pac Symp Biocomput. 2009:504-15 ([http://psb.stanford.edu/psb-online/proceedings/psb09/lim.pdf link to paper]).
 
* Lim WK, Lyashenko E, Califano A: Master regulators used as breast cancer metastasis classifier. Pac Symp Biocomput. 2009:504-15 ([http://psb.stanford.edu/psb-online/proceedings/psb09/lim.pdf link to paper]).
 
* Phillips HS, Kharbanda S, Chen R, Forrest WF, Soriano RH, Wu TD, Misra A, Nigro JM, Colman H, Soroceanu L, Williams PM, Modrusan Z, Feuerstein BG, Aldape K (2006)  Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis.  Cancer Cell 9(3):157-73.
 
* Phillips HS, Kharbanda S, Chen R, Forrest WF, Soriano RH, Wu TD, Misra A, Nigro JM, Colman H, Soroceanu L, Williams PM, Modrusan Z, Feuerstein BG, Aldape K (2006)  Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis.  Cancer Cell 9(3):157-73.

Latest revision as of 17:47, 31 July 2014

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Overview

Regulatory activity in the context of specific cellular phenotypes can be investigated 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 [Basso et al., 2005] 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).

Master Regulator analysis [Lefebvre et al., 2010] is an algorithm used to identify transcription factors whose targets (e.g., as represented in an ARACNe-generated interactome) are enriched for a particular gene signature (e.g. a list of differentially expressed genes). The enrichment is evaluated using a statistical test such as Fisher’s exact test or GSEA. The objective is to place the signature genes within a regulatory context and identify the master regulators responsible for coordinating their activity, thus highlighting the regulatory apparatus driving phenotypic differentiation.

Specifically, given an interaction network I, a (presumed) master regulator gene A, and a set of signature genes, MRA computes the enrichment of the signature genes in the regulon of A, where the regulon of A is defined as its neighbors in the interaction network I.

Interaction networks are represented as "adjacency matrices". An adjacency matrix lists the connections that each node takes part in, and includes a measure of the strength of that interaction (e.g. the mutual information in the case of matrices generated by ARACNe).

Their are two master regulator analysis components implementing different methods to evaluate the enrichment of the signature in the regulon. Either method will quantify how likely it is to encounter an enrichment of (at least) the observed size by chance alone. A small p-value is taken to imply that gene A may play a significant role in mediating the regulatory program that leads to the differential phenotypes.

  • FET Method (local service) - this method use Fisher's Exact Test. This method is implemented locally in geWorkbench.
  • MARINa Method *(grid service) - this method uses GSEA and differs in substantial ways from the FET-based method. This method is only implemented as a grid service and currently has restricted availability due to its computational cost. A t-test between two phenotype classes is built in to the implementation to produce the gene signature.

The MARINa method can use sample shuffling to correct for non-independance between the expression of various genes. Sample shuffling is not implemented for the MRA-FET method and hence in that method, the p-values are not directly comparable between genes.

Please note that MARINa does not employ any of the special gene lists available for use with the GSEA algorithm, such as MSigDB. It uses only a calculated list of differentially expressed genes and the regulon of the TF being tested.

With the release of geWorkbench 2.5.0, MRA-FET and MRA-MARINa are located in two separate sets of components, which can be loaded in the CCM.

Setting up an MRA run

Prerequisites

MRA-MARINa-CCM.png


Gene Expression dataset

A gene expression dataset in which the phenotypic signature was identified or can be demonstrated. A t-test of differential expression will be run to generate the graphic "bar code" display of the effect of the master regulator on its regulon or to generate the signature gene list (MARINa method).

Interaction Network

An interaction network in the form of an adjacency matrix (See File Formats. Networks can be loaded from a file, or calculated with ARACNe from a dataset which includes the particular cellular phenotypes being investigated. If calculating the network with ARACNe, all genes to be tested as possible master regulators should be used as hubs.

If the incorrect network format is chosen, the user is warned and the analysis setup is terminated.

If the network is loaded into MARINa as gene symbols or Entrez IDs, it will be transformed (expanded) to include all probesets annotated to each such gene if an annotation file has been loaded for the expression dataset.

