geWorkbench

Overview

MARINa uses gene set enrichment analysis (GSEA) to calculate if the regulon of a TF is enriched for genes that are differentially expressed among the 2 classes of microarrays. Following the GSEA runs, MARINa performs an additional "shadow" analyses. The "Synergy" analysis option is not currently supported.

Inputs

• Network - the network (e.g. from ARACNe) upon which MARINa will operate.
• GSEA and shadow/synergy analysis run parameters:
  • Minimum Number of Targets: minimum number of genes in a regulon for a GSEA run (a positive integer).
  • Minimum Number of Samples: minimum number of arrays in the input microarray set in order to run sample-based permutations in GSEA (a positive integer).
  • Number of GSEA permuations: number of GSEA permutations to compute enrichment score distribution (a positive integer).
  • GSEA Tail: whether 1-tailed (1) or 2-tailed GSEA (2) is to be performed. If all correlations in the network file are greater than zero, then one-tail GSEA will be performed regardless of the value the user assigns to "tail".
  • Shadow P-value: significance threshold for shadow analysis.
  • SynergyP-value: significance threshold for synergy analysis.
• Retrieve prior result with ID (checkbox) - if checked, all parameter fields are disabled and the prior ID field is enabled. The user can retrieve the results of a previous run.

other input

• GSEA P-Value: This parameter appears on the "Main" parameters tab. It indicates the enrichment score p-value below which a regulon is considered enriched in differentially expressed genes.

Unpaired/Paired Runs

Normally, the user will specify two classes of arrays, e.g. "case" and "control". Under these conditions, an unpaired calculation is performed. However, if the user only includes one class of array (control), then "paired" runs are performed.

Network alternative 5-column file format

MARINa in geWorkbench will normally be set up to work with a network that is either already in the Project area, or that is loaded from disk in a standard format, e.g. an ARACNe-style adjacency matrix or SIF file. geWorkbench will generate a file in the following 5-column format which is then sent to the remote MARINa service. However, a file in this format can also be directly loaded into the MARINa component. In this case, the values contained in the file are used directly, and are not replaced by values calculated by geWorkbench.

Each line represents a network edge and comprises five tab-delimited columns:

• Transcription factor id: A string that provides the transcription factor end of the edge. This is usually a probeset id or a gene symbol.
• Target id: A string that provides the target end of the edge. This is usually a probeset id or a gene symbol
• Mutual information: The mutual information (MI) of the edge (a real number). If the edge MI is not known/available, the value 1 can be entered here.
• Spearman's correlation: The Spearman's correlation for the edge, computed on the original microarray set that gave rise to the ARACNe network (a real number). If not known/available, the value 1 can be entered here.
• P-value for Spearman's correlation. The p-value associated with Spearman's correlation found in the previous column (a real number between 0 and 1). It this p-value is not known/available, a value of 0 can be entered here.

Example data files for running MARINa

MARINa runs, even on a large computational cluster, can be very time-consuming. We include here a small test dataset with which to demonstrate running MARINa.

Example data files (.zip). Includes expression file, network file (5-column format) and files defining two microarray classes. The individual files are:

• mra_grid_data.exp - Affymetrix File Matrix format expression file.
• mra_grid_5colnetwork.txt - A network file matching the expression data. This file is in the "5-column" format and can only be loaded directly into the MRA component. Network files in the adjacency matrix and SIF formats should normally be used, this file is for demonstration only.
• mra_grid_ixclass1.csv - array set definition file, for loading into the Arrays component to define phenotype class 1 (Case).
• mra_grid_ixclass2.csv - array set definition file, for loading into the Arrays component to define phenotype class 2 (Control).

Running MARINa

Prerequisites

• Expression Data - An expression data set must be loaded in geWorkbench.
• Phenotypic classes - specified in the Arrays component in the currently active list view.
  • Two classes - Normally, "case" and "control" array sets should be defined and activated in the Arrays component. The calculation will be run in "unpaired" mode.
  • Single class - However, if only one class is present (e.g. if no arrays are marked "case" or activated, then only a single class will be used. In this case, the calculation is run in "paired" mode.
• Network file - a network that is already present in the Projects area as a child of the expression dataset can be used, or a network can be loaded directly into the MRA component via its Main tab.

Results

With sample shuffling:

With probe shuffling (note that the MeanClass1 and MeanClass2 columns are omitted when probe shuffling is used):

The MARINa results file contains one line for each TF whose GSEA enrichment score renders it significant above the user-specified p-value threshold (pvalue_gsea), as long as such TF is not "shadowed" by other significant TFs. NOTE: for the time being, we will ignore below (and in our implementation of the grid service) the results of the synergy analysis.

The columns represented in the results are as follows:

• TFsym: transcription factor id (usually this is the probeset id).
• GeneName: transcription factor gene name.
• NumPosGSet: number of genes (among those in the regulon of the transcription factor) which are positively regulated by the TF. Only genes represented in the microarray set are counted.
• NumNegGSet: number of genes (among those in the regulon of the transcription factor) which are negatively regulated by the TF. Only genes represented in the microarray set are counted.
• NumLedgePos: number of genes (among those counted under the NumPosGSet column) which are in the GSEA leading edge.
• NumLedgeNeg: number of genes (among those counted under the NumNegGSet column) which are in the GSEA leading edge.
• NumLedge: sum of NumLedgePos and NumLedgeNeg.
• ES: GSEA enrichment score for the regulon of the TF.
• NES: GSEA normalized enrichment score for the regulon of the TF.
• absNES: absolute value of NES
• PV: p-value of normalized (?) enrichment score.
• OddRatio: (NumLedge/(microarray set genes in the regulon of the TF))/((number of differentially expressed genes left of the leading edge)/(total number of microarray set genes)) - <Aris>please check with Mukesh that this is the correct definition</Aris>.
• TScore - t-test t-statistic.
• MeanClass1: mean expression value of TF among all "Class 1" arrays.
• MeanClass2: mean expression value of TF among all "Class 2" arrays.
• Note - there are no MeanClass1 and MeanClass2 when probe shuffling is performed.
• Original MRA/Recovered_MRA: a value of 1 in this column means that the TF was found to be enriched by GSEA (above the significance level pvalue_gsea) and that it was not shadowed by any other TF. A value of 0 means that the TF was found to be enriched by GSEA and that it was shadowed by another TF and that it remained enriched even after the common targets with the other TF were removed from its regulon.

Extensions for the geWorkbench MRA component

To invoke the MARINa service from within the geWorkbench MRA component (and display the analysis results) we should make the following changes:

MRA Analysis component

• MARINa. In here we specify the params that are specific to the MARINa analysis.These are:
  • The Class 1 (i.e. case) and Class 2 (i.e. control) arrays. These should be selected (activated) in the Arrays component available array sets.
    • Note the two selected array sets have no arrays in common. An error popup should notify the user if either of these conditions are violated.
  • The values for the applicable parameters listed under "GSEA and shadow/synergy analysis run parameters" above.
    • "paired", - if the user only includes one class (control), then it is paired. If both classes are provided, it is unpaired.