Signature genes (FET method)

A list of signature gene markers which distinguish between two phenotypes. This list may come from a t-test, clustering, or some combination of methods. The user must define this set using methods relevant to the particular dataset and study goals.

Candidate master regulator list (FET method)

A set of gene markers that will be tested as candidate master regulators. This set may be comprised of e.g. transcription factor and signalling pathway genes.

Note on Marker Sets

geWorkbench provides a mechanism to restrict some analyses to using certain sets of markers by "activating" these sets in the Markers component. However, as the MRA analysis component uses named marker sets directly, it does not respect the activation state of marker sets in the Markers component, and such activated sets will have no effect on the analysis.

However, activating microarray sets would restrict the markers used in generating the "bar graph" by the MRA viewer.

For this reason, no marker sets should be "activated" (their check-box checked) during MRA analysis.

Parameters and Settings

Main

The settings on this tab apply to both the FET and MARINa methods.


Load Network

There are 2 ways to designate the interaction network, represented by an adjacency matrix, that will be used for computing the regulons of the candidate master regulator genes:

  • From File: by choosing a file that describes a network.
  • From Workspace: by selecting an adjacency matrix node from the Workspace component.
Load Network from File
  • The file loading controls will become active when this option is chosen.
  • Press the "Load" button to bring up the file browser.
  • After selecting a file, a second dialog will ask for details about the format and symbols used.

MRA Load Network Dialog.png

  • File Format:
    • ADJ
    • SIF
    • MARINa 5-column format (internal use only)
  • Nodes Represented by:
    • probeset id
    • gene symbol
    • entrez id
    • other

If the network is loaded into MARINa as gene symbols or Entrez ID, it will be transformed (expanded) to include all probesets annotated to each such gene if an annotation file has been loaded for the expression dataset.

After the file has been loaded, its name will be displayed in the adjacent text field.

Load Network from Workspace

Several analytical components in geWorkbench (e.g., ARACNe, CNKB) produce adjacency matrix results nodes that can be utilized for this purpose. Networks can also be loaded into the Workspace directly from a file.

  • The pulldown menu for choosing an available adjacency matrix will become active. Only adjacency matrices that are children of the current microarray dataset will be offered.

All edges in the network are assumed to be significant, and any strength value included is not used.

Enrichment Threshold

Enter a p-value for the significance at which to accept the overlap of the regulon of a candidate TF and the signature set of genes. For the FET (local service), this is calculated using the FET. For the MARINa (grid service) method, this is calculated using GSEA.

MRA-FET (Local service)

Please see the separate MRA-FET chapter for details on running the FET version of master regulator analysis.

MARINA (grid service)

Please see the separate MARINa chapter for details on running MARINa.

Dataset History

Each results node stores the parameter settings used to setup the corresponding MRA run. The specific parameter values can be inspected within the Dataset History component, after clicking on the MRA results node in the Workspace.

References

  • Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A (2005). Reverse engineering of regulatory networks in human B cells. Nat Genet 37(4):382-390 (link to paper).
  • Carro MS, Lim WK, Alvarez MJ, Bollo RJ, Zhao X, Snyder EY, Sulman EP, Anne SL, Doetsch F, Colman H, Lasorella A, Aldape K, Califano A, Iavarone A (2010) The transcriptional network for mesenchymal transformation of brain tumors. Nature 463(7279):318-25. PMID: 20032975.

  • Lefebvre C, Rajbhandari P, Alvarez MJ, Bandaru P, Lim WK, Sato M, Wang K, Sumazin P, Kustagi M, Bisikirska BC, Basso K, Beltrao P, Krogan N, Gautier J, Dalla-Favera R, Califano A (2010) A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol Syst Biol. 6:377. PMID: 20531406.

  • Lim WK, Lyashenko E, Califano A: Master regulators used as breast cancer metastasis classifier. Pac Symp Biocomput. 2009:504-15 (link to paper).
  • Phillips HS, Kharbanda S, Chen R, Forrest WF, Soriano RH, Wu TD, Misra A, Nigro JM, Colman H, Soroceanu L, Williams PM, Modrusan Z, Feuerstein BG, Aldape K (2006) Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9(3):157-73.
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